Bioelectrical Impedance Analysis Equations Research Articles

Appendicular Skeletal Muscle Mass (ASMM) and Fat-Free Mass (FFM) DXA–BIA Estimations for the Early Identification of Sarcopenia/Low Muscle Mass in Middle-Aged Women

Background/Objectives: Sarcopenia involves the loss of muscle mass along with a decrease in muscle strength and physical performance. The aim of this paper was to compare the already published BIA equations for the estimation of Appendicular Skeletal Muscle Mass (ASMM) and Fat-Free Mass (FFM) with dual X-ray densitometer DXA estimations in order to determine whether Bioelectrical Impedance Analysis (BIA) could be a feasible application on a general population for the detection of low muscle mass and sarcopenia. Methods: Seventy-nine healthy women aged 40–70 years were included. Assessments involved BIA and DXA evaluations and anthropometric measurements. Results: DXA and BIA estimations showed great agreement, particularly the ones introduced by Scafoglieri et al. (2017) for ASMM (mean difference 1.81 kg) and Kanellakis et al. (2020) equation for FFM (mean difference 0.52 kg) resulted in the best fit for the cohort in analysis. BIA could intercept a low muscle mass condition which can be linked to sarcopenia. Conclusions: This study showed how the use of BIA represents an effective and reliable method in the evaluation of sarcopenia.

Comparative analysis of basal metabolic rate measurement methods in overweight and obese individuals: A retrospective study.

The global prevalence of overweight and obesity is on the rise, presenting significant health challenges worldwide. Obesity is associated with various chronic diseases and imposes substantial economic burdens on society. Accurate assessment of basal metabolic rate (BMR) is crucial for effective weight management strategies. This retrospective study, conducted at Baskent University Hospital between October 2019 and October 2023, analyzed data from 133 overweight and obese individuals. Various methods including indirect calorimetry (IC), predictive equations (Harris-Benedict and Mifflin-St Jeor), and bioelectrical impedance analysis (BIA) were used to estimate BMR. Additionally, demographic, clinical, and body composition data were recorded. The mean BMR measured using IC was 1581 ± 322 kcal/day, which was significantly lower than the BMR estimated by other methods such as BIA (1765.8 ± 344.09 kcal/day), Harris-Benedict (1787.64 ± 341.4 kcal/day), and Mifflin-St Jeor equations (1690.08 ± 296.36 kcal/day) (P < .001). Among the predictive equations, the Mifflin-St Jeor method provided BMR estimates closest to the gold standard IC. When BMR measurement methods were compared to IC, 36.8% of measurements with the Harris-Benedict equation method, 50.4% with the Mifflin-St Jeor equation method, and 36.1% with the BIA method were within ± 10% agreement with IC measurements. Significant correlations were found between BMR and body composition parameters such as fat-free mass, muscle mass, and fat mass (R = 0.681, P < .001; R = 0.699, P < .001; R = 0.595, P < .001, respectively). Regression analysis identified that variables such as weight, height, body mass index, and muscle mass significantly predicted BMR measured by IC, accounting for 69.1% of the variance. This study underscores the challenges in assessing BMR in overweight and obese individuals. While IC remains the gold standard, predictive equations and BIA offer alternative methods. The Mifflin-St Jeor equation emerged as a practical option, closely aligning with IC results. However, discrepancies between methods and the influence of body composition highlight the importance of individualized approaches to BMR assessment in weight management strategies.

New specific skeletal muscle mass index cut-offs for the assessment of sarcopenia in patients with severe obesity.

