Single-frequency Bioelectrical Impedance Analysis Research Articles

Prognostic significance of novel muscle quality index utilization in hospitalized adults with cancer: A secondary analysis.

This study aimed to investigate and propose novel approaches to calculate muscle quality index (MQI) using muscle mass derived from single-frequency bioelectrical impedance analysis (SF-BIA) and calf circumference in both unadjusted and body mass index (BMI)-adjusted forms. In addition, we examined their prognostic significance in patients with cancer. A secondary analysis was conducted on a prospective cohort study of patients with cancer. Handgrip strength was measured. SF-BIA was conducted to estimate appendicular lean soft tissue (ALST, in kilograms). MQI was calculated using three approaches: (1) the ratio of handgrip strength to ALST (MQISF-BIA), (2) the ratio of handgrip strength to calf circumference (MQIcalf circumference), and (3) the ratio of handgrip strength to BMI-adjusted calf circumference (MQIadj. calf circumference). Maximally selected log-rank was calculated to estimate their cutoff values to predict survival. Two hundred eighty-four patients were included (51.1% men; median age, 61 years). Solid tumors were the most frequent (89.8%). All approaches to MQI (MQISF-BIA, MQIcalf circumference, and MQIadj. calf circumference) were independent predictors of 6-month mortality. The found cutoffs were (1) MQISF-BIA (<1.52 for men, <0.63 for women), (2) MQIcalf circumference (<0.74 for men, <0.24 for women), and (3) MQIadj. calf circumference (<0.75 for men, <0.25 for women). This study introduces MQISF-BIA, MQIcalf circumference, and MQIadj. calf circumference as future potential surrogate methods for computing MQI in clinical practice when other robust procedures are unavailable, pending further validation.

Estimation equation of limb lean soft tissue mass in Asian athletes using bioelectrical impedance analysis.

The lean soft tissue mass (LSTM) of the limbs is approximately 63% of total skeletal muscle mass. For athletes, measurement of limb LSTM is the basis for rapid estimation of skeletal muscle mass. This study aimed to establish the estimation equation of LSTM in Asian athletes using bioelectrical impedance analysis (BIA). A total of 198 athletes (121 males, 77 females; mean age 22.04 ± 5.57 years) from different sports in Taiwan were enrolled. A modeling group (MG) of 2/3 (n = 132) of subjects and a validation group (VG) of 1/3 (n = 68) were randomly assigned. Using the InBody S-10, resistance and reactance were measured at 50 kHz from the right palm to the right sole while the participant was in the supine position. Predictor variables were height (h), weight (W), age, Sex, Xc, resistance index (RI; RI = h2 / R). LSTM of arms and legs measured by dual-energy X-ray absorptiometry (DXA) was the response variable. Multivariate stepwise regression analysis method was used to establish BIA estimation equations as ArmsLSTMBIA-Asian and LegsLSTMBIA-Asian. Estimation equations performance was confirmed by cross-validation. Estimation equation "ArmsLSTMBIA-Asian = 0.096 h2/R- 1.132 Sex + 0.030 Weight + 0.022 Xc- 0.022 h + 0.905, r2 = 0.855, SEE = 0.757 kg, n = 132" and "LegsLSTMBIA Asian = 0.197h2/R" + 0.120 h- 1.242 Sex + 0.055 Weight- 0.052 Age + 0.033 Xc -16.136, r2 = 0.916, SEE = 1.431 kg, n = 132" were obtained from MG. Using DXA measurement results of VG for correlation analysis and Limit of Agreement (LOA) of Bland-Altman Plot, ArmsLST is 0.924, -1.53 to 1.43 kg, and LegsLST is 0.957, -2.68 to 2.90 kg. The established single-frequency BIA hand-to-foot (whole body) estimation equation quickly and accurately estimates LSTM of the arms and legs of Asian athletes.

Validation of Bioelectrical Impedance Devices for the Determination of Body Fat Percentage in Firefighters.

