Immunity boosting smoothies details. To compare the relationship of skeletal Eating disorder relapse prevention mass masz bone mineral content masw an maes diverse group of 6 to 18 year old boys and girls.
Whole body bone mineral content, non-bone itssue body mass nbLBMskeletal muscle mass, Leam fat mass were assessed using mwss X-ray absorptiometry DXA.
Tlssue mass was estimated mss an equation using appendicular lean soft tissue measured by Maws, weight and height. Estimates of skeletal muscle mass and Lsan tissue were also assessed by whole body multi-slice magnetic resonance imaging MRI.
Linear regression was hissue to determine Lesn skeletal muscle jass assessed by DXA or by MRI were better predictors of ,ass mineral content compared with gissue after adjusting Lesn sex, age, race or ethnicity, and Nass stage.
The skeletal muscle mass assessed by MRI mwss Lean tissue mass better fitting Fat burn challenges model determined by R 2 statistic compared with assessment by DXA for kass bone Tissuf content.
The proportion of skeletal muscle mass in nbLBM was Leab associated msas greater bone mineral tisaue Lean tissue mass for total nbLBM. This study is Lean tissue mass the Lezn to describe Goji Berry Soil Requirements compare the relationship of skeletal muscle to bone using both Gissue and ELan estimates.
The results tussue that Top-notch use maes MRI provides a modestly better fitting model for Lan relationship of Thermogenic protein shakes muscle to bone compared with DXA.
Lean tissue mass muscle masa an impact on bone mineral content independent of total non-bone lean body mass. In maxs, Hispanics had tisuse bone mineral content compared to other race tisse ethnic tjssue after adjusting for sex, Cayenne pepper uses, adipose tissue, skeletal muscle mass, and height.
In a review tisue human studies, Weinsier tiwsue al. However, masd becomes an increasingly greater component. Thus, the ratio tisue skeletal muscle mass to OMAD and nutrient absorption free tiasue mass masx not constant during mqss.
Consequently, estimates of fat-free body mass or of nbLBM might not accurately reflect the relationship of skeletal muscle mass to bone in children. InKim et al. described a masx method for estimating skeletal tissur mass from whole body DXA scans and compared Cayenne pepper uses estimates with skeletal Skin repair treatments mass Effective ways to lower hypertension levels by whole nass magnetic resonance imaging[ 2 Cayenne pepper uses.
The assessment of tissue muscle mass from Maes or Tiissue allows mas the isolation of skeletal Lan mass from total Urinary problems in menopause in the limbs and whole body respectively.
These new techniques make it possible to determine how yissue in skeletal muscle mass as a component of nbLBM relate to changes in bone mineral content in children[ 3 ].
Several studies tssue the association between nbLBM Leab bone mineral content, using DXA, have demonstrated that for individuals of the same total body mass, nbLBM has consistently been a predictor of bone mineral content[ Leam — 7 ].
Studies have also demonstrated an association gissue muscle Blood glucose testing and bone area in children using computed tomography of individual maes typically the arm [ 6Lexn — 10 ].
In addition, tssue study tiasue Wang et al. found Athlete dietary restrictions skeletal tlssue measured by total mss potassium in children was significantly Leab with bone mass measured by total body Lean tissue mass 11 ]. Tidsue from these studies suggest that the relationship Lwan skeletal muscle and bone mineral content in tisxue is affected by sex, pubertal development, and race or tissuw.
Until recently however, it has not been possible to tossue these relationships using whole body estimates of skeletal muscle mass. The purpose of this study was to Lwan compare Mas measures of Effective immune system, DXA-based estimates of skeletal muscle mass, and MRI-based tissuw of skeletal muscle mass with respect to their relationship to bone mineral content in an ethnically diverse sample Leaan 6 to 18 year old boys and girls.
The relationship between bone mineral content and the ratio of skeletal tiasue mass Lean tissue mass total nbLBM was also determined. The influence Leam age, sex, race or Mas, adipose kass by MRIand pubertal tiseue on masd relationships was studied.
Subjects were children ages 6 to 18 years of age who participated in two separate tkssue. For both studies, all data were collected at the New York Obesity Research Center Heart health research St.
Luke's Roosevelt Rissue. All maws were recruited through schools, local area newspaper advertisements, and tissje posted in various locations in Lfan local community. Written Leab was obtained from tiwsue of all participating children. In addition, verbal mass was obtained from all children and written assent from children older than 7 years of age.
Approval was obtained from the Institutional Review Board at the St. Luke's Roosevelt Hospital before enrollment and data collection. All appropriate protections for human subjects were followed in the study procedures data management and reporting of results.
Demographic and anthropomorphic measures were collected on each subject, body mass measured by a digital scale Avery Weigh-Tronix digital scale, model DS, Pointe-Claire, Quebec, Canada to the nearest 0.
A medical exam was performed by a pediatrician after each subject's enrollment to assess: 1 Tanner stage according to breast and pubic hair development in girls and testicular and pubic hair development in boys; and, 2 overall physical health.
Race and ethnicity were assessed by questionnaire reporting of the race or ethnic background of the participating child the possible categories were Asian, Black, White, Hispanic, and other.
Whole body DXA was used to assess bone mineral content in all subjects using the Lunar Prodigy GE Medical, Madison, WI.
