Article Navigation. J BIA skeletal muscle assessment Assexsment ; nuscle BIA skeletal muscle assessment — Med Mental alertness games Sports Exerc — Of patients with HCV, 34 patients achieved a sustained viral response with antiviral therapy. Results: The numbers of patients with low SMI using BIA or CT were 53

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The external validation and bias analysis can provide alternatives and close the gap between equation development and implementation of equations, in this case, for estimation at the group level Moreover, the influence of the DXA model used as reference method, is a factor which has not been explored in external validation studies.

Currently, the most widely used models are DXA Lunar and DXA Hologic, of which significant differences in body composition measurements have been reported between both models 31 , Considering this evidence, it is possible that the ASM estimated by an equation may not be entirely comparable or equivalent when compared to ASM measurements with a different DXA model than the one used for the generation of the equation.

This could lead to bias in external validation studies. For all of the above, the objective of this study was to assess the agreement between six equations based on BIA and dual-energy X-ray absorptiometry to estimate ASM in non-Caucasian older adults, considering the DXA model.

The bias was also analyzed in order to propose correction factors. This is a secondary analysis generated from various studies with a cross-sectional design 33 — 35 and the baseline data of one randomized clinical trial 36 carried out in the Body Composition Laboratory of the Food and Development Research Center, CIAD, A.

This analysis included a large sample of older men and women from Hermosillo, Sonora, México. The methodology has already been described previously in the mentioned studies, but a brief description is provided.

Independently of the cited studies, all participating subjects were adults over 60 years of age or older, who were invited to participate through flyers, telephone calls and home visits. The corresponding study protocol was explained to them, as well as the procedures to which they would undergo.

All volunteers underwent body composition measurement by different methodologies including DXA and BIA. Likewise, various questionnaires and scales were applied to determine the health status, including functionality and cognition.

All the subjects were free of physical disability according to the Lawton and Brody scale 38 or the Barthel Index 39 , and the majority were free of cognitive impairment according to the Pfeiffer Scale 40 or the Mini Mental State Examination Also, information on demographic and socioeconomic conditions was collected.

All these procedures were conducted at CIAD, A. From the cited studies, a primary database was built. All the volunteers selected for this study, had to have a physical file, which had to contain complete information on age, sex, waist circumference, resistance and reactance variables, and DXA scans.

They had to be free of diseases, conditions or medications that could affect body composition or hydration status. Regardless of their BMI, men and women older than 60 years were included.

All those subjects who did not have complete data on the variables necessary for this external validation protocol, and those who had atypical data or outliers detected by the exploratory analysis were excluded.

The identification of outlier variables was carried out through the visual identification of variables that were separated from the set of points of the scatter plot.

Height was measured in the same condition, placing the subject's head according to the Frankfurt plane and using a digital stadiometer SECA stadiometer , Hamburg, Germany. Afterwards, the BMI was calculated. Waist circumference was measured just above the superior border of the iliac crest. The measurement was made with the subject standing and using a fiberglass measuring tape Lafayette Instruments Company Inc.

Body composition in some of the cited studies was assessed using DXA Lunar Radiation Corp; Madison, WI, USA or DXA Hologic Discovery WI QDR Series; Waltham, USA.

It is important to point out that it has been reported that, quantitatively, these two models do not measure the exact same amount of body composition components such as ASM, or compartments such as fat mass.

Regarding the appendicular lean mass ALM , Shepherd et al. For this study, we analyzed the ASM measurements in a subsample of 70 older adults, who had been measured with both DXA models. DXA measurements were performed in the same day, following the same protocol for the whole scan and scan editing for ASM determination.

A paired t -test was used to determine if the mean difference of the measurements between both methods was different from zero. Protocol of DXA measurement, DXA scan edition for ASM and calibration were performed according to a published study Participants were measured wearing a disposable gown and free of plastic or metal objects.

The ASM determined by DXA ASM DXA was considered as reference. For those that did not fit in the DXA scan area, half-body scans were performed, and the remaining side was duplicated as described by Rothney et al. In the case of two subjects who wore non-removable metal accessories, the opposite half of the body to where they had the accessory, was duplicated.

