Preliminary study on the application of bioimpedance analysis to measure the psoas major muscle in older adults.

For the assessment of sarcopenia or other geriatric frailty syndromes, psoas major area may be one of the primary indicators. Aim to develop and cross-validate the psoas cross-sectional area estimation equation of L3-L4 of the elderly over 60 years old by bioelectrical impedance analysis (BIA). Ninety-two older adults with normal mobility were enrolled (47 females, 45 males), and were randomly divided into a modeling group (MG, n = 62) and validation group (VG, n = 30). Computed tomography (CT) was used to measure the psoas major area at the' L3-L4 lumbar vertebrae height as a predictor. Estimated variables were height (h), whole body impedance (Zwhole), whole body impedance index (h2/Zwhole, WBI), age, gender (female = 0, male = 1), and body weight (weight) by standing BIA. Relevant variables were estimated using stepwise regression analysis. Model performance was confirmed by cross-validation. BIA estimation equation for PMM obtained from the MG was: (PMMBIA = 0.183 h2/Z- 0.223 age + 4.443 gender + 5.727, r2 = 0.702, n = 62, SEE = 2.432 cm2, p < 0.001). The correlation coefficient r obtained by incorporating the VG data into the PMM equation was 0.846, and the LOA ranged from -4.55 to 4.75 cm2. PMMBIA and PMMCT both correlate highly with MG or VG with small LOA. The fast and convenient standing BIA for measuring PMM may be a promising method that is worth developing.

Bioimpedance analysis combined with sagittal abdominal diameter for abdominal subcutaneous fat measurement.

Abdominal subcutaneous fat tissue (ASFT) is an independent predictor of mortality. This prospective observational study aimed to establish a rapid, safe, and convenient estimation equation for abdominal subcutaneous fat area (SFA) using bioimpedance analysis (BIA) combined with sagittal abdominal diameter (SAD). A total of 520 adult subjects were recruited and were randomly divided into 2/3 (n = 346) and 1/3 (n = 174) to form a modeling group (MG) and a validation group (VG), respectively. Each subject's abdomen was scanned using computed tomography to obtain target variables (SFACT). Predictor variables for all subjects included bioimpedance index (h2/Z), anthropometric parameters height (h), weight (W), waist circumference (WC), hip circumference (HC), and SAD, along with age and sex (male =1, female = 0). SFA estimation equation SFABIA+SAD was established for the MG using stepwise multiple regression analysis. Cross-validation was performed using VG to evaluate the performance of the SFABIA+SAD estimation equation. Stepwise multiple regression analysis was applied from the MG, including SFABIA+SAD = 49.89 + 1.09 SAD-29.90 Sex + 4.71 W-3.63 h2/Z-1.50 h (r = 0.92, SEE = 28.10 cm2, n = 346, p < 0.001). Mean differences in SFABIA+SAD relative to SFACT were -1.21 ± 21.53, 2.85 ± 27.16, and -0.98 ± 36.6 cm2 at different levels of obesity (eutrophic, overweight, obese), respectively. This study did not have a large number of samples in different fields, so it did not have completely external validity. Application of BIA combined with SAD in anthropometric parameters achieves fast, accurate and convenient SAF measurement. Results of this study provide a simple, reliable, and practical measurement that can be widely used in epidemiological studies and in measuring individual SFA.

Assessment of total and regional bone mineral density using bioelectrical impedance vector analysis in elderly population.

This study aimed to investigate the relationship between bone mineral density (BMD) and height-adjusted resistance (R/H), reactance (Xc/H) and phase angle (PhA). A total of 61 male and 64 female subjects aged over 60 years were recruited from middle Taiwan. The R and Xc were measured using Bodystat Quadscan 4000 at a frequency of 50 kHz. BMD at the whole body, L2-L4 spine, and dual femur neck (DFN), denoted as BMDTotal, BMDL2-L4, and BMDDFN, were calculated using a Hologic DXA scanner. The R-Xc graph was used to assess vector shift among different levels of BMD. BMD was positively correlated with Xc/H and negatively correlated with R/H (p < 0.001). The General Linear Model (GLM) regression results were as follows: BMDTotal = 1.473-0.002 R/H + 0.007 Xc/H, r = 0.684; BMDL2-L4 = 1.526-0.002 R/H + 0.012 Xc/H, r = 0.655; BMDDFN = 1.304-0.002 R/H + Xc/H, r = 0.680; p < 0.0001. Distribution of vector in the R-Xc graph was significantly different for different levels of BMDTotal, BMDL2-L4 and BMDDFN. R/H and Xc/H were correlated with BMD in the elderly. The linear combination of R/H and Xc/H can effectively predict the BMD of the whole body, spine and proximal femur, indicating that BIVA may be used in clinical and home-use monitoring tool for screening BMD in the elderly in the future.