Bioelectrical impedance analysis (BIA) is the most used tool in clinical practice to evaluate body composition in patients with obesity. The skeletal muscle index (SMI) defined by BIA has been proposed for the identification of sarcopenia, but there are currently no univocal cutoffs for this condition. In this study, we aimed: 1) to determine the prevalence of sarcopenia in patients with severe obesity using the current cutoffs of SMI; 2) to define new specific cutoffs; 3) to validate the new cutoffs; and 4) to re-determine the prevalence of sarcopenia. A total of 300 patients, 74% women and 26% men (mean age = 42.6 ±; 9 years), with morbid obesity (mean BMI = 46.7 ±; 6.5 kg/m2) followed by the Unit of Endocrinology from January 2014 to December 2020 were retrospectively evaluated. SMI was calculated as the skeletal muscle mass normalized for squared height through the BIA equation by Janssen etal. The prevalence of sarcopenic obesity calculated using the cutoff points reported by De Rosa etal. (7.3 kg/h2 for women and 9.5 kg/h2 for men) was 2.3%. The prevalence of sarcopenia was calculated using the new cutoffs: with the cutoff obtained from the standard deviation method (8.2 kg/h2 for women and 10.2 kg/h2 for men), a prevalence of 14.7% was observed, whereas the prevalence reached 47.6% when using the cutoff calculated through the K-means unsupervised cluster (9.2 kg/h2 for women and 11.3 kg/h2 for men). The new cutoffs were validated with a second sample consisting of 300 patients with morbid obesity (BMI = 44.9 ±; 6.7 kg/m2): the rate of sarcopenic patients was still higher than that observed in the training cohort (56%). After the matching procedure (by BMI and age), the rates of sarcopenic patients were similar in both groups (50.2% in the validation group and 53% in the training group, p = 0.6). The new cutoffs calculated with cluster analysis could better identify sarcopenia in morbidly obese patients. However, further studies are needed to validate these cutoffs in different patient cohorts.

390 Comparison of Body Composition Measurements in Patients With Chronic Kidney Disease Receiving Maintenance Hemodialysis Using Bioelectrical Impedance Analysis and Anthropometric Equations

390 Comparison of Body Composition Measurements in Patients With Chronic Kidney Disease Receiving Maintenance Hemodialysis Using Bioelectrical Impedance Analysis and Anthropometric Equations

Comparison of bioimpedance equations and dual-energy X-ray for assessment of fat free mass in a Chinese dialysis population

Purpose Bioelectrical impedance analysis (BIA) is simple, noninvasive, inexpensive and frequently used for estimating fat free mass (FFM). The aims of this study were to evaluate the applicability of different BIA equations on FFM in Chinese subjects, and to compare the difference in hemodialysis and peritoneal dialysis patients with healthy controls respectively. Methods Dialysis patients and healthy adults were enrolled in this study, and the subjects were matched by age, gender, and the minimum sample size in each group was calculated using PASS. FFM estimated by BIA was calculated using equations of Kyle, Sun SS and Segal, and TBW/0.73. Dual-energy X-ray absorptiometry (DXA) method was set as reference method. Pearson’s correlation and Bland-Altman analysis were used to test the validity of the BIA equations. Results 50 hemodialysis (HD) patients, 52 peritoneal dialysis (PD) patients and 30 healthy adults aged 22–67 y were included in this study. Age, height, weight, BMI and gender did not differ significantly among HD, PD patients, and healthy controls (p > 0.05), but BIA parameters were quite different (p＜0.01). Bland-Altman analysis showed that in healthy volunteers, all equations showed good agreement with DXA measured. For dialysis patients, the FFM predictions of different equations showed differences between HD and PD patients, and the equations seemed more applicable for HD patients. Conclusion The equations developed by healthy subjects might be not appropriate for dialysis patients, especially peritoneal dialysis patients. It is recommended to develop a specific BIA equation from dialysis population.

A Comparison of Bioimpedance Analysis vs. Dual X-ray Absorptiometry for Body Composition Assessment in Postpartum Women and Non-Postpartum Controls.