For the purposes of this secondary analysis, resistance R and reactance Xc were measured by a RJL Systems single frequency bioimpedance 50 kHz , Detroit, Mich, USA, which complied with a daily calibration protocol with a resistance of ohms.

BIA measurements were according to the methodology published previously 24 , Both the DXA and BIA measurements were performed after an 8 h fast, with an empty bladder and without having consumed food or liquid prior to the measurement. English-language articles on topics of BIA equations or predictive models to estimate ASM published between and were identified in the PubMed database.

These cut-off points were considered since R 2 is expected to be as close to 1 and SEE as close to zero. An equation with these values can assure considerable precision. Only BIA equations generated including older adults aged 60 to 90 years, non-institutionalized of any nationality or ethnic group, and with DXA as the reference method were included.

Age, sex, and body weight are both clinically and statistically associated with ASM, and together with BIA variables, the predictive model can yield more precise and accurate results of the ASM. Finally, the selected equations must have been generated with a single or multi-frequency bioimpedance model.

Also, there was no discrimination regarding the method of generation and validation of the equations, nor the nutritional status of the subjects that integrated the generation or validation sample. The data was analyzed using STATA version 16 StataCorp LP, TX, USA.

An exploratory analysis of the primary data was carried out to observe the behavior of the data and detect atypical data or outliers. The significance of the differences between men and women was determined using an independent sample t -test and the results are presented as mean ± standard deviation.

To test if the differences between the ASM measured by DXA Lunar and Hologic were different from zero, a paired t -test was used in the sample of 70 adults. Regarding the validation procedure, the agreement between methods was evaluated using the Bland and Altman procedure, which considers that the average of the two methods is the best estimator.

Objectively agreement was tested by a paired t test and by simple linear regression analysis. The paired t -test assessed if the mean differences between the estimation of each equation and the ASM measurement by DXA were statistically different from zero, and the simple linear regression analysis, which assessed the homogeneity of the dependent variable.

To visually analyze the mean of the differences and the distribution of the differences between methods, Bland and Altman 46 plots were incorporated. Additionally, the simple regression analysis must test that the differences are randomly distributed.

This would prove the homogeneity of the bias, that is, the homogeneous distribution of the differences along the spectrum of the mean of ASM between methods. If these two conditions were met, agreement was accomplished, meaning that the BIA equation can be considered as an interchangeable method to DXA to assess ASM in this large sample of non-Caucasian older adults.

This methodology to establish agreement has been described and applied in other validation studies 33 , This bias analysis supports or rejects the possibility of deriving a CF.

In order to propose one, the bias distribution must be homogeneous, and the mean of the differences must be different from zero. If so, the equation can be corrected by subtracting or adding the mean difference to the respective equation.

This CF does not change the behavior of the variables included in the equation, but it makes it possible to reduce the average of the differences bias in the estimates at group level.

This correction has been proposed in other studies 33 , 48 , and has provided the opportunity to improve the estimates according to the equations where applicable.

The initial sample made up of all the subjects participating in the previously mentioned studies was of participants. Ninety-five volunteers were excluded due to lack of BIA data. The sample consists of women Some of them reported a previous diagnosis of hypertension, controlled type 2 diabetes, and dyslipidemia, with their respective pharmacological control.

Other diseases reported were colitis, gastritis, bronchitis, rheumatoid arthritis, bronchial asthma, or controlled hypothyroidism, with stable weight according to self-report. The mean value of BMI was According to their BMI classification, 6 subjects were underweight 1.

The mean value of ASM in the whole sample was of According to the DXA model, the mean ASM measured by DXA Hologic was The general characteristics of both samples are found in Table 1. Regarding the BIA equations to estimate the ASM, a total of 25 equations were found, of which 10 were generated in older adults.

Of these, only 5 had reported an internal validation process, and 6 have been externally validated in other studies. Only 6 equations which met the selection criteria were selected: Kim's, Kyle's, Rangel-Peniche's, Sergi's, Toselli's and Yoshida's equations.

