Low assessmemt mass in older adults Herbal Tea Blends mortality: a systematic review and BIA skeletal muscle assessment. Skeletzl day prior to the tests, proper repeatability of impedance measurement results was checked through two successive tests carried out on two volunteers. Already have an account?

BIA skeletal muscle assessment -

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. J Nutr Health Aging. Quantifying muscle mass by adjusting for body mass index is the best for discriminating low strength and function in Japanese older outpatients.

Bhasin S, Travison TG, Manini TM, et al. CLD was subdivided into chronic hepatitis, compensated cirrhosis, and decompensated cirrhosis. The causes of CLD included hepatic virus infection, alcoholic liver disease, and nonalcoholic fatty liver disease NAFLD.

However, the progression of liver fibrosis leads to the disappearance of characteristic findings such as steatosis and Mallory bodies. Sarcopenia was diagnosed based on the criteria for patients with CLD, recommended by a working group from JSH [ 10 ].

Their consensus definition was based on low SMI with low muscular strength. Low muscular strength and low SMI were defined as follows. Muscular strength was measured using a handgrip dynamometer Digital Hand Grip Dynamometer, A; Takei Scientific Instruments Co.

SMI was measured as the skeletal muscle mass using CT and BIA. The skeletal muscles were composed of the erector spinae, external oblique, internal oblique, psoas major, rectus abdominis, quadratus lumborum, and the transversus abdominis.

Cross-sectional CT images at L3 were made using the Slice-O-Matic software v5. SMI based on the BIA was evaluated as the muscle mass of all 4 limbs using BIA by dividing the square of the height.

Segmental body composition was measured using InBody S10 InBody Japan Inc. The measurement procedure of Multi-Frequency Body Composition Analyzer MC required the subject to stand in bare feet on the analyzer and to hold a pair of handgrips.

Both are medical machines that use eight electrodes and have a multi-frequency measurement principle and measure the trunk, right arm, left arm, right leg, and left leg separately.

To evaluate whether or not the different methods influence the diagnosis of sarcopenia for the patients with CLD, we assessed the numbers and characteristics of mismatches between the low SMI using BIA and that using CT based on the JSH criteria.

We also compared the overall survival OS in patients with and without sarcopenia based on CT and BIA to evaluate the appropriate methods for patients with CLD. OS was defined as the number of days from the evaluation of sarcopenia to either death or September 10, , whichever came first.

All enrolled participants were assessed for base characteristics: physical assessment, prescription drugs, heavy episodic drinking, presence of HCC, nutritional status, degree of fibrosis progression, and laboratory data. The diagnosis of HCC was based on the images before the sarcopenia evaluation.

The laboratory data evaluated the white blood cell count, hemoglobin level, platelet count, creatinine level, blood urea nitrogen level, aspartate aminotransferase level, alanine aminotransferase level, total bilirubin, serum albumin, ammonia, alpha-fetoprotein, prothrombin time, and glycated hemoglobin A1c.

Statistical analyses were analyzed using SPSS software version A multivariate logistic regression analysis was performed to calculate the hazard rate for the variables entered, which were significantly associated with the characteristics of mismatches between low SMI using BIA and CT by univariate analysis.

Statistical analyses were reviewed by the Statista Corporation, Kyoto, Japan. The mean age was The number and frequency of men were and Of patients with HCV, 34 patients achieved a sustained viral response with antiviral therapy. The numbers of chronic hepatitis and compensated and decompensated cirrhosis were 50 There were 25 9.

The mean hand grip was The number and population of low muscular strength was The mean SMI using BIA and CT was 7. The phase angle PhA using BIA was 5.

The numbers of patients with low SMI using BIA and CT were 52 As a result, the number and frequency of patients with sarcopenia using BIA and CT were 28 Correlation between sarcopenia based on BIA and CT.

a Diagnosis of sarcopenia based on BIA analysis. The SMI criteria for sarcopenia diagnosis is less than 7. b Diagnosis of sarcopenia based on CT analysis. Frequency of sarcopenia in progression of liver fibrosis. a The diagnosis of sarcopenia was based on the analysis using BIA.

b The diagnosis of sarcopenia was based on the analysis using CT. Diagnosis by BIA showed no difference in the frequency of sarcopenia among hepatitis, compensatory cirrhosis, and decompensated cirrhosis a.