Jagim, AR, Luedke, J, Erickson, JL, Fields, JB, and Jones, MT. Validation of bioelectrical impedance devices for the determination of body fat percentage in firefighters. J Strength Cond Res 38(8): e448-e453, 2024-To cross-validate bioelectrical impedance devices for the determination of body fat percentage (BF%) in firefighters. Twenty-eight structural firefighters were evaluated (female, n = 2; male, n = 26 [mean ± SD] age: 38.2 ± 8.3 years; height: 180.2 ± 7.5 cm; body mass: 86.7 ± 20.8 kg; body mass index: 25.8 ± 7.8 kg·m-2) using multifrequency bioelectrical impedance analysis (MFBIA) hand-to-foot device, and single-frequency BIA foot scale (F2FBIA), and a single-frequency handheld BIA device (HHBIA). Dual X-ray absorptiometry served as the criterion. Validity metrics were examined to establish each method's performance. Body fat % values produced by MFBIA (r = 0.913), F2FBIA (r = 0.695), and HHBIA (r = 0.876) were strongly associated (p < 0.001) with criterion BF% measures. However, MFBIA, F2FBIA, and HHBIA all significantly (p < 0.001) underestimated BF% when compared with the criterion measure. Constant error ranged between 4.0 and 5.5% across all BIA devices. Despite strong associations between the BIA devices included in the current study and the criterion measure, all BIA devices underestimated BF%, which resulted in an overestimation of fat-free mass. In addition, proportional bias was observed in which BF% was overestimated at lower values and underestimated at higher values.

Fat-free mass predictive equation using multifrequency bioelectrical impedance data in adolescent soccer athletes: development and cross-validation

ObjectivesThis study aimed to develop and cross-validate a fat-free mass (FFM) predictive equation using multifrequency bioelectrical impedance analysis (BIA) data in adolescent soccer athletes. MethodsMale adolescent soccer athletes (n = 149; 13–19 y old) were randomly sorted using Excel and independently selected for development group (n = 100) or cross-validation group (n = 49). The FFM reference values were determined using dual-energy X-ray absorptiometry. Single-frequency BIA was used to plot tolerance ellipses. Multifrequency-BIA raw data were used as independent variables in regression models. Student's independent t-test was used to compare development and cross-validation groups. Stepwise multiple regression was used to develop the FFM predictive equation. Bland-Altman plots, Lin's concordance correlation coefficient, according to McBride criteria, precision, accuracy, and standard error of estimate (SEE) were calculated to evaluate the concordance and reliability of estimates. Bioelectrical impedance vector analysis was plotted to assess hydration status. ResultsNo differences (P > 0.05) were observed between development and validation groups in chronological age, anthropometric data, bioelectrical impedance data, and FFM values obtained using dual-energy X-ray absorptiometry. Bioelectrical impedance vector analysis tolerance showed that all participants presented adequate hydration status compared to the reference population. The new FFM predictive equation developed and validated: FFM (kg) = −7.064 + 0.592 × chronological age (y) + 0.554 × weight (kg) + 0.365 × height²/resistance (cm²/Ω), presented R² = 0.95; SEE = 1.76 kg; concordance correlation coefficient = 0.95, accuracy = 0.98, and strength of concordance = 0.99. ConclusionsThe present study developed and cross-validated an FFM predictive equation based on multifrequency bioelectrical data providing substantial FFM accuracy for male adolescent soccer athletes.

Is phase angle associated with visceral adiposity and cardiometabolic risk in cardiology outpatients?

Phase angle (PhA) serves as a prognostic marker in various clinical scenarios, reflecting oxidative stress and cellular damage. Despite its clinical relevance, its connection with adiposity and cardiovascular risk markers remains underexplored. Hence, our study sought to investigate the relationship between PhA and metabolic, adiposity, and cardiovascular risk parameters among outpatients with cardiology diagnosis. Adults aged between 26 and 59 years, under the care of a cardiology unit, were included. Ultrasound imaging was used to assess visceral adipose tissue (VAT). Single-frequency bioelectrical impedance analysis (BIA) [50 kHz] was employed to calculate PhA, from BIA's resistance and reactance measurements. Muscle strength, body mass index, waist circumference, and waist-to-height ratio were also evaluated. Framingham's risk score was calculated to estimate the cardiovascular risk events. Metabolic blood samples' results were obtained from medical records. One hundred and five participants were included in our study. Low PhA was observed in 29.5% of our sample. Higher PhA values were independently and inversely associated with both higher VAT and cardiovascular risk (adjusted OR: 0.79 [95% CI 0.69;0.91], OR: 0.74 [95% CI 0.60;0.89], respectively). Lower PhA values (≤5.59) were goodly associated with high VAT (AUC: 0.82 p < 0.001). Lower PhA values (≤5.06) were fairly associated with higher cardiovascular risk (AUC: 0.70 p = 0.003). Our study provides evidence that PhA is independently and inversely associated with elevated VAT and cardiovascular risk. These findings underscore the potential of PhA as a valuable complementary marker in assessing cardiometabolic health.