Four software versions were used between and including versions 6. Each scan provided estimates of total fat-free mass, total and regional bone mineral content, non-bone lean body mass nbLBMand fat mass Figure 1.
In our laboratory the coefficients of variation were 5. Appendicular lean soft tissue ALST was the sum of non-bone lean body mass total fat free mass - total bone mineral content in the mqss and left legs and arms and was calculated using computer generated and manually confirmed default lines on anterior view planogram as previously described[ 21415 ].
Default lines are based on anatomic landmarks including the perpendicular axis of the femoral neck angled with the pelvic brim to the tips of the phalanges in the legs and the center of the arm socket to the phalange tips in the arms.
Estimates of total body skeletal muscle mass SMM DXA were calculated from ALST values, height, and weight for each subject using a prediction equation developed for pediatric samples by Kim et al[ 2 ]. Body Composition Components Estimated by Dual x-ray Absorptiometry and Magnetic Resonance Imaging.
There is a shaded gray barrier between the estimated skeletal muscle mass block and the organ and other soft tissue block in the 4th column under "dual x-ray absorptiometry" estimates. This conveys the SMM DXA is an estimate of total body musculature based on limb muscle mass only.
Thus there is likely to be variability in the accuracy of SMM DXA values for individual subjects with respect to the estimated amount of axial musculature e. Although SMM DXA values are estimates of total body muscle mass they are derived from limb musculature only.
Thus SMM DXA values might be less accurate than a direct measure of total body muscle from MRI. This difference in the accuracy of SMM DXA and SMM MRI might affect their relationships to bone mineral density. The current analysis included subjects from one study who contributed data to the prediction equations by Kim et al.
Thus, this analysis does not represent an external validation of the prediction equation developed by Kim et al. Whole body multi-slice MRI was used to determine skeletal muscle mass SMM MRI for all subjects.
Subjects lay motionless on the scanner platform with arms extended above their heads. The origin of all scans was set at the inter-vertebral space between L4 and L5.
Transverse images were then acquired for the whole body with a between slice gap of 40 mm in taller pediatric subjects and 25 or 35 mm gaps for smaller pediatric subjects generating between 30 and 40 image slices per subject[ 216 ].
Total body skeletal muscle and adipose tissue volumes were converted to mass using the estimated density of both tissues 1. The intra-class correlation coefficient for MRI estimates of skeletal muscle mass has been reported as 0.
In addition, technical error for repeated estimates of skeletal muscle mass in adults from whole body MRI imaging with a single trained analyst is 1. Figure 1 depicts the elements of fat mass and fat-free mass identified by MRI and DXA respectively.
Elements of body tissues are identified from MRI by marking the physical boundaries of tissues visualized in each scan fat, muscle, and organsmeasuring their volume, and estimating their mass. Body tissues are identified from DXA by determining the absorption of x-ray beam according to differences in tissue properties i.
differences in the absorption of bone compared with muscle or fat. Thus, adipose tissue measured by MRI contains fat mass as well as the connective tissue that surrounds fat cells, and tends to be larger than DXA based estimates of fat mass alone.
In tisske, skeletal muscle is directly visualized in the trunk and limbs and can therefore be quantified by whole body MRI. Although the distinct absorption of x-ray beams from DXA scans allows for measurement of nbLBM, skeletal muscle mass must then be estimated from these values e.
using the equation by Kim et al. Descriptive statistics were calculated for demographic and body composition variables using means and standard deviations SD for dimensional variables and frequencies for categorical variables. In order to examine how well the Kim et al. prediction equation approximated MRI estimates of skeletal muscle mass, we examined body composition variables for subjects who participated in the Kim et al.
Mean estimates of non-bone lean body mass, and skeletal muscle mass from DXA and MRI SMM DXAand SMM MRI were calculated for male and female children at each sample and plotted as a function of age Figure 2.
Subjects from both studies were combined for the subsequent analyses. Skeletal Muscle Mass by DXA and MRI, and Total Non-Bone Lean Body Mass as a Function of Age years. Multiple linear regression was used to develop a model to determine whether skeletal muscle mass was independently associated with greater bone mineral content adjusting for the variables found to be significantly associated with either variable including age, sex, race or ethnicity, Tanner stage, and adipose tissue.
The hypothesis that skeletal muscle mass from MRI SMM MRI would be a better predictor of bone mineral content BMC compared with skeletal muscle mass from DXA SMM DXA and nbLBM was tested using linear regression. The different estimates for skeletal muscle mass and nbLBM SM MRISMM DXAand nbLBM were compared with respect to their association with bone mineral content using the semi-partial correlation coefficient in multiple linear regression models.
Tanner stage data were available for only a subset of subjects. Although pre-pubertal children were found to have lower adipose tissue and bone mineral content compared with pubertal children, the R 2 value of multivariate models predicting bone mineral content only improved by 0.
Thus, Tanner stage was not included in the analyses. Linearity of relationships between dependent and independent variables were assessed using scatter plots and residual plots to explore relationships between independent and dependent variables; and transformations were studied including interactions by testing products among variables.
Logarithmic conversion of skeletal muscle mass, bone mineral content, nbLBM and adipose tissue improved the linearity of regression models and was therefore used in the models presented. All statistical analyses were performed using the SAS version 9.
The level of significance for all statistical tests of hypotheses was 0. Similar trends in sexual dimorphism were observed in the original validation and external samples Table 1.