The characteristics of these equations are shown in Table 2. These equations were applied to the complete sample, and with this, the variables ASM Kim , ASM Kyle , ASM Rangel , ASM Sergi , ASM Toselli and ASM Yoshida were obtained.

Importantly, Kim's and Toselli's equations generated with DXA Lunar, were tested on subjects measured with DXA Lunar, while BIA equations generated using DXA Hologic as the reference method, were tested on those measured with that model.

This, in order to eliminate the effect or possible bias due to DXA model in this validation procedure. The results of the paired t -test between the measurements by both DXA models in the subsample of 70 subjects, showed a mean difference different from zero These differences between DXA models support the decision to validate the equations according to the DXA model taken as reference, since the measurements between both models are not interchangeable.

The mean value of ASM estimated by the Kim's and Toselli's equations in the sample of subjects measured by DXA Lunar was Regarding the Kyle, Rangel-Peniche, Sergi and Yoshida equations, the mean value of ASM was Clearly, these results indicate that 2 equations underestimated ASM DXA , while 4 overestimated it Table 3.

Figure 1. Bland and Altman plots of the equations generated using DXA Lunar. Behavior of the mean difference against the mean of the measurements between the equations of Kim et al. Solid red lines indicate the mean difference. Solid blue lines indicate limits of agreement.

Solid black lines indicate the regression line. Dotted line indicates zero. ASM, appendicular skeletal muscle mass; MD, mean of the differences. A Kim et al. B Toselli et al. Figure 2. Bland and Altman plots of the equations generated using DXA Hologic. Behavior of the mean difference against the mean of the measurements between the equations of Kyle et al.

A Kyle et al. B Rangel-Peniche et al. C Sergi et al. D Yoshida et al. This indicates that these equations do not significantly underestimate or overestimate as ASM increases.

Having a homogeneous bias allows us to suggest a correction factor, which could correct the significant differences found in the paired t tests in these three equations. Table 4. Comparison of the mean values of the estimated ASM and the ASM DXA. This wasn't possible for Kim's, Sergi's and Yoshida's equations.

In these cases, the overestimation or underestimation of these equations as the ASM increases is significant, so they cannot be corrected.

Considering the finding of homogeneous bias, correction factors were proposed by considering the mean difference between DXA and both equations. The bias of each one of the equations was subtracted or added as following:. ASM ToselliCF , corrected Toselli's equation.

ASM KyleCF , corrected Kyle's equation. ASM RangelCF , corrected Rangel-Peniche's equation. WC, waist circumference in cm.

Weight in kilograms. Sex: 0 for women and 1 for men. Age in years. The mean value of ASM estimated by the corrected Toselli's equation Toselli CF in the sample of subjects measured by DXA Lunar was On the other hand, the mean value of ASM estimated by the corrected Kyle's equation Kyle CF and the corrected Rangel-Peniche's equation Rangel CF in the sample of subjects measured by DXA Hologic was When these three corrected BIA equations were compared with their respective reference method, the mean differences were less than 0.

By carrying out the same tests applied previously paired t test and simple linear regression , and considering the criteria to determine agreement, it was possible to achieve agreement between the three corrected BIA equations and the ASM DXA.

This analysis gave us three corrected equations with a bias very close to zero, which is not statistically significant, and which maintained a homogeneous bias in the estimation.

Table 5. Figure 3. Bland and Altman plots and simple linear regression of the selected equations applying the correction factors.

Behavior of the mean difference against the mean of the measurements between the corrected equations and their respective reference method.

Solid red line indicates the mean difference. Solid blue line indicates the limits of agreement. Solid black line indicates the regression line. ASM, appendicular skeletal muscle mass.

MD, mean of the differences. A Corrected Toselli's equation Toselli CF. B Corrected Kyle's equation Kyle CF. C Corrected Rangel-Peniche's equation Rangel CF.

The purpose of this study was to validate some published BIA equations for estimating ASM. None of these BIA equations met the criteria for agreement in this sample. However, the analysis of bias permitted to derive CFs, which, when applied to some equations, showed agreement with DXA.