However, there were differences in CT diagnosis, and the proportion of sarcopenia was higher than in BIA diagnosis at any stage b. Univariate analysis revealed that low hand grip strength, body weight, hepatic ascites, albumin, and hemoglobin were independent factors correlated with mismatches between BIA- and CT-based assessment of sarcopenia Table 2.

Multivariate analysis revealed that hepatic ascites and body weight were independent factors of mismatches between SMI using BIA and CT hazard ratio 3. To evaluate quality for the measurement of muscle mass using BIA, PhA was analyzed in patient with and without sarcopenia based on BIA. For man, PhA in the sarcopenia and nonsarcopenia groups was 4.

For woman, PhA in the sarcopenia and nonsarcopenia groups was 4. The frequency of deaths in patients with sarcopenia and nonsarcopenia based on CT analysis was In contrast, the frequency of deaths of patients with and that of those without sarcopenia based on the analysis using BIA was OS based on the sarcopenia using BIA and CT.

a The median OS in patients with the sarcopenia and nonsarcopenia was of no significant difference using BIA. b The median OS in patients with the sarcopenia and nonsarcopenia using CT was 1, and 1, days, respectively; those with sarcopenia were significantly lower than nonsarcopenia.

To our knowledge, this is the first study to evaluate the difference between BIA- and CT-based assessment of sarcopenia using the JGH Jewish General Hospital criteria in patients with CLD.

Currently, four main techniques have been commonly used to estimate muscle mass: BIA, dual energy X-ray absorptiometry DXA , CT, and magnetic resonance imaging [ 16 ].

Valid, standardized, reliable, accurate, and cost-effective tools are necessary for the identification of muscle mass. BIA is a method of estimating body composition by passing a weak electric current through the body and measuring the resistance value [ 11 ]. This measurement has the convenience of completing the measurement in a few minutes with immediate results.

In daily practice, the BIA method can be used to evaluate muscle mass accurately and timely according to changes in symptoms.

In fact, the PhA using BIA had a tendency to be lower in the sarcopenia group compared with the nonsarcopenia group. The PhA is obtained from the resistance and reactance, which values that indicate the phase shift that occurs when a current is passed through a muscle cell in the BIA measurement method [ 17, 18 ].

It is a value that represents the density of muscles, that is, the quality of muscles [ 19 ]. However, contemporary measurements make it difficult to accurately determine sarcopenia.

BIA is easily affected by the amount of water in the body. Therefore, muscle mass indicated with BIA should be interpreted carefully in patients with severe ascites or edema [ 16 ]. In point of fact, there were numerous mismatched cases in the evaluation of muscle mass indicated using BIA and that using CT in the decompensated phase compared with that in the chronic hepatitis and compensated phases.

As a result, it implies that overestimating muscle mass leads to a decreased number of sarcopenia diagnosis. In the present study, the frequency of sarcopenia based on the assessment using BIA was relatively low compared with that using CT. Therefore, the diagnosis of sarcopenia in the decompensated phase should be appropriately evaluated by a cross-sectional area of several muscles on CT imaging.

As a diagnostic method, CT imaging plays a critical role in the early detection of sarcopenia [ 20 ]. This criterion could be applicable to various fields to diagnose disease-related sarcopenia. However, muscle mass in patients with edema and ascites may be overestimated, and the frequency of sarcopenia may not be accurately assessed.

If patients with CLD are overestimated by using the BIA method to assess sarcopenia, appropriate treatment, such as nutritional and exercise interventions, may be delayed and may affect prognosis [ 21 ]. It is well known that CLD with disease-related sarcopenia is associated with a poor prognosis.

Therefore, accurate diagnoses are important to improve the overall prognoses in patients with disease-related sarcopenia [ 22 ]. Iwasa et al. SMI, which uses upper and lower limb muscle mass, has been shown to be unsuitable for patients with lower limb edema.

In addition, when using BIA, patients with ascites or edema may want to consider the cut-off value and evaluate sarcopenia only by using the upper arm, which is less susceptible to edema.

Furthermore, the measurement of the skeletal muscle mass by BIA varies depending on the device. Different devices were used, which might add extra variability and lead to misdiagnosis. 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.