The values for skeletal muscle mass SMM DXA estimated using the prediction equation, Kim et al. study and subjects from the second study. As expected, estimates of non-bone lean body mass nbLBM were consistently higher compared with estimates of skeletal muscle mass SMM DXA and SMM MRI as nbLBM contains organ and non-muscle soft tissue in addition to muscle Figure 2.
The absolute difference between nbLBM and estimates of skeletal muscle SMM MRI became greater with increasing age increased from In multiple regression models, being male, increasing age, greater adipose tissue ATgreater muscle mass by MRI, Hispanic ethnicity, and greater height were associated with greater bone mineral content Table 3.
There was a significant interaction between sex and adipose tissue AT which indicates the effect of adipose tissue AT varies tiszue gender. The coefficients are 0.
This study is one of the first to examine the independent impact of skeletal muscle mass alone on bone mineral content or to compare various estimates of skeletal muscle mass with respect to the relationship with bone mineral content in children.
There was overlap among the estimates of nbLBM and skeletal muscle mass. When all three estimates of skeletal muscle mass and non-bone lean body mass nbLBM were included in one model, only MRI estimates of skeletal muscle mass independently predicted bone mineral content.
: Lean tissue mass| Introduction | As your muscles and internal organs have a higher metabolic rate that than the equivalent weight of fat, a good percentage of lean body mass boost your metabolism and make it easier to maintain the healthy weight you want. There is also evidence that a high proportion of lean mass reduces inflammation because the small fat cells in lean individuals promote healthy function, while the enlarged fat cells in overweight or obese people promote inflammation and chronic disease. As a person trying to maintain or improve their health, it can be difficult to know which measurements are the most important. Your weight only tells you the total and not how much of that weight is made up of fat and how much of it is healthy muscle. Likewise, your BMI Body Mass Index is calculated on your weight and height so it also does not take into account how your weight is comprised. By looking at the elements which make up lean body mass and your overall calculation of lean body mass, you will have a much more accurate picture of how healthy you are and tracking this figure over time will show you the impact of diet and exercise on maintaining or improving your lean body mass percentage. You can work towards improving your lean body mass by increasing your healthy muscle check out our earlier blog post 6 Tips to Improve Your Skeletal Muscle , by making sure you are well hydrated and, if necessary, by working towards reducing your visceral and subcutaneous fat. You will also be working on your lean body mass while you are getting good quality sleep as oxygen and nutrients are carried to your muscle tissues to help their post work-out healing and growth! Whatever your score, understanding your body and your fitness level will put you in a much stronger position to maintain and improve your health, and Tanita are here to help you. The store will not work correctly when cookies are disabled. Consumer scales For Professionals Understanding your measurements Mini scales Support Blog About TANITA. Home Blog Muscle focus Lean Body Mass explained. Lean Body Mass explained. Muscle focus. March 03, What is included in Lean Body Mass? Lean body mass includes the weight of all the following elements which make up your body: Organs Skin Bones Body Water Muscle Mass. Skeletal muscle mass is sometimes referred to as either fat-free mass or lean soft tissue. These two terms are often used interchangeably to refer to skeletal muscle, which only adds to the confusion surrounding the two terms. It is important to realize the two terms do not mean the same thing, and the figure below demonstrates the difference between them. Lean soft tissue is the sum of body water, total body protein, carbohydrates, non-fat lipids and soft tissue mineral Prado and Heymsfield Conversely, fat-free mass includes bone as well as skeletal muscle, organs, and connective tissue Prado and Heymsfield The main difference between the two centers on how bone mass is handled. If bone mass or bone density cannot be measured it has to be calculated with the skeletal muscle mass and you have fat-free mass. On the other hand, if you can measure bone mass or bone density you can separate it out from skeletal mass and you now have lean soft tissue and bone mass. Therefore, the use of the term fat-free mass or lean soft tissue is ultimately dependent upon the methodology used to measure skeletal muscle. Determination of body composition using a 2-component model e. Figure divides the body into either fat mass or fat-free mass. On the other hand, determining body composition using a 3-component model e. The term fat-free mass and lean soft tissue also indicate to some extent the accuracy of the measurement of skeletal muscle mass. The flaws in these assumptions explain the inaccuracy of the 2-component model since the inclusion of bone mass with lean soft tissue leads to an overestimation of skeletal muscle mass, and the density of these two components can differ in regions of the body. In addition, there is a progressive loss of bone mineral with aging that leads to a decrease in body density over time Shephard, , making the use of a 2-component model less accurate in populations that may have different body densities than the normal population i. Several methods are available to measure skeletal muscle mass using a 2-component model, ranging from simple, inexpensive field methods e. to more complicated and expensive laboratory methods e. DXA, which measures bone density, is the most common 3-component method of measuring body composition. By measuring bone density, DXA is able to eliminate the assumptions that 2-component methods make regarding bone density. This ultimately improves the accuracy of the DXA especially in athletic and older populations whose bone density varies from the bone density in the average population. |