Postpartum fat mass (FM) and fat-free mass (FFM) may be informative predictors of future disease risk among women; hence, there is growing use of bioelectrical impedance analysis (BIA) to quantify FFM and FM among postpartum women due to the quick, non-invasive, and inexpensive nature of BIA. Despite this, very few studies have examined BIA’s performance, and it remains unclear as to whether specific BIA equations are needed for postpartum women. To explore these questions, we measured total body FFM and FM with a multi-frequency, segmental BIA, and dual-X-ray absorptiometry (DXA) in (1) women at one and four months postpartum (n = 21); and (2) height- and weight-matched non-postpartum women (controls, n = 21). BIA was compared to DXA using Deming regression models, paired t-tests, and Bland–Altman plots. Between-group comparisons were performed using an analysis of variance models. The mean difference between DXA and BIA was 1.2 ± 1.7 kg FFM (p < 0.01) and −1.0 ± 1.7 kg FM (p < 0.05) in postpartum women at both time points. The measurements of longitudinal changes in FFM and FM were not significantly different between BIA and DXA. Furthermore, there was no significant difference in BIA’s performance in postpartum vs. non-postpartum women (p = 0.29), which suggests that population-specific equations are not needed for postpartum women. The results of this study suggest that BIA is a suitable method to assess postpartum body composition among women at one and four months postpartum, using existing age-, race-, and sex-adjusted equations.

External validation of BIA equations to estimate appendicular skeletal muscle mass in older adults: Importance of the bias analysis and derivation of correction factors to achieve agreement.

There are several equations based on bioelectrical impedance analysis (BIA) to estimate with high precision appendicular skeletal muscle mass (ASM). However, most of the external validation studies have reported that these equations are inaccurate or biased when applied to different populations. Furthermore, none of the published studies has derived correction factors (CFs) in samples of community-dwelling older adults, and none of the published studies have assessed the influence of the dual-energy X-ray absorptiometry (DXA) model on the validation process. This study assessed the agreement between six BIA equations and DXA to estimate ASM in non-Caucasian older adults considering the DXA model and proposed a CF for three of them. This analysis included 547 non-institutionalized subjects over 60 years old from the northwest of Mexico who were physically independent and without cognitive impairment: 192 subjects were measured using DXA Hologic, while 355 were measured by DXA Lunar. The agreement between each of the equations and DXA was tested considering the DXA model used as a reference method for the design of each equation, using the Bland and Altman procedure, a paired t test, and simple linear regression as objective tests. This process was supported by the differences reported in the literature and confirmed in a subsample of 70 subjects measured with both models. Only six published BIA equations were included. The results showed that four equations overestimated ASMDXA, and two underestimated it (p < 0.001, 95% CI for Kim's equation:−5.86-−5.45, Toselli's:−0.51-−0.15, Kyle's: 1.43–1.84, Rangel-Peniche's: 0.32–0.74, Sergi's: 0.83–1.23, and Yoshida's: 4.16–4.63 kg). However, Toselli's, Kyle's and Rangel-Peniche's equations were the only ones that complied with having a homogeneous bias. This finding allowed the derivation of CFs, which consisted of subtracting or adding the mean of the differences from the original equation. After estimating ASM applying the respective CF, the new ASM estimations showed no significant bias and its distribution remained homogeneously distributed. Therefore, agreement with DXA in the sample of non-Caucasian was achieved. Adding valid CFs to some BIA equations allowed to reduce the bias of some equations, making them valid to estimate the mean values of ASM at group level.

Accuracy of bioimpedance equations for measuring body composition in a cohort of 2134 patients with obesity