The choice of diagnostic modality influences the proportion of low muscle strength, low muscle mass, and sarcopenia in colorectal cancer patients

Low muscle strength, low muscle mass, and sarcopenia have a negative impact on health outcomes in colorectal cancer (CRC) patients. Different diagnostic modalities are used to identify these conditions but it is unknown how well the modalities agree. The aim of this study was to compare different diagnostic modalities by means of calculating the proportion of low muscle strength, low muscle mass, and sarcopenia in CRC patients, and to investigate the agreement for sarcopenia between the various modalities. Men and women participating in the Norwegian Dietary Guidelines and colorectal cancer Survival (CRC-NORDIET) study were included in the analyses. Cut-off values for low muscle strength, low muscle mass, and sarcopenia were defined according to the second consensus set by the European Working Group on Sarcopenia in Older People (EWGSOP2). The diagnostic modalities used to assess muscle strength were handgrip strength and the sit-to-stand test. For muscle mass, computed tomography, dual-energy X-ray absorptiometry (DXA), multi-frequency bioelectrical impedance analysis (MF-BIA), and single-frequency BIA (SF-BIA) were applied. Cohen's kappa was calculated to determine the agreement for low muscle strength and confirmed sarcopenia between diagnostic modalities. Five hundred and three men and women (54% men, mean age of 66 (range 50-80) years old) were included in the analysis. As much as 99% (n=70) of the population was identified with low muscle mass by MF-BIA, while the other modalities identified 9-49% as having low muscle mass. Handgrip strength identified a lower proportion of low muscle strength as compared with the sit-to-stand test (4% vs. 8%). When applying various combinations of diagnostic modalities for low muscle strength and low muscle mass, the proportion of sarcopenia was found to be between 0.3 and 11.4%. There was relatively poor agreement between the different diagnostic modalities with Cohen's Kappa ranging from 0.0 to 0.55, except for the agreement between SF-BIASergi and MF-BIASergi, which was 1. The proportion of low muscle strength, low muscle mass, and sarcopenia in CRC patients varied considerably depending on the diagnostic modalities used. Further studies are needed to provide modality-specific cut-off values, adjusted to sex, age and body size.

Application of Bioelectrical Impedance Analysis in Nutritional Management of Patients with Chronic Kidney Disease.

Body composition measurement plays an important role in the nutritional diagnosis and treatment of diseases. In the past 30 years, the detection of body composition based on bioelectrical impedance analysis (BIA) has been widely used and explored in a variety of diseases. With the development of technology, bioelectrical impedance analysis has gradually developed from single-frequency BIA (SF-BIA) to multi-frequency BIA (multi-frequency BIA, MF-BIA) and over a range of frequencies (bioimpedance spectroscopy, BIS). As the clinical significance of nutrition management in chronic kidney disease has gradually become prominent, body composition measurement by BIA has been favored by nephrologists and nutritionists. In the past 20 years, there have been many studies on the application of BIA in patients with CKD. This review describes and summarizes the latest research results of BIA in nutritional management of patients with CKD including pre-dialysis, hemodialysis, peritoneal dialysis and kidney transplantation, in order to provide reference for the application and research of BIA in nutritional management of chronic kidney disease in the future.

Prevalence and determinants of lymphedema in newly diagnosed Nigerian breast cancer patients using bioimpedance estimations.