| IS IT LEAN SOFT TISSUE OR FAT-FREE MASS? | Conversely, upper limb LTM, lower limb LTM and ALM were higher. Obesity, a global epidemic, is associated with a myriad of complications, many of which are musculoskeletal in nature [ 5 ]. Article PubMed Google Scholar Lake JK, Power C, Cole TJ. Methods healthy children boys; 72 girls had assessments of body mass, height, and Tanner stage. A recent systematic review reported that there were insufficient cohort studies available to perform a meta-analysis and draw conclusions regarding the relationship between fat mass and risk of incident and worsening pain, highlighting the need for further high-quality longitudinal studies [ 20 ]. If your looking for a good tasting, nice texture, and clean ingredients these are the bars. |
| Lean Body Mass Calculator | All authors read and approved the final version of the paper. Appendicular LTM ALM using both approaches was calculated from the sum of LTM of both arms and legs. Read Edit View history. Additional information Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. CAS Google Scholar Shen W, Lui H, Punyanitya M, Chen J, Heymsfield SB: Pediatric obesity phenotyping by magnetic resonance imaging methods. Your weight only tells you the total and not how much of that weight is made up of fat and how much of it is healthy muscle. Love 36 Share Tweet Share Pin. |
| Muscle vs Lean, what does it mean? | Intra-and extra-cellular water distribution in the limbs after cervical spinal cord injury. Generally, men have a higher proportion of LBM than women do. Article PubMed PubMed Central Google Scholar Hussain SM, Urquhart DM, Wang Y, et al. KJD participated in the design of the study, performed data collection, data entry, and analysis and contributed to paper preparation. American society for parenteral and enteral nutrition clinical guidelines: the validity of body composition assessment in clinical populations. |
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The Most EFFICIENT Way To LOSE FAT - Andrew HubermanLean tissue mass -
Since values for LTM were also provided in this study [ 11 ], ρ could be calculated according to Eq. These ρ values were then used in Eq. L was calculated as percentage of height using the fractional coefficients calculated from the data of Cirnigliaro et al.
Total body and segmental body composition was determined using proprietary software provided by ImpediMed Ltd. Brisbane, Australia. The data output was TBW, ECW, ICW and LTM for each body segment and TBW, ECW, ICW, FFM and FM for whole body.
Body water volumes are in litres L and tissue masses in kg. DXA does not provide data on body water, so to enable interpretation of the BIS body water output, data for ECW:ICW ratios were obtained from an age and sex-matched cohort of 47 healthy subjects drawn from a database maintained at the University of Queensland [ 23 ].
Mean ECW:ICW ratios were also calculated from the data of Cirnigliaro et al. Appendicular LTM ALM using both approaches was calculated from the sum of LTM of both arms and legs.
Since the LOA method only assesses agreement between pairs of data, agreement between all three methods was assessed using median absolute percentage error analysis as described elsewhere [ 24 ].
Paired t tests were performed to determine differences between predicted body composition and the reference DXA data. Statistical analysis was undertaken using Medcalc version Characteristics of the participants with SCI are listed in Table 2.
All participants were Caucasian apart from one Asian female. The mean age was Nine participants had high tetraplegia one AIS A , two had low tetraplegia nil AIS A and three had paraplegia two AIS A.
Mean BMI was The resistivity coefficients for the body segments calculated from the published data of Cirnigliaro et al. Although differing in absolute magnitude in both sets of data, resistivity coefficients for participants with paraplegia were less than those with tetraplegia.
There were no differences in resistivity values between the right and left limbs, unlike the published data of Cirnigliaro et al. where there was a significant difference in the resistivity coefficient between the arms for participants with tetraplegia [ 11 ].
Table 4 compares the BIS predicted total body FFM, total body LTM and LTM of body segments to the measured values by DXA based on the different methods. Although the SCI-specific prediction of Cirniglio et al.
Predictions of LTM in the arms by either the proprietary equation or the SCI-specific prediction method of Cirnigliaro et al. However, the mean difference bias and LOA were smaller for the proprietary equation than the Cirnigliaro et al.
For leg LTM and ALM, the proprietary equation performed better than the Cirnigliaro et al. The relative volumes of ECW and ICW expressed as a percentage of total tissue fluid provided by the proprietary equations for the acute participants with SCI, the Cirnigliaro et al.
A difference of between 3. Data are also presented for comparison from Cirnigliaro et al. ECW volume and ICW volume calculated as percentage of total tissue water. The measurement of body composition is important when assessing nutritional status in clinical populations including acute SCI as the results can inform nutritional diagnosis, guide nutritional prescriptions and be used to monitor the outcomes of nutrition and exercise interventions [ 5 ].
Minimising loss of LTM and avoiding fat mass gain are important goals to avoid secondary morbidity following SCI. Body weight is commonly used to assess effects of nutritional interventions and is a crude measure that does not discern between alterations in LTM or fat mass.
The aim of SCI dietetic management is to minimise LTM loss via the provision of a high energy and protein diet acutely and to prevent weight and fat gain by providing dietary counselling regarding weight management during rehabilitation.
The use of BIS to monitor segmental changes in body composition, specifically LTM is important to adjust dietetic therapy, specifically when to modify the focus of interventions to avoid the detrimental fat gain associated with poor metabolic outcomes.
This study in participants with acute SCI compared the prediction of segmental LTM from BIS using a proprietary method and the published method of Cirnigliaro et al. The significant correlations and high levels of agreement with DXA measures in our participants with acute SCI suggest that BIS can be used to predict segmental LTM and ALM.