Measuring body composition is an important issue to phenotype patients with obesity and to follow the nutritional care efficiency. Bioelectrical Impedance Analysis (BIA) is a simple and rapid technique. However, validity of BIA in patients with obesity remains controversial. Thus, we aimed to evaluate the validity of several BIA equations to assess body composition in a large cohort of patients with obesity by using dual X ray absorptiometry (DXA) as reference. Seven BIA equations have been retrospectively applied on electrical data measured by BIA in patients with obesity with BMI equal or higher than 30kg/m2 and results were compared to DXA-derived fat mass (FM) and fat-free mass (FFM). BIA and DXA were done the same day after an overnight fasting. Results were compared with Bland-Altman method and Pearson correlation. We also calculated the accuracy defined as the percentage of patients with DXA-BIA difference within ± 10% of DXA measures for FFM and FM. Data from 2134 patients with class I and II obesity (ob1/2, n=1452, 47.4±14.2y; 35.0±2.7kg.m-2) and class III obesity (ob3, n=682, 48.2±13.9y; 44.1±3.5kg.m-2) were analyzed. The best results to evaluate FFM both in ob1/2 and ob3 groups were obtained with Roubenoff's equation: Bland Altman bias at-1.96 and-0.82kg, Pearson correlation r at 0.93 and 0.87, accuracy at 75.7% and 83.3%, respectively. However, limits of agreements at 95% were high: [-9.42; 5.49kg] and [-8.16; 6.52kg]. For FM evaluation, Roubenoff's equation also showed best results for ob1/2 group (bias at-1.17kg; correlation r at 0.89 and accuracy at 79.1%) but not for ob3 group. In this latter group, Deurenberg's equation exhibited the best results (bias at 2.09kg; correlation r at 0.81 and accuracy at 76.8%). However, limits of agreements remained high. In patients with obesity, Roubenoff BIA equation should be recommended to assess fat free mass, even if limits of agreements remain high.

A height-weight formula to measure body fat in childhood obesity

BackgroundThe assessment of body composition is central in diagnosis and treatment of paediatric obesity, but a criterion method is not feasible in clinical practice. Even the use of bioelectrical impedance analysis (BIA) is limited in children. Body mass index (BMI) Z-score is frequently used as a proxy index of body composition, but it does not discriminate between fat mass and fat-free mass. We aimed to assess the extent to which fat mass and percentage of body fat estimated by a height-weight equation agreed with a BIA equation in youths with obesity from South Italy. Furthermore, we investigated the correlation between BMI Z-score and fat mass or percentage of body mass estimated by these two models.MethodsOne-hundred-seventy-four youths with obesity (52.3% males, mean age 10.8 ± 1.9) were enrolled in this cross-sectional study. Fat mass and percentage of body fat were calculated according to a height-weight based prediction model and to a BIA prediction model.ResultsAccording to Bland–Altman statistics, mean differences were relatively small for both fat mass (+ 0.65 kg) and percentage of body fat (+ 1.27%) with an overestimation at lower mean values; the majority of values fell within the limits of agreement. BMI Z-score was significantly associated with both fat mass and percentage of body fat, regardless of the method, but the strength of correlation was higher when the height-weight equation was considered (r = 0.82; p < 0.001).ConclusionsThis formula may serve as surrogate for body fat estimation when instrumental tools are not available. Dealing with changes of body fat instead of BMI Z-score may help children and parents to focus on diet for health.

Development and Cross-Validation of a Predictive Equation for Fat-Free Mass in Brazilian Adolescents by Bioelectrical Impedance.

The bioelectrical impedance analysis (BIA) is one of the most commonly used techniques for assessing body composition in a clinical setting and in field approaches, as it has the advantages of easy application, fast, and non-invasive, in addition to its relatively low cost. However, the available predictive equations need to be valid for the evaluated subjects. The aim of this study was to verify the validity of several published BIA equations in estimating fat-free mass (FFM) among Brazilian adolescents, in addition to developing and cross-validating a BIA equation to estimate FFM appropriate for Brazilian adolescents. This is a cross-sectional study with 257 adolescents (128 girls) aged 10–19 years, randomly divided into two groups, namely, development (n = 172) and cross-validation (n = 85). The standard technique for assessing FFM was dual X-ray absorptiometry (DXA). The paired t-test, multiple regression, and the Bland-Altman plots were used to test the validity of the proposed models and to perform cross-validation of the model. The equation derived in this study was as follows: FFM = −17.189 + 0.498 (Height2/Resistance) + 0.226 Weight + 0.071 Reactance − 2.378 Sex + 0.097 Height + 0.222 Age; r2 = 0.92; standard error of the estimate = 2.49 kg; the new equation for FFM showed better agreement when compared with that of the equations developed in other countries. In conclusion, the newly developed equations provide a valid FFM estimation and are recommended for Brazilian adolescents with similar characteristics.