Breast cancer-related lymphedema (BCRL) is common and has significant impact on quality of life. Very little is known about BCRL in sub-Saharan Africa. Generally, BCRL has been mostly evaluated post treatment, with very limited data on the prevalence of pre-treatment BCRL at baseline. This study presents the prevalence and clinical associations of lymphedema among newly diagnosed, treatment-naive breast cancer patients in a Nigerian cohort using bioimpedance estimations. Consecutively consenting, newly diagnosed, treatment-naive breast cancer patients were assessed for upper limb lymphedema using bioimpedance measurements of the extracellular fluid and the single-frequency bioelectrical impedance analysis value at 5 kHz. Patients were classified as having lymphedema if there was >10% difference in arm measurements or if the ratios of the arm measurements were >3 SD above a normative mean generated from representative controls. Regression analysis was performed to determine clinical variables associated with lymphedema. There were 154 breast cancer patients with a median age of 47 (40.0-56.8) years and a body mass index of 27 (23.5-30.9) kg/m2. The majority (70%) had stage III disease. All measurements were significantly higher in cases than controls. Using various definitions, the prevalence of lymphedema was between 11.7% and 14.3%. Various clinical variables relating to clinical stage were significantly associated with lymphedema. The predominance of locally advanced disease in the Nigerian setting is associated with high pre-treatment lymphedema rates. This may set the stage for higher rates in the post-operative setting. Management of lymphedema should be incorporated into the treatment planning.

Differences in Body Composition Analysis by DEXA, Skinfold and BIA Methods in Young Football Players.

The most widely used method in professional sports for fat percentage assessment is the skinfold method. However, there is the chance of bias and human error. For this reason, other more precise methods are used, such as single-frequency bioelectrical impedance analysis (BIA) or dual energy X-ray absorptiometry (DEXA). However, there are limited data that demonstrate the methodological shortcomings or congruences in fat and fat-free mass estimates including gender differences and differences in athlete populations. Thus, the aim of the present study was to compare total body fat (%BF) estimated by six skinfold thickness measurement (SKF) and single-frequency bioelectrical impedance analysis (BIA) methods, using three different sets of equations, to that assessed by the dual energy X-ray absorptiometry (DEXA) method using a DEXA Hologic Serie Discovery QDR. For this aim, 76 males and 70 females belonging to the professional Spanish football federation were evaluated. We found significant differences between the three measures. BIA significantly underestimates the fat percentage, followed by skinfolds. With DEXA being the more objective or accurate method, an equation is established by means of linear regression analysis that allows the percentage of adipose tissue to be obtained either through anthropometry or electrical bioimpedance and adjusted to that which would be obtained by the DEXA system.

Low phase angle in critically ill older patients is associated with late mortality: A prospective study

ObjectivesThe aim of this study was to ascertain the accuracy of phase angle (PhA) as a predictor of mortality during intensive care unit (ICU) stay (MICU) and at 28 (M28) and 60 d (M60) after ICU admission among patients aged >60 y. MethodsPatients aged >60 y who were under mechanical ventilation (MV) ≥48 h were included once they were hemodynamically stable. PhA was measured by single-frequency bioelectrical impedance analysis up to 48 h after admission. ICU prognostic scores, functional scale, and nutritional assessments were performed in the first 24 h. Patients were followed for 60 d after ICU admission. ResultsWe enrolled 102 patients into the present study. PhA was significantly higher (P < 0.001) in survivors at MICU, M28, and M60. Areas under the receiving operator characteristic curves for MICU, M28, and M60 were 0.77 (95% confidence interval [CI], 0.67–0.86), 0.71 (95% CI, 0.60–0.82), and 0.71 (95% CI, 0.60–0.81), respectively. The PhA cutoff to predict mortality was 3.29° for males at MICU, M28, and M60 and lower for females at M28 (2.63°) and M60 (3.01°). PhA better discriminated M60 than conventional prognostic scores. Logistic regression showed that even after controlling for other factors, PhA was a protective factor against late mortality. Survival analysis at 60 d revealed that low PhA was associated with lower median survival (18 versus 58 d; log-rank P < 0.001). ConclusionsLow PhA values are associated with higher late mortality and a short survival time at 60 d in critically ill older adults. Low PhA values can be considered a useful ICU prognostic score in similar populations.