A valid corrected equation for this group of older adults can be a useful tool for epidemiological studies. To the best of our knowledge, in Mexico, low muscle mass has only been assessed at the national level using calf circumference From our perspective, estimating it with accurate and practical tools, such as BIA equations could guarantee a better estimate of skeletal muscle, particularly ASM.

All the BIA equations selected for this study have already been tested in other populations previously, where they were discarded for its inaccuracy in certain populations due to the difference in age ranges 11 , 12 , 21 , nutritional status 20 , 50 , differences in body composition and anthropometry measurements related to ethnicity 18 , health status 19 , differences in functional status 14 , or BIA device employed For example, in other external validation studies 18 , 20 , Kim's equation was found to have the highest mean difference compared to DXA Lunar ASM estimations.

In these studies, authors discuss that it is most likely due to the fact that it was developed for an Asian population, but also because the authors used a multifrequency bioimpedance device, operating at a single frequency of Hz. It is already well recognized, that low frequencies predominantly measure extracellular water.

At higher frequencies, in contrast, cell membranes are permeable to current, so both intracellular and extracellular water are measured In this way, it is understood that multifrequency devices measure body composition in a slightly different way.

In our study, the Kim equation yielded the highest mean difference of all Both equations were generated in older Asian adults and using multi-frequency BIA devices, thus, we hypothesize that these two characteristics may have been an important factor contributing to bias in this sample as well.

Sergi's equation was generated in Caucasian subjects, and it only included older adults for its generation process. Even though their generation sample has very similar characteristics to ours, the equation had a very high bias, and like the others, the mean of the differences was significant.

It is important to remember that several studies have described the differences in body composition between different ethnic groups 42 , 52 , 53 , which could also have contributed to the bias of this equation as well. This did not allow a correction factor to be proposed for these models. Kyle's equation was developed for Swiss adults in the age range of 22 to 94 years.

Many studies have tried to validate it in external validation protocols. In almost all validation studies 11 , 12 , 14 , 16 , 18 , 19 , 21 — 23 , 50 , 54 , the equation has overestimated the ASM in different conditions, which the authors consider is due to the fact that it is not specific for a particular age group.

Therefore, this equation is usually discarded for use in certain populations. In our study, this equation overestimated 1. Toselli and Rangel-Peniche equations were the ones with the mean of the differences closest to zero In the case of the Rangel-Peniche equation, this must be since it was developed in a group of individuals of the same nationality as our sample.

Despite this, this equation does not meet the established criteria for agreement in this sample of older adults from the northwest of the same country. This confirms the nature of the equations to be specific for the population where it was generated and very similar populations.

In fact, another study by Rangel-Peniche et al. After adjusting for age, body weight, height, health status, estimated energy expenditure, and some demographic variables, ASM and the appendicular muscle mass index in older adults from central Mexico were significantly higher compared to the older adults from the northwest of Mexico.

This could be one reason why Rangel-Peniche's equation was not valid for our sample. In other studies, such as the one by Yu et al. In another study 19 , it overestimated approximately 0. In the study by Coëffier et al. Due to these values, these studies have decided to rule out the use of this equation.

On the other hand, this is the first study to externally validate Toselli's equation. This model, which includes waist circumference among the predictor variables, turned out to have a very low bias in our sample In their study, the authors discuss the relationship between waist circumference and ASM.

We believe that having taken this variable into account in this model and applying it to a sample with a high mean waist circumference, could be the reason why it had the smallest mean difference.

According to our results, none of the selected equations was valid for older adults from the northwest of Mexico. However, an important finding achieved when analyzing the bias of the equations, is that we realized that the Toselli, Rangel-Peniche and Kyle equations had a homogeneous bias.

This allowed them to be further improved to yield accurate data in this sample of older Mexican adults. By deriving a correction factor for Toselli's, Kyle's and Rangel-Peniche's equations, precise, accurate, and bias-free ASM estimates were obtained.