Comparison of Dual-Energy X-ray Absorptiometry and Bioelectrical Impedance Analysis in the Assessment of Body Composition in Women with Anorexia Nervosa upon Admission and Discharge from an Inpatient Specialist Unit.

Assessment of body composition is fundamental in diagnosis and treatment of anorexia nervosa (AN). The gold standard dual-energy X-ray absorptiometry (DXA) is expensive and not universally available. Bioelectrical impedance analysis (BIA) is a non-invasive, inexpensive method relative to DXA. We compared DXA and BIA in the assessment of fat-free mass (FFM), fat mass (FM), and body fat percentage (BF%) in women with AN upon admission (ANT1) and discharge (ANT2) from an inpatient specialist unit with a referent healthy control (HC) group. The study population consisted of 31 ANT1, 25 ANT2, and 52 HC women with median age of 21 years. Body composition was measured by DXA and Tanita foot-to-foot BIA. Comparison between the two methods was done using Bland–Altman analysis, Pearson’s correlation coefficient, Lin’s concordance correlation coefficient, and linear regression. The mean difference (bias) in FM and BF% values obtained by DXA and BIA in ANT1 (FM: +1.01 kg, BF%: +2.26%) and ANT2 (FM: +1.49 kg, BF%: +1.66%) were comparable to HC (FM: −1.32 kg, BF%: −2.29%) although in opposite directions. Less bias was observed in FFM values in ANT1 (−0.46 kg) and ANT2 (−0.86 kg) than in HC (+2.03 kg); however, the limits of agreement between the two methods were wider in ANT1 and ANT2 than in HC for all measurements. No association was observed between age, percentage of total body water, and the time spent on the inpatient specialist unit with the difference in estimates of body composition between DXA and BIA. Comparison of DXA and BIA suggests that DXA should remain the gold standard for measuring body composition; the development of more specific BIA equations is required to improve validity and precision of BIA in patients with AN. Despite ease and cost in both BIA access and operation, the suitability of BIA in a low bodyweight eating disorders population remains questionable.

New bioelectrical impedance analysis equations for children and adolescents based on the deuterium dilution technique

Body composition in childhood is not only a marker of the prevalence of obesity, but it can also be used to assess associated metabolic complications. Bioelectrical impedance analysis (BIA) shows promise as an easy to use, rapid, and non-invasive tool to evaluate body composition. The objectives of this study were to: (a) develop BIA prediction equations to estimate total body water (TBW) and fat-free mass (FFM) in European children and early adolescents and to validate the analysis with the deuterium dilution as the reference technique and (b) compare our results with previously published paediatric BIA equations. The cohort included 266 healthy children and adolescents between 7 and 14 years of age, 46% girls, in five European countries: Bosnia and Herzegovina, Latvia, Montenegro, North Macedonia, and Portugal. TBW and FFM were the target variables in the developed regression models. For model development, the dataset was randomly split into training and test sets, in 70:30 ratio, respectively. Model tuning was performed with 10-fold cross-validation that confirmed the unbiased estimate of its performance. The final regression models were retrained on the whole dataset. Cross-validated regression models were developed using resistance index, weight, and sex as the optimal predictors. The new prediction equations explained 87% variability in both TBW and FFM. Limits of agreement between BIA and reference values, were within ±17% of the mean, (-3.4, 3.7) and (-4.5, 4.8) kg for TBW and FFM, respectively. BIA FFM and TBW estimates were within one standard deviation for approximately 83% of the children. BIA prediction equations underestimated TBW and FFM by 0.2kg and 0.1kg respectively with no proportional bias and comparable accuracy among different BMI-for-age subgroups. Comparison with predictive equations from published studies revealed varying discrepancy rates with the deuterium dilution measurements, with only two being equivalent to the equations developed in this study. The small difference between deuterium dilution and BIA measurements validated by Bland-Altman analysis, supports the application of BIA for epidemiological studies in European children using the developed equations.