Bioelectrical impedance vector analysis and body composition in cervical spinal cord injury: A pilot study.

IntroductionBody composition assessment in cervical spinal cord injury (c-SCI) individuals is important to monitor the fat free-mass (FFM) loss, due to immobilization, or gain, due to exercise practice. Single frequency bioelectrical impedance analysis (SF-BIA) is low in cost, simple and easy.ObjectivesThe aims of this study are: to evaluate the concordance between the FFM values obtained using dual X-ray absorptiometry (DXA) and the three SF-BIA previous predictive equations; and to test the applicability of the bioelectrical impedance vector analysis (BIVA).MethodsTwenty-three c-SCI males were divided into two groups: Physically active (PA; n = 13; at least 150 min/week) and non-active individuals (NPA) and were assessed by DXA and SF-BIA simultaneously.ResultsFFM values were similar between groups PA and NPA. Considering all participants, FFM values obtained by Kocina and Heyward (>11%) and Sun (<15.4%) predictive equations were different when compared to DXA (p < 0.01). However, Buchholz's et al. predictive equation showed FFM values similar to DXA, but presented poor concordance (<7%, p = 0.99; concordance coefficient = 0.85). BIVA showed consistency in ellipse distribution using FFM obtained using Buchholz et al. predictive equation.ConclusionsThe use of non-specific BIA equations can lead to misinterpretation in FFM values in male c-SCI individuals. Predictive equations for this group need to be developed.

Predicting resting energy expenditure in people with chronic spinal cord injury.

Cross-sectional study. The aims of this study were (1) to validate the two recently developed SCI-specific REE equations; (2) to develop new prediction equations to predict REE in a general population with SCI. University, the Netherlands. Forty-eight community-dwelling men and women with SCI were recruited (age: 18-75 years, time since injury: ≥12 months). Body composition was measured by dual-energy X-ray absorptiometry (DXA), single-frequency bioelectrical impedance analysis (SF-BIA) and skinfold thickness. REE was measured by indirect calorimetry. Personal and lesion characteristics were collected. SCI-specific REE equations by Chun et al. [1] and by Nightingale and Gorgey [2] were validated. New equations for predicting REE were developed using multivariate regression analysis. Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE (Chun et al.: -11%; Nightingale and Gorgey: -11%). New equations were developed for predicting REE in the general population of people with SCI using FFM measured by SF-BIA and Goosey-Tolfrey et al. skinfold equation (R2 = 0.45-0.47; SEE = 200 kcal/day). The new equations showed proportional bias (p < 0.001) and wide limits of agreement (LoA,±23%). Prediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE and showed large individual variations in a general population with SCI. The newly developed REE equations showed proportional bias and a wide LoA (±23%) which limit the predictive power and accuracy to predict REE in the general population with SCI. Alternative methods for measuring REE need to be investigated.

Comparison of Bioelectrical Impedance Analysis with DXA in Adolescents with Cystic Fibrosis before and after a Resistance Training Intervention.

Background: The purpose of this pilot study was to compare body composition metrics obtained by two portable bioelectrical impedance analysis (BIA) devices with dual-energy X-ray absorptiometry (DXA) among adolescents with cystic fibrosis (CF) before and after a resistance exercise training program. Methods: Participants with CF were assessed using DXA, single-frequency BIA (SFBIA), and multiple-frequency BIA (MFBIA) to quantify percent body fat (%Fat), fat mass (FM), and fat-free mass (FFM) at baseline and after a home-based resistance training intervention comprised of 36, 1 h sessions completed in 12–14 weeks. Repeated measures analysis of variance, paired samples t-tests, Cohen’s d effect sizes, and Pearson’s correlations were used to compare differences between and within methods at baseline and post-intervention. Results: Ten participants (15.8 ± 2.2 yr, 60.1 ± 15.1 kg) completed the assessments. At baseline, both SFBIA and MFBIA scales significantly underestimated %Fat and FM and overestimated FFM, with small to moderate effect sizes. Post-intervention, small, non-significant differences were found between DXA and both BIA scales for all body composition metrics. Significant changes in %Fat and FFM were observed with DXA. MFBIA displayed less constant error than SFBIA when compared to DXA for pre- and post-intervention assessments for %Fat (MFBIA: pre and post −2.8 and −0.8 vs. SFBIA: −4.6 and −2.0), FM (−0.4 and −0.4 vs. −3.0 and −1.1), and FFM (+0.8 and +0.6 vs. +3.1 and +1.3). Near-perfect correlations were observed at both time points between DXA and each BIA scale. Conclusions: Portable BIA results should be interpreted with caution, and further validation studies in CF patients are needed prior to clinical use.