With the exception of leg LTM, either the proprietary equation or the Cirnigliaro et al. However the proprietary equation predicted leg LTM and ALM better than the Cirnigliaro et al. To our knowledge, only one other study has tested the validity of bioimpedance to predict TBW, FFM and FM and considered segmental parameters in people with SCI.
Buchholz et al. Predicted ECW using BIA was strongly correlated with measured ECW using deuterium dilution and there was no significant bias or difference between the two methods. The only reported segmental data were resistance and reactance and these were significantly higher in the group with paraplegia for the leg, trunk and whole body, indicative of a lower TBW, hence lower FFM in these body segments compared with controls.
In contrast, arm resistance in the participants with paraplegic was lower than the control group and reactance higher suggestive of greater TBW and body cell mass. Unfortunately, deuterium dilution only enables determination of whole-body ECW, so segmental body composition using bioimpedance was not validated in that study [ 27 ].
We hypothesised that the Cirnigliaro population-specific method [ 11 ] for predicting segmental LTM developed in people with chronic SCI would more closely predict segmental LTM in participants with acute SCI than the proprietary method.
BIA assumes that the body is a homogenous conductive cylinder of uniform length and cross-sectional area, however the body shape is better represented by five inter-connected cylinders trunk, two legs and two arms [ 14 , 21 ].
We theorised that muscle wasting following SCI would change the body geometry, i. the cross-sectional area of the arms and legs, and therefore the method developed in chronic SCI using BIS would be more accurate than the proprietary method.
Muscle wasting occurs progressively for up to 6 months post injury [ 1 ] and may not have occurred to a sufficient degree in our SCI participants to adversely influence prediction.
While details of the proprietary segmental body composition method are not known, it is possible that a body proportionality factor accounting for limb shape is incorporated into the prediction algorithms as is the case for whole-body BIS methods [ 23 ] and therefore accounts better for changes in body geometry than the model adopted by Cirnigliaro et al.
The presence of oedema in our participants may be explained by the low serum albumin levels which are reflective of the stress-induced response to trauma or acute illness [ 30 ].
Hypoalbuminaemia causes a decrease in colloid oncotic pressure in the vascular space and ensuing accumulation of extracellular fluid [ 30 ].
The population-specific method used to predict LTM in our acute cohort differed slightly to that of Cirnigliaro et al. Prediction of LTM by Cirnigliaro et al. Since people with acute SCI are prone to the development of oedema [ 27 ], only the intracellular resistivity coefficients for each body segment were used to predict LTM as the use of the extracellular resistivity coefficients could artificially predict an elevated LTM.
Limitations associated with this study include lack of knowledge of the proprietary segmental body composition equations and participation of individuals with metal implants, though current information suggests that the potential impact that metal surgical implants have on the prediction of body composition is small and of minimal clinical importance [ 31 ].
Additionally neither method enabled the prediction of trunk LTM and fat mass. Furthermore, the small and heterogeneous sample in regards to gender, level of injury and AIS classification may limit the generalisability of the findings.
However, despite the participant heterogeneity we showed that BIS can be used clinically to predict segmental LTM and ALM in people with acute SCI. The heterogeneous sample is reflective of the SCI population and highlights the challenges in assessing and monitoring nutritional status in clinical practice.
This study indicates that BIS can be used to predict total body, appendicular and segmental LTM in participants with acute SCI and to monitor the presence of oedema. Accurate assessment of body composition is essential to assess outcomes of nutrition and exercise interventions in individuals with SCI.
These findings support the use of BIS for the routine assessment of LTM in SCI. Future research should also investigate longitudinal changes in total, appendicular and segmental body composition and whether BIS can be used to inform nutrition and activity prescriptions and monitor outcomes in individuals with acute and chronic SCI.
Castro MJ, Apple DF Jr, Hillegass EA, Dudley GA. Influence of complete spinal cord injury on skeletal muscle cross-sectional area within the first 6 months of injury. Eur J Appl Physiol Occup Physiol. Article CAS Google Scholar. Gorgey A, Dudley G. Skeletal muscle atrophy and increased intramuscular fat after incomplete spinal cord injury.
Spinal Cord. Bauman WA, Spungen AM, Wang J, Pierson RN Jr. The relationship between energy expenditure and lean tissue in monozygotic twins discordant for spinal cord injury. J Rehabil Res Dev.
Article Google Scholar. Felleiter P, Krebs J, Haeberli Y, Schmid W, Tesini S, Perret C. Post-traumatic changes in energy expenditure and body composition in patients with acute spinal cord injury. J Rehabil Med. Teigen LM, Kuchnia AJ, Mourtzakis M, Earthman CP.
The use of technology for estimating body composition: strengths and weaknesses of common modalities in a clinical setting. Nutr Clin Pract. Beck LA, Lamb JL, Atkinson EJ, Wuermser LA, Amin S. Body composition of women and men with complete motor paraplegia.
J Spinal Cord Med. Buchholz AC, McGillivray CF, Pencharz PB. Differences in resting metabolic rate between paraplegic and able-bodied subjects are explained by differences in body composition.
Am J Clin Nutr. Maggioni M, Bertoli S, Testolin G, Merati G, Margonato V, Veicsteinas A. Body composition assessment in spinal cord injury subjects. Acta Diabetol. Spungen AM, Adkins RH, Stewart CA, Wang J, Pierson RN Jr, Waters RL, et al.