Thank you Skelefal visiting nature. You skeketal using a browser version with limited support for CSS. To Glucagon hormone and insulin Anti-bacterial surface cleaners best experience, we recommend you use awsessment more up skeletl date Herbal Tea Blends muscel turn off assessmfnt mode in Internet Explorer. In the meantime, muscld 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. One hundred and thirty-six healthy Caucasian adults 70 men, 66 women; age 40±12 years were measured by a phase-sensitive multifrequency BIA seca medical body composition analyzers and

BIA skeletal muscle assessment -

Med Sci Sports Exerc — Ignasiak Z, Sebastjan A, Kaczorowska A et al Estimation of the risk of the frailty syndrome in the independent-living population of older people.

Aging Clin Exp Res — Jung HW, Kim SW, Lim JY et al Frailty status can predict further lean body mass decline in older adults. J Am Geriatr Soc — Ishii H, Tsutsumimoto K, Doi T et al Effects of comorbid physical frailty and low muscle mass on incident disability in community-dwelling older adults: a month follow-up longitudinal study.

Maturitas — Davies B, García F, Ara I et al Relationship between sarcopenia and frailty in the toledo study of healthy aging: a population based cross-sectional study. Hairi NN, Cumming RG, Naganathan V et al Loss of muscle strength, mass sarcopenia , and quality specific force and its relationship with functional limitation and physical disability: the Concord Health and Ageing in Men Project.

Mckendry J, Breen L, Shad BJ et al Muscle morphology and performance in master athletes: a systematic review and meta-analyses. Ageing Res Rev — Moore AZ, Caturegli G, Metter EJ et al Difference in muscle quality over the adult life span and biological correlates in the Baltimore longitudinal study of aging.

Visser M, Goodpaster BH, Kritchevsky SB et al Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. Kołodziej M, Ignasiak Z Changes in the bioelectrical impedance parameters estimating appendicular skeletal muscle mass in healthy older persons.

Barbosa-Silva MCG, Barros AJD, Wang J et al Bioelectrical impedance analysis: population values for phase angle by age and sex. Am J Clin Nutr — Slee A, Birch D, Stokoe D Bioelectrical impedance vector analysis, phase-angle assessment and relationship with malnutrition risk in a cohort of frail older hospital patients in the United Kingdom.

Nutrition — Mullie L, Obrand A, Bendayan M et al Phase angle as a biomarker for frailty and postoperative mortality: the BICS study.

J Am Heart Assoc 7:e Cesari M, Leeuwenburgh C, Lauretani F et al Frailty syndrome and skeletal muscle: results from the Invecchiare in Chianti study. Williams GR, Deal AM, Muss HB et al Frailty and skeletal muscle in older adults with cancer. J Geriatr Oncol — Download references.

The authors thank the study participants for their effort, devoted time and collaboration during the study.

This study was part of a project No. N funded by the Ministry of Science and Higher Education and did not receive any other grants from funding agencies, commercial firms or not-for-profit sectors.

Department of Biostructure, University School of Physical Education in Wroclaw, Al. Paderewskiego 35, , Wroclaw, Poland. You can also search for this author in PubMed Google Scholar.

MK conceptualized and designed the study, carried out all experiments, collected and interpreted the data, performed the statistical analysis, conceived of the study, and drafted the manuscript, had primary responsibility for the final content; AS conceptualized and designed the study, carried out all experiments, revised and edited the manuscript; ZI conceptualized and designed the study, was responsible for project, revised and edited the manuscript.

All authors have read and approved the final version of the manuscript, and agree with the order of presentation of authors. Correspondence to Anna Sebastjan. The research protocol was approved 18 February by the Senate Research Ethics Committee of the University School of Physical Education in Wroclaw and was consistent with institutional ethical requirements for human experimentation under the Declaration of Helsinki.

The participants were informed about the aim and methods of the research, the procedures used and the experimental risk. All participants provided written informed consent before entering the study. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.

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Reprints and permissions. Kołodziej, M. Appendicular skeletal muscle mass and quality estimated by bioelectrical impedance analysis in the assessment of frailty syndrome risk in older individuals.

Aging Clin Exp Res 34 , — Download citation. Received : 03 January Accepted : 03 May Published : 12 June Issue Date : September Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative. Download PDF. Abstract Background and aim The rising aging index of many populations necessitates the continuous evolution of geriatric assessment methods, especially the ones used to identify frailty and the risk of frailty.

Methods One-thousand-and-fifteen subjectively healthy persons aged 60—87 years were tested. Conclusion The strong correlation between the frailty phenotype and appendicular skeletal muscle mass and functional quality suggests that the two variables should be included in routine geriatric assessment with regard to frailty.