Bioelectrical impedance vector analysis in patients on the waiting list for liver transplant: Associated factors and prognostic effects

ObjectivesThe aim of this study was to assess patients on the waiting list for liver transplant (LTx) according to bioelectrical impedance vector analysis (BIVA), as well as to verify the association between the placement of the vectors on the graph with clinical outcomes and identify the predictors to vector placement in quadrant 4 (Q4; indicating more hydration and less cellularity). MethodsThis was a retrospective observational study including 129 patients ≥20 y of age awaiting LTx. Patients’ nutritional status was assessed by using different tools, including single-frequency bioelectrical impedance analysis and the Subjective Global Assessment (SGA). Clinical data were registered. The BIVA was evaluated by comparing the individual vectors plotted for all patients to the tolerance ellipses of 50%, 75%, and 95% of the reference healthy population. The quadrant of the vector for each patient was registered. ResultsThe majority of the vectors were placed in Q1 (n = 54; 41.9%) and Q4 (n = 39; 30.2%). The presence of ascites or edema (hazard ratio [HR], 2.43; 95% confidence interval [CI], 1.15–5.12; P = 0.019) and the BIVA vector placed in Q4 in any ellipse (HR, 2.10; 95% CI, 1.07–4.09; P = 0.029) were independent predictors for mortality on the waiting list or ≤1 y after LTx. BIVA was not associated with longer hospital length of stay. The predictors of vector placement in Q4 were higher age, malnutrition according to SGA, and presence of ascites or edema. ConclusionPatients on the waiting list for LTx with BIVA vectors placed in Q4, in the 50%, 75%, or 95% tolerance ellipses, presented a worse prognosis.

Accuracy of bioelectrical impedance analysis and skinfold thickness in the assessment of body composition in people with chronic spinal cord injury.

Cross-sectional study. This study: (1) investigated the accuracy of bioelectrical impedance analysis (BIA) and skinfold thickness relative to dual-energy X-ray absorptiometry (DXA) in the assessment of body composition in people with spinal cord injury (SCI), and whether sex and lesion characteristics affect the accuracy, (2) developed new prediction equations to estimate fat free mass (FFM) and percentage fat mass (FM%) in a general SCI population using BIA and skinfolds outcomes. University, the Netherlands. Fifty participants with SCI (19 females; median time since injury: 15 years) were tested by DXA, single-frequency BIA (SF-BIA), segmental multi-frequency BIA (segmental MF-BIA), and anthropometry (height, body mass, calf circumference, and skinfold thickness) during a visit. Personal and lesion characteristics were registered. Compared to DXA, SF-BIA showed the smallest mean difference in estimating FM%, but with large limits of agreement (mean difference = -2.2%; limits of agreement: -12.8 to 8.3%). BIA and skinfold thickness tended to show a better estimation of FM% in females, participants with tetraplegia, or with motor incomplete injury. New equations for predicting FFM and FM% were developed with good explained variances (FFM: R2 = 0.94; FM%: R2 = 0.66). None of the measurement techniques accurately estimated FM% because of the wide individual variation and, therefore, should be used with caution. The accuracy of the techniques differed in different subgroups. The newly developed equations for predicting FFM and FM% should be cross-validated in future studies.