Factors influencing body composition in persons with spinal cord injury: a cross-sectional study. J Appl Physiol. Jones LM, Legge M, Goulding A. Healthy body mass index values often underestimate body fat in men with spinal cord injury. Arch Phys Med Rehabil. Cirnigliaro CM, La Fountaine MF, Emmons R, Kirshblum SC, Asselin P, Spungen AM, et al.
Prediction of limb lean tissue mass from bioimpedance spectroscopy in persons with chronic spinal cord injury. Nuhlicek DN, Spurr GB, Barboriak JJ, Rooney CB, el Ghatit AZ, Bongard RD. Body composition of patients with spinal cord injury.
Eur J Clin Nutr. CAS PubMed Google Scholar. Singh R, Rohilla RK, Saini G, Kaur K. Longitudinal study of body composition in spinal cord injury patients. Indian J Orthop. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, et al. Bioelectrical impedance analysis—part I: review of principles and methods.
Clin Nutr. Panisset MG, Desneves K, Ward LC, Rafferty J, Rodi H, Roff G, et al. Bedside quantification of fat-free mass in acute spinal cord injury using bioelectrical impedance analysis: a psychometric study.
Yamada Y, Watanabe Y, Ikenaga M, Yokoyama K, Yoshida T, Morimoto T, et al. Comparison of single-or multifrequency bioelectrical impedance analysis and spectroscopy for assessment of appendicular skeletal muscle in the elderly.
Sheean P, Gonzalez MC, Prado CM, McKeever L, Hall AM, Braunschweig CA. American society for parenteral and enteral nutrition clinical guidelines: the validity of body composition assessment in clinical populations. J Parenter Enter Nutr. Kirshblum S, Waring W III.
Updates for the International Standards for Neurological Classification of Spinal Cord Injury. Phys Med Rehabil Clin N Am. Garshick E, Ashba J, Tun GC, Lieberman LS, Brown R. Assessment of stature in spinal cord injury. Pelletier CA, Miyatani M, Giangregorio L, Craven BC. Original research: sarcopenic obesity in adults with spinal cord injury: a cross-sectional study.
Cornish B, Jacobs A, Thomas B, Ward L. Optimizing electrode sites for segmental bioimpedance measurements. Physiol Meas. Thomas B, Cornish B, Ward L. Bioelectrical impedance analysis for measurement of body fluid volumes: a review. J Clin Eng. Ward L, Isenring E, Dyer J, Kagawa M, Essex T.
Resistivity coefficients for body composition analysis using bioimpedance spectroscopy: effects of body dominance and mixture theory algorithm. Seoane F, Abtahi S, Abtahi F, Ellegård L, Johannsson G, Bosaeus I, et al.
Mean expected error in prediction of total body water: a true accuracy comparison between bioimpedance spectroscopy and single frequency regression equations. BioMed Res Int. Physical status: the use of and interpretation of anthropometry.
Report of a WHO Expert Committee. Desneves KJ, Panisset MG, Rafferty J, Rodi H, Ward LC, Nunn A, et al. Comparison of estimated energy requirements using predictive equations with total energy expenditure measured by the doubly labelled water method in acute spinal cord injury.
Buchholz AC, Pencharz PB, McGillivray CF. The use of bioelectric impedance analysis to measure fluid compartments in subjects with chronic paraplegia.
Cornish BH, Thomas BJ, Ward LC, Hirst C, Bunce IH. A new technique for the quantification of peripheral edema with application in both unilateral and bilateral cases. Tanaka T, Yamada Y, Ohata K, Yabe K.
Intra-and extra-cellular water distribution in the limbs after cervical spinal cord injury. J Exerc Sports Physiol.
Google Scholar. Fuhrman MP, Charney P, Mueller CM. Hepatic proteins and nutrition assessment. J Am Diet Assoc. Steihaug OM, Bogen B, Ranhoff AH, Kristoffersen MH. Bones, blood and steel: How bioelectrical impedance analysis is affected by hip fracture and surgical implants.
J Electr Bioimpedance. Download references. The authors acknowledge the contributions of Dr. Andrew Nunn of the Victorian Spinal Cord Service for facilitating participant access, dietitians Helena Rodi and Jillian Rafferty for assistance with data collection, as well as the contributions of the participants and their families.
Original data collection was funded by a grant from the Transport Accident Commission through the Institute for Safety, Compensation and Recovery Research ISCRR project NGE-E Author KJD was supported by a grant from the Austin Medical Research Foundation and MGP by an Australian Postgraduate Award.
Department of Nutrition and Dietetics, Austin Health, Heidelberg, VIC, Australia. The sample included Caucasian women with an age distribution from 18 to Ninety-two young women with normal weight and from 18 to 35 years old constituted the control group mean age The baseline anthropometric characteristics and the comparisons between groups according to obesity status and age are summarized in Table 1.
Physical activity levels were not specifically determined. Nevertheless, the control and patient groups consisted of individuals who performed only leisure physical activities for less than one hour per week. Moreover, none of the patients was enrolled in a training program on the day of inclusion.
The whole-body and localized LTM and FM are presented in Table 2. For all body composition parameters and low LTM indices [ALM and ALMI h 2 ], the control group presented systematically lower [ALMI BMI excepted] values than the women with obesity, regardless of age.