Prediction of Nonalcoholic Fatty Liver Disease by Anthropometric Indices and Bioelectrical Impedance Analysis in Children.

Background: Nonalcoholic fatty liver disease (NAFLD) is highly prevalent in children and is associated with obesity. Objectives: To test whether addition of bioelectrical impedance analysis (BIA) parameters to BMI and anthropometric indices improves the prediction performance of NAFLD than BMI z score (BAZ) alone. Methods: This cross-sectional study recruited 933 children 6-12 years of age for anthropometric measure, BIA, and liver ultrasound. Prediction models of the BAZ, anthropometric, and BIA sets were built in children with obesity using machine learning algorithms. Results: Prevalences of NAFLD were 44.4% (59/133) and 20% (12/60) in boys and girls with obesity, respectively. In both sexes, BAZ set performed worst; adding anthropometric indices into the model improved the model performance, whereas BIA parameters were the best approach for predicting NAFLD. The best result in boys achieved had an accuracy of 75.9% and area under receiver operating characteristic curve of 0.854. In girls, the best result achieved had an F-measure score of 0.615, Matthews correlation coefficient of 0.512, and area under precision-recalled curve of 0.697. Conclusion: BIA is a simple and highly precise tool that yields better NAFLD prediction model than anthropometric indices, and much better performance than BAZ. This study suggests BIA as a potential predictor for pediatric NAFLD.

Standing 8-Electrode Bioelectrical Impedance Analysis as an Alternative Method to Estimate Visceral Fat Area and Body Fat Mass in Athletes.

PURPOSE: To investigate the potential of standing 8-electrode bioelectrical impedance analysis (BIA) for assessing visceral fat area (VFA) and body fat mass (BFM) in athletes. MATERIALS AND METHODS: A total of 95 subjects (50 males and 45 females) were recruited. VFA and BFM measurements were obtained using three standing 8-electrode BIA devices, InBody230, InBody770, and IOI353. These acquired VFA and BFM were expressed as VFAIOI353, VFAInBody230, VFAInBody770 V, BFMIOI353, BFMInBody230, and BFMInBody770, respectively. As reference measurement, the VFA acquired from computer tomography (CT) was expressed as VFACT, and the BFM measured by dual-energy X-ray absorptiometry (DXA) was denoted as BFMDXA. RESULTS: The coefficient of determination (r2) in regression analysis between the measurements by VFAIOI353, VFAInBody230, VFAInBody770 and VFACT were 0.425, 0.492, and 0.473, respectively. Also, the limits of agreement (LOA) obtained from Bland-Altman analysis were -25.18 to 56.62, -29.74 to 62.44, and -32.96 to 71.93 cm2. For BFM, r2 in regression analysis between the measurements by BFMIOI353, BFMInBody230, BFMInBody770 and BMFDXA were 0.894, 0.950, and 0.955, respectively; LOA were -7.21 to 5.75, -4.70 to 4.05, and -5.48 to 3.05 kg, respectively. CONCLUSION: The results showed when assessing BFM, these instruments delivered comparable measurements, and the degree of agreement ranged from excellent to moderate compared with the reference method. However, when assessing VFA, the agreements were weak. Therefore, the application of standing 8-electrode BIA devices for assessing athletes' VFA still needs improvement.

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults.

BACKGROUND: Bioelectrical impedance vector analysis (BIVA) has been used for prediction of muscle performance. However, little is known about BIVA in Asian adults, and even less is known about using standing BIVA devices. Standing impedance analyzer allows quicker and more convenient way to gather data than conventional supine analyzer and is more suitable for clinical practice. This study aimed to investigate the relations between muscle function and BIVA parameters measured with a standing impedance analyzer in healthy Taiwanese adults. METHODS: A total of 406 healthy subjects (age 34.5 ± 17.3 years, body mass index 24.1 ± 4.1 kg/m2) were recruited for BIVA and handgrip strength (HGS) measurements. Impedance parameters, including resistance (R) and reactance (Xc), were measured and normalized to body size by dividing by height (H). The resulting phase angle (PhA) was calculated. HGS in the dominant, left, and right hands were referred to as HGSDH, HGSLH, and HGSRH, respectively. All subjects were divided into 5 grades according to HGS. RESULTS: Muscle strength in the dominant, right, and left arms was correlated with variables in the order of sex, weight, age, height, Xc/H, and R/H (all, p < 0.001). Using all 6 variables, the determination coefficients were 0.792, 0.782, and 0.745, respectively, whereas the standard errors of estimates were 56.89, 58.01, and 56.67 N for HGSDH, HGSLH, and HGSRH, respectively. HGS was positively correlated with PhA, and negatively correlated with Xc/H and R/H. CONCLUSIONS: BIVA parameters measured with a standing impedance analyzer and anthropometric variables can predict and discriminate muscle function with good performance in healthy Asian adults.