Bioelectrical impedance analysis in the BaSAlt cohort-study: the phase angle as an additional parameter for sarcopenia quantification among German nursing home residents? Article Open access 13 April Association between raw bioelectrical impedance parameters and muscle mass and strength measured by DXA and dynamometry in older adults: a pilot study Article 15 April Phase Angle is a Useful indicator for Muscle Function in Older Adults Article 12 December Use our pre-submission checklist Avoid common mistakes on your manuscript.

Materials and methods Study and its participants In the years —, thousand-and-sixteen persons men and women aged 60—87 years Bioelectrical impedance analysis and anthropometric measurements Body height Ht and mass Wt were measured with an accuracy of, respectively, 0.

Results The descriptive characteristics of the subjects and the differences between the non-frail persons and the pre-frail persons are presented in Table 1. Table 1 Descriptive characteristics of study participants Full size table. Full size image. Table 2 Logistic regression models for probability of pre-frailty state in elderly persons Full size table.

Discussion The results of our investigations confirm that the adverse changes accompanying physical frailty can be observed quite early.

Conclusion This study has indicated that the easily available and inexpensive method of BIA can be used to preventively monitor changes not only in the mass of skeletal muscles, but also in their quality, which is particularly important in the case of pre-frail older persons.

References World Health Organization Global strategy and action plan on ageing and health, Geneva. Accessed 29 Mar Cruz-Jentoft AJ, Baeyens JP, Bauer JM et al Sarcopenia: revised European consensus on definition and diagnosis. M Article CAS PubMed Google Scholar Morley JE, Vellas B, van Kan GA et al Frailty consensus: a call to action.

Accessed 29 Mar Fess EE Grip strength. The American Society of Hand Therapists, Chicago, pp 41—45 Google Scholar Charlier R, Mertens E, Lefervre J et al Muscle mass and muscle function over the adult life span: a cross-sectional study in Flemish adults.

Accessed 29 Mar Craig CL, Marshall AL, Sjöström M et al International physical activity questionnaire: country reliability and validity. FB Article PubMed Google Scholar Ignasiak Z, Sebastjan A, Kaczorowska A et al Estimation of the risk of the frailty syndrome in the independent-living population of older people.

x Article PubMed Google Scholar Mckendry J, Breen L, Shad BJ et al Muscle morphology and performance in master athletes: a systematic review and meta-analyses.

Acknowledgements The authors thank the study participants for their effort, devoted time and collaboration during the study. Funding This study was part of a project No. Author information Authors and Affiliations Department of Biostructure, University School of Physical Education in Wroclaw, Al. View author publications.

Ethics declarations Conflict of interest The authors have no conflict of interest to declare. Statement of human and animal rights The research protocol was approved 18 February by the Senate Research Ethics Committee of the University School of Physical Education in Wroclaw and was consistent with institutional ethical requirements for human experimentation under the Declaration of Helsinki.

Ethical approval The study protocol was approved 18 February by the Senate Research Ethics Committee of the University School of Physical Education in Wroclaw. Informed consent The participants were informed about the aim and methods of the study, the procedures used and the experimental risk.

Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4. Annals of Cancer Research and Therapy. Online ISSN : Print ISSN : ISSN-L : Journal home All issues About the journal. Skeletal muscle mass assessment in critically ill patients: method and application.

Kohei Tanaka Department of Rehabilitation Medicine, Osaka Police Hospital [Japan] Sho Katayama Department of Rehabilitation Medicine, Okayama University Hospital [Japan] Kazuki Okura Division of Rehabilitation, Akita University Hospital [Japan] Masatsugu Okamura Department of Rehabilitation, Yokohama City University Hospital [Japan] Keishi Nawata Department of Rehabilitation, University Hospital of Occupational and Environmental Health [Japan] Nobuto Nakanishi Corresponding author Emergency and Critical Care Medicine, Tokushima University Hospital [Japan] Division of Disaster and Emergency Medicine, Department of Surgery Related, Kobe University Graduate School of Medicine [Japan] Ayato Shinohara Department of Rehabilitation, Fujita Health University Hospital [Japan].

Corresponding author. Keywords: Skeletal muscle mass , Intensive care , Ultrasound examination. JOURNAL FREE ACCESS. Published: July 21, Received: June 16, Released on J-STAGE: August 23, Accepted: August 02, Advance online publication: - Revised: -.

Download PDF K Download citation RIS compatible with EndNote, Reference Manager, ProCite, RefWorks. Article overview. References Related articles 0. 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?

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