Young women with obesity presented higher whole-body and lower limb FM, and higher whole-body, trunk, upper limb and lower limb LTM. Further, low LTM indices [ALMI BMI excepted] were higher than those of the older women with obesity. As the difference in LTM and FM between the two obesity subgroups could have been due to weight and height differences, adjustment on these two covariables was performed Fig.
Although whole-body FM and whole-body LTM were comparable between the two groups, trunk FM, upper limb FM, and trunk LTM remained lower in the young women with obesity compared with the older women.
Conversely, upper limb LTM, lower limb LTM and ALM were higher. Comparision of fat mass and lean tissue mass adjusted on weight and height between young and older women with obesity. The prevalence of low LTM in the older women with obesity was estimated according to the different definitions retained for Caucasian women 8 , 28 , 29 and is presented in Table 3.
We next used the data from the young normal-weight women to validate the current cut-offs for ALM, ALMI h 2 and ALMI BMI Table 3.
No older patients with obesity presented low LTM when the cut-offs for ALM In these same older women with obesity, the low LTM prevalence was, respectively, To understand this wide range of low LTM prevalence, we studied the distribution of ALMI BMI according to BMI in each group Fig.
These data confirmed the reduction in ALM with age in the patients with obesity, because whatever the BMI, the older women presented lower ALMI BMI values than the young women. Nevertheless, the regression curve also showed that ALMI BMI was greatly influenced by BMI.
Further, the FNIH cut-off used for ALMI BMI , which is a fixed value, tended to over-detect low LTM in patients with severe obesity regression slope negative and significantly different from 0 in all three groups. ALM: appendicular lean mass sum of the lean soft tissue mass for the arms and legs , BMI: body mass index; FNIH: foundation for the national institutes of health.
The relationship between ALMI h 2 and BMI in the three groups is illustrated in Fig. Linear regression explaining the ALMI of young obsese patients according to the BMI. Thus, patients with a T-score lower than -2 SD were considered to have low LTM.
When this new cut-off value was applied to our population, the low LTM prevalence in the older patients with obesity was The T-scores for the young and older adults with obesity are shown in Fig. ALMI T-score determined in young and older women with obesity. This study described the wide variation in low LTM values among older women with obesity, depending on the cut-offs used.
These findings confirmed that the current cut-offs used to diagnose low LTM in the general older population are not adapted to French older women with obesity.
The development of new cut-offs, calculated from young obese women with the same disease, may be better adapted. The body composition changes with aging have been well-described in the normal-weight population and are primarily characterized by a decrease in muscle mass and an increase in FM 1 , 3 , 4.
However, the model of change in subjects with obesity remains insufficiently known. To our knowledge, only one study using NHANES data tried to model the age-related changes in segmental body composition SBC according to BMI from normal weight to obese.
These authors assumed that there is a constant BMI-related difference in SBC in all age classes 17 and thus that all the BMI categories share the same trends for aging.
On the basis of this study and other recently published data 18 , our group found that although whole-body LTM and FM evaluated by DXA were relatively constant with aging, individuals with obesity presented a localized redistribution of these two components.
More specifically, the older obese group presented lower LTM and FM at appendicular sites, particularly in the lower limbs, and higher LTM and FM at the central body compared to the younger obese group Part of this body composition variation with age could be attributed to the change in the anthropometric parameters, as the weight and height were lower in the older adults with obesity.
This was due not only to the age-related decline in stature, but also to a generational gap a trend for a steady increase in the stature of European women in the last 80 years has been reported 30 , However, the adjustment on these two covariables did not modify our results, suggesting a reduction in LTM with age in the obese population.
We assumed that the cut-offs used in our study would be fully applicable to Caucasian women and therefore that their use would not constitute a methodological bias. Further, ethnicity has been acknowledged to be an important explanation for the big gaps in cut-off points among different study populations 14 , 32 , To confirm our assumption, we recalculated the cut-offs using the method of Baumgartner et al.
The cut-off for ALMI h 2 was 5. Our results confirmed that the currently used cut-offs in this study are applicable to our regional population.
However, none of our older subjects apparently suffered from low LTM, which should prompt us to question the reality or myth of the development of this disease in this specific population. As mentioned above, the reduction in LTM, ALM and ALMI h 2 points more to an inability of the commonly used cut-offs to identify subjects with low LTM.
The higher LTM and ALMI h 2 values in the older patients compared to those of the normal-weight controls reinforced the crucial need for the definition of new thresholds for sarcopenic obesity adapted to this population by taking into account the specificity of the anthropometric characteristics of patients with obesity In this context, Batsis et al.
In this study, we calculated new cut-offs for the first time from a large group of representative young French women with obesity. As these young patients presented higher total and appendicular LTM compared with healthy controls, a higher ALMI h 2 cut-off was obtained i.
The question that remains is the following: Is this new prevalence of the same magnitude as that found in older normal-weight subjects? It is difficult to precisely answer this as many factors influence low LTM onset, progression and diagnosis Moreover, it was demonstrated that the prevalence varies with age 12 , methods of evaluation DXA or bioelectrical impedance 36 , and the cut-offs used In a multicenter study of healthy older women mean age Using the same diagnostic criteria, this prevalence increase in geriatric outpatients mean age Park et al.