Comparison of Bioelectrical Impedance Analysis and Dual Energy X-ray Absorptiometry for Total and Segmental Bone Mineral Content with a Three-Compartment Model.

Modern bioelectrical impedance analysis (BIA) provides a wide range of body composition estimates such as fat mass (FM), lean body mass (LBM), and body water, using specific algorithms. Assuming that the fat free mass (FFM) and LBM can be accurately estimated by the 8-electrode BIA analyzer (BIA8MF; InBody230, Biospace), the bone mineral content (BMC) may be calculated by subtracting the LBM from the FFM estimates based on the three-compartment (3C) model. In this cross-sectional study, 239 healthy Taiwanese adults (106 male and 133 female) aged 20-45 years were recruited for BIA and dual-energy X-ray absorptiometry (DXA) measurements of the whole body and body segments, with DXA as the reference. The results showed a high correlation between BIA8MF and DXA in estimating total and segmental LBM, FM and percentage body fat (r = 0.909-0.986, 0.757-0.964, and 0.837-0.936, respectively). For BMC estimates, moderate to high correlations (r = 0.425-0.829) between the two methods were noted. The percentage errors and pure errors for BMC estimates between the methods ranged from 33.9% to 93.0% and from 0.159 kg to 0.969 kg, respectively. This study validated that BIA8MF can accurately assesses LBM, FM and body fat percentage (BF%). However, the estimation of segmental BMC based on the difference between FFM and LBM in body segments may not be reliable by BIA8MF.

Performance of Bioelectrical Impedance Analysis in the Estimation of Bone Mineral Content in Healthy Children Aged 6-12 Years.

BACKGROUND: Bioelectrical impedance analysis (BIA) is a widely available tool which provides mineral estimate. However, BIA is not currently recognized as a bone mineral measuring method. This study aimed to explore the ability of BIA to predict bone mineral content (BMC) in children, using dual-energy X-ray absorptiometry as a gold standard. METHODS: Healthy children aged 6-12 years (n = 176) were recruited for BIA and dual-energy X-ray absorptiometry measurements. Predictive models were generated using basic indices (age, height, weight, waist circumference, hip circumference, etc.) and BIA parameters (minerals, fat mass, and fat free mass). RESULTS: The root-mean-square deviation and R2 for the total BMC predictive model were 0.089 kg and 0.926, respectively using height and weight as predictors whereas 0.113 kg and 0.886, respectively using minerals by BIA. The root-mean-square deviation and R2 for the subtotal BMC predictive model were 0.080 kg and 0.935, respectively using height and weight as predictors whereas 0.098 kg and 0.906, respectively using minerals by BIA. The best predictive models included basic indices and BIA parameters as predictors, but they had only slightly better performance over simple models. CONCLUSIONS: Mineral content by BIA was good predictor of total and subtotal BMC in healthy children but with similar overall model performance compared to basic indices. More complex models combined all the predictive variables gave better prediction power, but of little improvement to these simple models. The BIA instrument does not appear to be useful in estimating BMC in healthy children as basic indices are more widely available measures but provide comparable performance. Future studies are needed to determine the clinical usefulness of the more complex prediction model in children with disease or children in other subgroups.

Discrepancies between leg-to-leg bioelectrical Impedance analysis and computerized tomography in abdominal visceral fat measurement.

The aim of this study was to evaluate leg-to-leg bioelectrical impedance analysis (LBIA) using a four-contact electrode system for measuring abdominal visceral fat area (VFA). The present study recruited 381 (240 male and 141 female) Chinese participants to compare VFA measurements estimated by a standing LBIA system (VFALBIA) with computerized tomography (CT) scanned at the L4-L5 vertebrae (VFACT). The total mean body mass index (BMI) was 24.7 ± 4.2 kg/m2. Correlation analysis, regression analysis, Bland-Altman plot, and paired sample t-tests were used to analyze the accuracy of the VFALBIA. For the total subjects, the regression line was VFALBIA = 0.698 VFACT + 29.521, (correlation coefficient (r) = 0.789, standard estimate of error (SEE) = 24.470 cm2, p < 0.001), Lin's correlation coefficient (CCC) was 0.785; and the limit of agreement (LOA; mean difference ±2 standard deviation) ranged from -43.950 to 67.951 cm2, LOA% (given as a percentage of mean value measured by the CT) was 48.2%. VFALBIA and VFACT showed significant difference (p < 0.001). Collectively, the current study indicates that LBIA has limited potential to accurately estimate visceral fat in a clinical setting.