Agreement of bioelectrical resistance, reactance, and phase angle values from supine and standing bioimpedance analyzers

Objective. Bioimpedance devices are commonly used to assess health parameters and track changes in body composition. However, the cross-sectional agreement between different devices has not been conclusively established. Thus, the objective of this investigation was to examine the agreement between raw bioelectrical variables (resistance, reactance, and phase angle at the 50 kHz frequency) obtained from three bioimpedance analyzers. Approach. Healthy male (n = 76, mean ± SD; 33.8 ± 14.5 years; 83.9 ± 15.1 kg; 179.4 ± 6.9 cm) and female (n = 103, mean ± SD; 33.4 ± 15.9 years; 65.6 ± 12.1 kg; 164.9 ± 6.4 cm) participants completed assessments using three bioimpedance devices: supine bioimpedance spectroscopy (BIS), supine single-frequency bioelectrical impedance analysis (SFBIA), and standing multi-frequency bioelectrical impedance analysis (MFBIA). Differences in raw bioelectrical variables between the devices were quantified via one-way analysis of variance for the total sample and for each sex. Equivalence testing was used to determine equivalence between methods. Main results. Significant differences in all bioelectrical variables were observed between the three devices when examining the total sample and males only. The devices appeared to exhibit slightly better agreement when analyzing female participants only. Equivalence testing using the total sample as well as males and females separately revealed that resistance and phase angle were equivalent between the supine devices (BIS, SFBIA), but not with the standing analyzer (MFBIA). Significance. The present study demonstrated disagreement between different bioimpedance analyzers for quantifying raw bioelectrical variables, with the poorest agreement between devices that employed different body positions during testing. These results suggest that researchers and clinicians should employ device-specific reference values to classify participants based on raw bioelectrical variables, such as phase angle. If reference values are needed but are unavailable for a particular bioimpedance analyzer, the set of reference values produced using the most similar analyzer and reference population should be selected.

Physiological And Anthropometric Predictors Of Discrepancies Between Bioelectrical Impedance Analysis And Dual-energy X-ray Absorptiometry

Assessment methods such as dual-energy x-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are commonly used to determine molecular-level body composition estimates in both healthy and clinical populations. Though discrepancies in body composition estimates between BIA and DXA have been previously reported, virtually no investigations have explored potential predictor variables that may explain the disagreement between these methods. PURPOSE: To explore the physiological and anthropometric predictors of discrepancies between DXA and BIA total body composition estimates. METHODS: During a single visit, 103 female participants (Mean ± SD; 33.4 ± 15.9 years; 65.6 ± 12.1 kg; 165.5 ± 6.3 cm; 32.2 ± 7.4 DXA body fat percentage [BF%]) and 76 male participants (33.8 ± 14.5 years; 83.9 ± 15.1 kg; 178.8 ± 6.6 cm; 22.9 ± 8.0 DXA BF%) underwent body composition assessment via DXA and 8-point single-frequency BIA. Potential predictors of the discrepancies between DXA and BIA body composition estimates were obtained during the same visit using air displacement plethysmography, bioimpedance spectroscopy, and 3-dimensional optical scanning. Select DXA variables were also utilized as predictors. Ordinary least squares regression was conducted to predict the differences in total fat mass (FM) and total lean soft tissue (LST) between DXA and BIA. Standardized model coefficients (β), p-values for coefficients, and R2 values were generated. RESULTS: For FM estimates, significant predictors (p ≤ 0.01) of the differences between DXA and BIA were hydration of LST (TBW:LST; β = -0.82), height (β = -0.78), percentage of TBW as extracellular fluid (%ECF; β = -0.36), and the male sex (β = 0.26). For LST, significant predictors of the errors between DXA and BIA were TBW:LST (β = 0.85), height (β = 0.77), %ECF (β = 0.40), the male sex (β = -0.22), and racial identification as Black (β = -0.09). The R2 values for regression models predicting differences between DXA and BIA were 0.80 to 0.86 for FM and 0.73 to 0.87 for LST. CONCLUSION: Hydration variables and height are primary predictors of discrepancies between DXA and BIA total body composition estimates.

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.