Bioelectrical impedance analysis for assessment of body composition in infants and young children-A systematic literature review.

Bioelectrical impedance analysis (BIA) is an easy to use, portable tool, but the accuracy of the technique in infants and young children (<24 months) remains unclear. A systematic literature review was conducted to identify studies that have developed and validated BIA equations in this age group. MEDLINE, Scopus, EMBASE, and CENTRAL were searched for relevant literature published up until June 30, 2020, using terms related to bioelectrical impedance, body composition, and paediatrics. Two reviewers independently screened studies for eligibility, resulting in 15 studies that had developed and/or validated equations. Forty-six equations were developed and 34 validations were conducted. Most equations were developed in young infants (≤6 months), whereas only seven were developed among older infants and children (6-24 months). Most studies were identified as having a high risk of bias, and only a few included predominantly healthy children born at term. Using the best available evidence, BIA appears to predict body composition at least as well as other body composition tools; however, among younger infants BIA may provide little benefit over anthropometry-based prediction equations. Currently, none of the available equations can be recommended for use in research or in clinical practice.

Validity of Bioimpedance Equations to Evaluate Fat-Free Mass and Muscle Mass in Severely Malnourished Anorectic Patients.

Background: Bioelectrical impedance analysis (BIA) is a simple and rapid technique to measure body composition (BC). Validity of BIA in patients with low body mass index (BMI) remains controversial. We assessed the validity of several BIA equations to evaluate fat-free mass (FFM), fat mass (FM) and muscle mass in patients with anorexia nervosa (AN) by using dual X ray absorptiometry (DXA) as reference. Methods: Sixteen BIA equations developed for FFM and appendicular lean mass (ALM) were applied on electrical data measured by BIA in AN patients with BMI <16 kg/m². BIA and DXA were done the same day after overnight fasting. Results were compared with the Bland–Altman method, Pearson correlation and a Lin concordance test. Results: Data from 115 female AN patients (14.6 ± 1.2 kg/m²; 32.3 ± 14.5 years) were included. FM and FFM assessed by DXA were, respectively, 4.2 ± 2.4 kg and 35.5 ± 3.8 kg. The best results were obtained with Sun’s equation: respectively for FM and FFM, Bland Altman bias at 0.548 and 0.706 kg, Pearson correlation r at 0.86 and 0.86 and Lin concordance coefficient at 0.81 and 0.84. However, confidence intervals (CI) at 95% were high (−2.73–3.83 kg for FM; −4.55–3.13 kg for FFM). Other equations also showed high 95% CI. Accuracy was acceptable for Sun and Bedogni equations for FFM (approximately 66%) but very low for FM prediction considering all equations (<15%). Concerning ALM evaluated at 14.88 ± 2.04 kg by DXA, only Scafoglieri and Yoshida equations showed acceptable values: bias (−0.2 and 2.8%), Pearson r (0.89 and 0.86), Lin concordance coefficient (0.82 and 0.82) and accuracy (83.5 and 82.6%). Confidence intervals at 95% were high for both equations (−2.1–2.0 for Scafoglieri equation and −1.6–2.4 for Yoshida equation). Conclusion: In AN patients with BMI < 16 kg/m², no BIA equation tested was adapted to evaluate BC at the individual level.