Importantly, this was possible after the analysis of the bias in this external validation study. This turned out to be a very useful strategy to use the existing equations in the literature, and thus not contribute to the development of more equations, which would have been generated unjustifiably and that, as mentioned in the systematic review by Beaudart et al.

This study has several advantages: to our knowledge, it is the first study to propose correction factors for BIA equations to estimate ASM, derived from a validation study with a large sample that included subjects of a wide nutritional range, age range, physically independent and without uncontrolled diseases that affected body composition.

Likewise, it is the first study that considers the DXA model in the validation process. Many external validation studies have treated the DXA model indistinctly, despite the differences that are already recognized in the literature 31 , 32 , 55 — In this study, in addition to considering these differences, we tested if the measurements taken by both DXA models were different in a subsample of subjects.

Once confirmed, we chose to separate the validation according to the DXA model: the equations generated with a model, were applied only in subjects measured with that same model.

This reduces the influence of the DXA model in the validation process, which could have been an important contributing bias factor.

Another advantage is that this validation confirms that single frequency bioimpedance devices are a valid tool for ASM estimation compared to DXA. These models are cheaper and more practical compared to others, and they can be a portable alternative for epidemiological studies.

A final advantage that we find are the criteria established in this article to determine agreement between methods. When assessing other validation studies, we noticed that some of them only carry out paired t -tests between methods, some use the pure error, or the Pearson or Lin coefficient.

Some others are satisfied with only determining which was the lowest mean error of the selected equations. We also notice that most studies do not analyze the bias distribution. We opted for the criteria mentioned in the Materials and Methods section, because, by adding paired t -tests and simple linear regression to the statistical methods, we address more than what is included in the Bland and Altman plot, testing agreement not only subjectively, but also objectively.

These steps should be fundamental in validating equations. One disadvantage of this study is that, due to its nature, the CF may not be generalizable to other populations. Likewise, this CF could be more viable for overweight and obese subjects, since approximately A very little percentage of our subjects is made up of low-weight subjects, so it could be less valid for this group of individuals.

Another disadvantage is that, despite that this study has a larger sample compared to others published, our sample is not representative or randomized, so our results are only valid in this sample, and we hypothesize that it may be valid in subjects with similar characteristics.

Moreover, it is important to mention that in this study, agreement was proven statistically, and this is not synonymous with clinical significance. It is notorious that, when applying these CFs, there are no changes or improvements in the amplitude of the limits of agreement of the estimations, and it only allows the reduction of the mean difference.

Given this, the corrected equations by this CFs are only useful for estimating mean values of ASM on populations and are not valid if applied at the individual level, since the estimates exceed clinically significant physiological values. Because of this, first, we recommend exploring through regression models, which are the variables associated with bias in each of the equations, to obtain a broader picture of the main contributing bias factors.

Subsequently, knowing the variables associated with the bias in these equations, we recommend generating more complex correction equations, to obtain values closer to the real ones at the individual level. We also recommend validating these CFs on an independent sample, as long as the DXA model used as the reference method is considered.

Furthermore, we consider that clinically acceptable limits of accuracy need to be defined when estimating the ASM. None of the published BIA equations met the criteria to achieve agreement with DXA. However, the bias analysis done after stratifying by DXA model, was determinant to derive and apply correction factors to Toselli's generated with DXA Lunar , Kyle's and Rangel-Peniche's equations generated with DXA Hologic.

Incorporating the correction factors to the corresponding BIA equations showed an extremely low bias. Therefore, these three corrected BIA equations could be used to estimate the mean values of ASM at group level in older adults from the northwest of Mexico.

Requests to access these datasets should be directed to HA-M, helio ciad. Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. HA-M: design. MC-R and HA-M: drafting of manuscript.

MC-R, HA-M, JE-R, and RG-A: data analysis. HA-M, RG-A, MR-T, RU-R, DR-P, and GF-P: recruitment and data collection. All authors contributed to the article and approved the submitted version. We would like to thank the National and International Agencies that provided funding for each of the projects included in this secondary analysis.