Explaining Discrepancies Between Total and Segmental DXA and BIA Body Composition Estimates Using Bayesian Regression

Introduction/Background: Few investigations have sought to explain discrepancies between dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) body composition estimates. The purpose of this analysis was to explore physiological and anthropometric predictors of discrepancies between DXA and BIA total and segmental body composition estimates. Methodology: Assessments via DXA (GE Lunar Prodigy) and single-frequency BIA (RJL Systems Quantum V) were performed in 179 adults (103 F, 76 M, age: 33.6 ± 15.3 yr; BMI: 24.9 ± 4.3 kg/m2). Potential predictor variables for differences between DXA and BIA total and segmental fat mass (FM) and lean soft tissue (LST) estimates were obtained from demographics and laboratory techniques, including DXA, BIA, bioimpedance spectroscopy, air displacement plethysmography, and 3-dimensional optical scanning. To determine meaningful predictors, Bayesian robust regression models were fit using a t-distribution and regularized hierarchical shrinkage “horseshoe” prior. Standardized model coefficients (β) were generated, and leave-one-out cross validation was used to assess model predictive performance. Results: LST hydration (i.e., total body water:LST) was a predictor of discrepancies in all FM and LST variables (|β|: 0.20–0.82). Additionally, extracellular fluid percentage was a predictor for nearly all outcomes (|β|: 0.19–0.40). Height influenced the agreement between whole-body estimates (|β|: 0.74–0.77), while the mass, length, and composition of body segments were predictors for segmental LST estimates (|β|: 0.23–3.04). Predictors of segmental FM errors were less consistent. Select sex-, race-, or age-based differences between methods were observed. The accuracy of whole-body models was superior to segmental models (leave-one-out cross-validation-adjusted R2 of 0.83–0.85 for FMTOTAL and LSTTOTAL vs. 0.20–0.76 for segmental estimates). For segmental models, predictive performance decreased in the order of: appendicular lean soft tissue, LSTLEGS, LSTTRUNK and FMLEGS, FMARMS, FMTRUNK, and LSTARMS. Conclusions: These findings indicate the importance of LST hydration, extracellular fluid content, and height for explaining discrepancies between DXA and BIA body composition estimates. These general findings and quantitative interpretation based on the presented data allow for a better understanding of sources of error between 2 popular segmental body composition techniques and facilitate interpretation of estimates from these technologies.

Pulmonary rehabilitation outcome in chronic obstructive pulmonary disease patients with a different body composition

ContextChange in body composition is commonly present in chronic obstructive pulmonary disease (COPD) patients.AimThe aim of this study was to investigate the effects of pulmonary rehabilitation program (PRP) on COPD patients who have a different body composition.Materials and methodsIn this study, we measure;Deg;BM; Deg;I and fat-free mass index (FFMI) using a single-frequency bioelectrical impedance analysis apparatus to classify patients into three categories: Group 1 nonmuscle depleted;Deg;BM;Deg;I greater than or equal to 21 kg/m2 and FFMI greater than or equal to 16. Group 2 muscle depleted; Deg;BM;Deg;I greater than or equal to 21 kg/m2 and FFMI less than 16 in men or FFMI less than 15 in women. Group 3 muscle depleted with cachexia;Deg;BM;Deg;I less than 21 kg/m2 and FFMI less than 16 in men or FFMI less than 15 in women. PRP outcomes were assessed by the improvement in pulmonary function severity, exercise capacity by 6-min walk test, dyspnea score by modified-British Medical Research Council, and health status by combined assessment test score and arterial blood gas improvements.ResultsForty-four patients with FFMI were measured by bioelectrical impedance analysis. The patients were mainly elderly men (N=35; 79%), who have a mean age of 65 years with different global initiative obstructive lung disease stage I–IV. In the nonmuscle depleted group, there were statistically significant improvements in the mean values of FFMI (kg/m2) while in the muscle depleted group there were improvements as regards the mean values of dyspnea score by modified-British Medical Research Council; in the cachectic group there were statistically significant improvements in the mean values of BMI (kg/m2), forced expiratory volume in the first second (FEV1), forced expiratory volume in first second divided by forced vital capacity ratio, combined assessment test score after PRP.ConclusionsA comprehensive PRP outcome change in COPD patients with different body compositions.