Development and Validation of Bioelectrical Impedance Analysis Equations in Adolescents with Severe Obesity

Body mass index measures excess weight for size, and does not differentiate between fat mass (FM) and fat-free mass (FFM). Bioelectrical impedance analysis (BIA) is most commonly used to assess FM and FFM as it is simple and inexpensive. Variables from BIA measurements are used in predictive equations to estimate FM and FFM. To date, these equations have not been validated for use in adolescents with severe obesity. In a cohort of adolescents with severe obesity (SO), a BMI≥ 120% of the 95th percentile, this study aimed to 1) derive a BIA predictive equation data from air displacement plethysmography (ADP) measurements; 2) reassess the equation in a second validation cohort; and 3) compare the accuracy of existing body composition equations. Adolescents with SO were assessed using ADP and BIA. FM values derived from ADP measurements from the first cohort (n=27) were used to develop a BIA predictive equation (i.e., Hamilton). A second cohort (n=65) was used to cross-validate the new and 9 existing BIA predictive equations. Ninety-two adolescents (15.8±1.9 y; BMI: 46.1±9.9 kg/m2) participated. Compared with measured FFM using ADP: 1) the Lazzer, Hamilton, Gray, and Kyle equations were without significant bias; 2) the Hamilton and Gray equations had the smallest absolute and relative differences; 3) the Kyle and Gray equations showed the strongest correlation; 4) the Hamilton equation most accurately predicted FFM within±5% of measured FFM; and 5) 8 out of 9 equations had similar root mean squared prediction error values (6.03-6.64 kg). The Hamilton BIA equation developed in this study best predicted body composition values for groups of adolescents with severe obesity in a validation cohort.

Bioelectrical impedance analysis, hydrometry and hydrodensitometry for body composition assessment in adult Colombian women

Bioelectrical impedance analysis (BIA) is a noninvasive method for assessment of body composition with better accuracy, and less inter-observer error than anthropometry. Despite these advantages, reported BIA equations may not be suitable for populations other than where they were developed. The aim of this study was to develop equations for single-frequency (SF) BIA and bioelectrical impedance spectroscopy (BIS) using a three compartment (3C) model as the criterion method to predict total body water (TBW) and fat-free mass (FFM) in South-American adult women. A cross-sectional observational study was conducted using a convenience sample (n=40). SF-BIA and BIS prediction equations were developed using forward-stepwise multiple regression with TBW by D2O and FFM by 3C model as dependent variables; and weight, resistance and impedance index (stature2/resistance) as independent variables. A cross-validation was conducted in a randomly split subsample (n=20). Four final equations were developed. There were no differences between SF-BIA and BIS equations for TBW (p=0.68) and FFM (p=0.66). The cross-validation showed a strong association and broadly meets the limits of agreement with the 3C reference method. The equations have an excellent goodness-of-fit to predict TBW and FFM. Validation of these equations in populations of different ages and ethnicities is warranted.

Criterion validity of assessment methods to estimate body composition in children with cerebral palsy: A systematic review

Poor nutritional status is a problem in a high number of children with cerebral palsy (CP) and impairs their well-being. Therefore, periodic assessment of nutritional status and especially body composition is essential. However, we lack consensus on the best method to assess body composition in clinical practice. We aimed to systematically review the available evidence on the criterion validity of equation-based skinfold measurement and bioelectrical impedance analysis (BIA) to estimate body composition in children with CP. In a systematic review (MEDLINE, Cochrane Library and EMBASE), we identified studies that reported on the agreement between the estimation of body composition by equations of skinfold thickness or impedance values of BIA with a gold standard (isotope techniques or dual-energy-X-ray-absorptiometry [DXA]) in children with CP. We included only studies that provided correlations or agreement between estimations of body compartments (e.g., percentage body fat [%BF] or fat mass). Limits of agreement of 2.5%BF points were considered acceptable. Study quality was assessed by using the Quality Assessment of Diagnostic Accuracy Studies 2. We included reports of 9 studies describing 3 skinfold equations and 4 equations to estimate body composition with BIA. Neither skinfold equations nor BIA could be reliably used to assess body composition in an individual child with CP at one point in time. On a population level, the Gurka skinfold equation was valid in ambulant children with CP, and the Kushner and Fjeld BIA equations were valid in a heterogeneous group of children with CP. Conclusions The future role of skinfold equations and BIA to assess and monitor body composition in an individual child with CP needs to be further investigated.