Validation of two portable bioelectrical impedance analyses for the assessment of body composition in school age children.

BACKGROUND: Bioelectrical impedance analysis (BIA) is a convenient and child-friendly method for longitudinal analysis of changes in body composition. However, most validation studies of BIA have been performed on adult Caucasians. The present cross-sectional study investigated the validity of two portable BIA devices, the Inbody 230 (BIA8MF) and the Tanita BC-418 (BIA8SF), in healthy Taiwanese children. METHODS: Children aged 7-12 years (72 boys and 78 girls) were recruited. Body composition was measured by the BIA8SF and the BIA8MF. Dual X-ray absorptiometry (DXA) was used as the reference method. RESULTS: There were strong linear correlations in body composition measurements between the BIA8SF and DXA and between the BIA8MF and DXA. Both BIAs underestimated fat mass (FM) and percentage body fat (%BF) relative to DXA in both genders The degree of agreement in lean body mass (LBM), FM, and %BF estimates was higher between BIA8MF and DXA than between BIA8SF and DXA. The Lin's concordance correlation coefficient (ρc) for LBM8MF met the criteria of substantial to perfect agreement whereas the ρc for FM8MF met the criteria of fair to substantial agreement. Bland-Altman analysis showed a clinically acceptable agreement between LBM measures by BIA8MF and DXA. The limit of agreement in %BF estimation by BIA and DXA were wide and the errors were clinically important. For the estimation of ALM, BIA8SF and BIA8MF both provided poor accuracy. CONCLUSIONS: For all children, LBM measures were precise and accurate using the BIA8MF whereas clinically significant errors occurred in FM and %BF estimates. Both BIAs underestimated FM and %BF in children. Thus, the body composition results obtained using the inbuilt equations of the BIA8SF and BIA8MF should be interpreted with caution, and high quality validation studies for specific subgroups of children are required prior to field research.

Hand-to-Hand Model for Bioelectrical Impedance Analysis to Estimate Fat Free Mass in a Healthy Population.

This study aimed to establish a hand-to-hand (HH) model for bioelectrical impedance analysis (BIA) fat free mass (FFM) estimation by comparing with a standing position hand-to-foot (HF) BIA model and dual energy X-ray absorptiometry (DXA); we also verified the reliability of the newly developed model. A total of 704 healthy Chinese individuals (403 men and 301 women) participated. FFM (FFMDXA) reference variables were measured using DXA and segmental BIA. Further, regression analysis, Bland-Altman plots, and cross-validation (2/3 participants as the modeling group, 1/3 as the validation group; three turns were repeated for validation grouping) were conducted to compare tests of agreement with FFMDXA reference variables. In male participants, the hand-to-hand BIA model estimation equation was calculated as follows: FFMmHH = 0.537 h²/ZHH - 0.126 year + 0.217 weight + 18.235 (r² = 0.919, standard estimate of error (SEE) = 2.164 kg, n = 269). The mean validated correlation coefficients and limits of agreement (LOAs) of the Bland-Altman analysis of the calculated values for FFMmHH and FFMDXA were 0.958 and -4.369-4.343 kg, respectively, for hand-to-foot BIA model measurements for men; the FFM (FFMmHF) and FFMDXA were 0.958 and -4.356-4.375 kg, respectively. The hand-to-hand BIA model estimating equation for female participants was FFMFHH = 0.615 h²/ZHH - 0.144 year + 0.132 weight + 16.507 (r² = 0.870, SEE = 1.884 kg, n = 201); the three mean validated correlation coefficient and LOA for the hand-to-foot BIA model measurements for female participants (FFMFHH and FFMDXA) were 0.929 and -3.880-3.886 kg, respectively. The FFMHF and FFMDXA were 0.942 and -3.511-3.489 kg, respectively. The results of both hand-to-hand and hand-to-foot BIA models demonstrated similar reliability, and the hand-to-hand BIA models are practical for assessing FFM.