We are also grateful to the studies' participants, the students involved in each project and the personnel of the Coordination of Nutrition, CIAD, A. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers.

Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Frontera WR, Ochala J. Skeletal muscle: a brief review of structure and function. Calcif Tissue Int. doi: PubMed Abstract CrossRef Full Text Google Scholar.

As Muraki indicates, Computed Tomography CT and Magnetic Resonance Imaging MRI are highly accurate but difficult to implement in clinical and community settings. Conversely, Dual-energy X-ray Absorptiometry DXA and Bioelectrical Impedance Analysis BIA are easier to implement but have accuracy concerns.

D3-creatine dilution and echocardiography are promising in terms of applicability and accuracy, but there is insufficient evidence regarding cutoff values; however, future development is expected. Furthermore, when estimating skeletal muscle mass with DXA and BIA, correction is made for height squared, weight, and Body Mass Index BMI , but there is no consensus on which is the most appropriate correction method, although height squared correction has been commonly used.

For example, in the case of obesity, correction for height squared tends to overestimate skeletal muscle mass; thus, correction for body weight or BMI is preferable.

Although we have demonstrated the utility of skeletal muscle measurement in the longitudinal cohort and clinical studies 2 , 3 , 4 , Sarcopenia Definitions and Outcomes Consortium SDOC has determined that skeletal muscle measurement is not necessary for the diagnosis of sarcopenia 5.

Currently, the Global Leadership Initiative on Sarcopenia GLIS group has been formed to globally discuss the definition and diagnostic criteria for sarcopenia. Liang-Kung Chen, Jean Woo, and Hidenori Arai are participating in this group from Asia, and further discussions are expected.

Muraki I. Muscle mass assessment in sarcopenia: a narrative review. View Article. Otsuka R, Matsui Y, Tange C, et al. What is the best adjustment of appendicular lean mass for predicting mortality or disability among Japanese community dwellers?

BMC Geriatr. View Article PubMed. Kinoshita K, Satake S, Matsui Y, et al. Association between sarcopenia and fall risk according to the muscle mass adjustment method in Japanese older outpatients.

Nahoko AssessmemtDiabetes and hormone imbalance UojimaHisashi Hidaka assessmsnt, Shuichiro SkeketalNaohisa Wada BIA skeletal muscle assessment, Kousuke Kubota BIA skeletal muscle assessment, Takahide NakazawaAkitaka Shibuya Dispelling popular nutrition myths, Makoto KakoSoeletal TakeYoshihiko SakaguchiTeruko SatoChika Skeldtal Evaluation of Skeletal S,eletal Mass in Patients with Chronic Liver Disease Shows Different Results Turmeric for hair growth on Bioelectric Impedance Analysis and Computed Tomography. Ann Nutr Metab Turmeric for hair growth December ; 78 6 : — Objective: We aimed to evaluate the difference between computed tomography CT -based and bioelectrical impedance analysis BIA -based assessment of sarcopenia in patients with chronic liver disease CLD. Methods: We enrolled a total of patients who were evaluated with or without sarcopenia. Sarcopenia was defined as a low skeletal muscle mass index SMI with low muscular strength by the Japan Society of Hepatology. To evaluate whether or not the different methods influence the diagnosis of sarcopenia for patients with CLD, we assessed the number and characteristics of mismatches between the low SMI using BIA and CT. We also compared the overall survival OS in patients with and without sarcopenia based on CT and BIA to evaluate the appropriate methods. Thank you for visiting nature. Sleletal are using sekletal BIA skeletal muscle assessment version musfle Turmeric for hair growth support for CSS. To obtain the best experience, we recommend you use a more Diabetes prevention for children to date browser BIIA turn off Turmeric for hair growth mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Bioelectrical impedance analysis BIA provides noninvasive measures of skeletal muscle mass SMM and visceral adipose tissue VAT. This study i analyzes the impact of conventional wrist-ankle vs segmental technology and standing vs supine position on BIA equations and ii compares BIA validation against magnetic resonance imaging MRI and dual X-ray absorptiometry DXA.

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