Assessment of fat-free mass from bioelectrical impedance analysis in men and women with Prader-Willi syndrome: cross-sectional study

We have recently shown that population-specific formulae are required to estimate fat-free mass (FFM) from bioelectrical impedance analysis (BIA) in obese women with Prader-Willi syndrome (PWS) matched by age and percent fat mass (FM) to non-PWS women. The present cross-sectional study was aimed at developing generalised BIA equations that could be used in PWS subjects independently of sex and FM. We used dual-energy X-ray absorptiometry to measure FFM and BIA to measure whole-body impedance at 50 kHz (Z50) in 34 women and 21 men with PWS. The impedance index, that is, height (cm)2/Z50 (Ω), explained 77% (BCa-bootstrapped 95% CI 65 to 85%) of the variance of FFM with a root mean squared error of the estimate of 3.7 kg (BCa-bootstrapped 95% CI 3.2 to 4.5 kg). BIA can be used to estimate FFM in obese and non-obese PWS men and women by means of population-specific equations.

Fat-free mass in adolescent athletes: Accuracy of bioimpedance equations and identification of new predictive equations

The aims of this study were to evaluate the effect of biological maturity on body composition in Brazilian adolescent athletes, to verify the accuracy of previous bioimpedance predictive equations for estimating fat-free mass (FFM), and to develop new predictive equations, considering sexual and skeletal maturity. There were 318 Brazilian adolescent athletes (52% male) involved in this study. FFM was determined using single-frequency (50 kHz) bioelectrical impedance analysis (BIA) and dual-energy x-ray absorptiometry (DXA), which was used as the reference method. The adolescents were classified into skeletally mature using bone age (both sexes), and sexually mature using menarche occurrence (female). The effect of maturity on bioelectrical values was tested using bioelectrical impedance vector analysis. Three predictive BIA equations to estimate FFM were selected from the reviewed literature. Lin's concordance correlation coefficient and Bland-Altman test were used to test the concordance and accuracy of BIA equations. Stepwise multiple regression was used to develop new predictive equations, considering BIA vectors, age, skeletal, and sexual maturity. DXA and BIA results showed wide limits of disagreement for FFM for all the three equations. Two new equation models were developed, including age and skeletal maturity for both sexes and menarche status for females. Both models showed high R2 (males = 0.92 and females = 0.84). The assessment of body composition in adolescent athletes should consider sexual (female) or skeletal (male) maturity. The newly proposed equations showed promising results in Brazilian adolescent athletes. A test in different groups and populations is necessary to evaluate the general suitability of the equations in adolescents.

Bioelectrical impedance analysis in the assessment of sarcopenia.

Bioelectrical impedance analysis (BIA) is an accepted technique to estimate low muscle mass for sarcopenia diagnosis. However, muscularity assessment from BIA relies on prediction equations, estimating different compartments according to the calibration method. Low muscle mass can be defined using different approaches. There is a lack of standardization on how low muscularity is defined in the context of sarcopenia. Recent studies have shown discrepant results for the estimation of low muscle mass when different prediction equations are used in the same BIA device. Different sarcopenia prevalence rates are observed if different definitions are used to identify low muscle mass. Most of the studies using BIA for diagnosing sarcopenia use the incorrect combination of specific population cut-off or a different device from the original equation. The lack of standardization of BIA use for assessing muscularity results in a wide range of sarcopenia prevalence rates among studies, even when conducted in the same population. As BIA equations and cut-off values are population and device-specific, results should be interpreted with caution when data from different devices are applied in equations or using cut-off values from a different population.