Comparison of Standing Posture Bioelectrical Impedance Analysis with DXA for Body Composition in a Large, Healthy Chinese Population.

Bioelectrical impedance analysis (BIA) is a common method for assessing body composition in research and clinical trials. BIA is convenient but when compared with other reference methods, the results have been inconclusive. The level of obesity degree in subjects is considered to be an important factor affecting the accuracy of the measurements. A total of 711 participants were recruited in Taiwan and were sub-grouped by gender and levels of adiposity. Regression analysis and Bland-Altman analysis were used to evaluate the agreement of the measured body fat percentage (BF%) between BIA and DXA. The BF% measured by the DXA and BIA methods (Tanita BC-418) were expressed as BF%DXA and BF%BIA8, respectively. A one-way ANOVA was used to test the differences in BF% measurements by gender and levels of adiposity. The estimated BF%BIA8 and BF%DXA in the all subjects, male and female groups were all highly correlated (r = 0.934, 0.901, 0.916, all P< 0.001). The average estimated BF%BIA8 (22.54 ± 9.48%) was significantly lower than the average BF%DXA (26.26 ± 11.18%). The BF%BIA8 was overestimated in the male subgroup (BF%DXA< 15%), compared to BF%DXA by 0.45%, respectively. In the other subgroups, the BF%BIA8 values were all underestimated. Standing BIA estimating body fat percentage in Chinese participants have a high correlation, but underestimated on normal and high obesity degree in both male and female subjects.

Cross-mode bioelectrical impedance analysis in a standing position for estimating fat-free mass validated against dual-energy x-ray absorptiometry.

Bioelectrical impedance analysis (BIA) is commonly used to assess body composition. Cross-mode (left hand to right foot, Z(CR)) BIA presumably uses the longest current path in the human body, which may generate better results when estimating fat-free mass (FFM). We compared the cross-mode with the hand-to-foot mode (right hand to right foot, Z(HF)) using dual-energy x-ray absorptiometry (DXA) as the reference. We hypothesized that when comparing anthropometric parameters using stepwise regression analysis, the impedance value from the cross-mode analysis would have better prediction accuracy than that from the hand-to-foot mode analysis. We studied 264 men and 232 women (mean ages, 32.19 ± 14.95 and 34.51 ± 14.96 years, respectively; mean body mass indexes, 24.54 ± 3.74 and 23.44 ± 4.61 kg/m2, respectively). The DXA-measured FFMs in men and women were 58.85 ± 8.15 and 40.48 ± 5.64 kg, respectively. Multiple stepwise linear regression analyses were performed to construct sex-specific FFM equations. The correlations of FFM measured by DXA vs. FFM from hand-to-foot mode and estimated FFM by cross-mode were 0.85 and 0.86 in women, with standard errors of estimate of 2.96 and 2.92 kg, respectively. In men, they were 0.91 and 0.91, with standard errors of the estimates of 3.34 and 3.48 kg, respectively. Bland-Altman plots showed limits of agreement of -6.78 to 6.78 kg for FFM from hand-to-foot mode and -7.06 to 7.06 kg for estimated FFM by cross-mode for men, and -5.91 to 5.91 and -5.84 to 5.84 kg, respectively, for women. Paired t tests showed no significant differences between the 2 modes (P > .05). Hence, cross-mode BIA appears to represent a reasonable and practical application for assessing FFM in Chinese populations.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly.

BACKGROUND: This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. METHODS: A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. RESULTS: The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. CONCLUSION: When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM.

Preventive effects of Spirulina platensis on skeletal muscle damage under exercise-induced oxidative stress.

The effects of spirulina supplementation on preventing skeletal muscle damage on untrained human beings were examined. Sixteen students volunteered to take Spirulina platensis in addition to their normal diet for 3-weeks. Blood samples were taken after finishing the Bruce incremental treadmill exercise before and after treatment. The results showed that plasma concentrations of malondialdehyde (MDA) were significantly decreased after supplementation with spirulina (P < 0.05). The activity of blood superoxide dismutase (SOD) was significantly raised after supplementation with spirulina or soy protein (P < 0.05). Both of the blood glutathione peroxidaes (GPx) and lactate dehydrogenase (LDH) levels were significantly different between spirulina and soy protein supplementation by an ANCOVA analysis (P < 0.05). In addition, the lactate (LA) concentration was higher and the time to exhaustion (TE) was significantly extended in the spirulina trail (P < 0.05). These results suggest that ingestion of S. platensis showed preventive effect of the skeletal muscle damage and that probably led to postponement of the time of exhaustion during the all-out exercise.