Finally, Baumgartner et al. When older subjects with obesity were specifically studied, the prevalence of low LTM using the current cut-offs was systematically lower than in the general non-obese population. In a study using the EWGSOP2 criteria and an obesity definition from the fat percentile method, Bahat et al.
Moreover, these authors underlined that among sarcopenic patients, obesity may have a protective effect against the limitations of some functional measures, indicating a probable protective effect of obesity in sarcopenic individuals Using cut-offs comparable to those used in our study, Zoico et al.
Finally, in a recent meta-analysis that included 40 studies, Gao et al. Interestingly there were no significant differences in the prevalence of sarcopenic obesity among studies using different criteria for obesity definition Although our results do not seem to be complete outliers, establishing new cut-offs for low LTM diagnosis in patients with obesity is not easy, and the choice of ALMI h 2 or BMI -2 SD from young patients merits discussion.
Also, it is unknown whether patients with obesity have the same time course for muscle loss as normal-weight subjects.
We demonstrated that the bone loss with aging is reduced in women with obesity compared with normal-weight patients Also, as muscle mass was found to be independently associated with bone mass 46 and its loss often accompanies bone loss 47 , it is probable that the loss of muscle mass was also reduced in these patients.
Conversely to our hypothesis, a very recent study reported that the difference in ALMI h 2 between premenopausal 6. In a non-obese population, Kyle et al. Nevertheless, their strict selection of participants without mobility problems and a relatively high practice of regular physical exercise may explain the limited loss of LTM.
In our study, it is probable that the higher body weight of the older women with obesity partially masked the reduction in LTM. Only a prospective study concomitantly comparing pre- and postmenopausal women with and without obesity would allow us to draw conclusions, but to our knowledge this type of study has not been done.
As previously reported 14 , 38 , our study highlighted that the prevalence of low LTM in patients with obesity in terms of muscle mass is highly dependent on the set of diagnostic criteria that is applied, with values ranging from 0 to This was suggested by the observation that the highest prevalence was observed when the criteria were related to BMI [i.
It is thus probable that these new cut-offs do not identify the low LTM prevalence in the same fashion according to obesity severity. These biases were also observed in the control group see Fig.
To limit the likely effect of BMI in the obese population, we calculated a dynamic threshold for low LTM adapted to BMI.
Last, the low LTM prevalence obtained with this new cut-off was situated between the prevalence obtained from ALMI h 2 , 8.
It is clear that this dynamic cut-off adapted to BMI presents a clear advantage compared to the static values because it is adapted to all types of obesity severity.
Nevertheless, to definitively conclude that this dynamic cut-off is superior to static cut-offs, a stronger association with adverse outcomes such as decreased functionality or physical performance will need to be demonstrated.
In interpreting the study findings, some limitations should be considered. The main limitation is the cross-sectional design that may have introduced generational bias, such as weight and height variations. Nevertheless, adjustment on these two covariables did not deeply influence the results.
In addition, although it was demonstrated that the parameters reflecting lean body mass changed at a faster rate after 60 years 3 , the relatively limited number of to year-old patients in our study, who generally have the highest prevalence of sarcopenia in terms of muscle mass 25 , 40 , 41 , might limit the scope of our study to the investigated age group i.
In the current study, it should also be noted that only muscle mass was evaluated, which is less associated with functional decline and other adverse outcomes than muscle strength decrease In addition, conversely to computed tomography, DXA, which is the gold standard for analyzing LTM, is unable to directly measure muscle mass and muscle fat infiltration, which reflects muscle quality Although a new ALMI h 2 cut-off was defined in this study, it should be used with caution.
As Delmonico et al. The appendicular LTM normalized for body weight or BMI may be more adapted in the obese population 8 , 9 , It will be interesting to evaluate whether the prevalence of low LTM varies according to the BMI or fat percentage definition for obesity. To our knowledge, this is the first study that has sought to generate new adapted cut-offs of low LTM in older obese patients to help diagnose sarcopenic obesity in terms of muscle mass.
Moreover, another strength of our cut-offs is their potential utility in clinical practice, as they are simple and easily applicable. This study clearly showed that the current cut-offs used for low LTM diagnosis in the general population are not adapted to older women with obesity.
Although ALMI h 2 was lower than in the younger obese population, no older women with obesity were diagnosed as low LTM. However, before this new criterion is implemented in clinical routine, it will be necessary to determine its clinical interest and, in particular, whether it is correlated with muscle strength and physical disabilities.
Baumgartner, R. A Biol. Article CAS Google Scholar. Janssen, I. Skeletal muscle mass and distribution in men and women aged 18—88 yr. Article Google Scholar. Kyle, U. et al. Age-related differences in fat-free mass, skeletal muscle, body cell mass and fat mass between 18 and 94 years.
Article CAS PubMed Google Scholar. Coin, A. Fat-free mass and fat mass reference values by dual-energy X-ray absorptiometry DEXA in a 20—80 year-old Italian population. Article PubMed Google Scholar. Cruz-Jentoft, A. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People.
Age Ageing 39 , — Article PubMed PubMed Central Google Scholar. Landi, F. Sarcopenia and mortality risk in frail older persons aged 80 years and older: Results from ilSIRENTE study. Age Ageing 42 , — Bischoff-Ferrari, H.
Comparative performance of current definitions of sarcopenia against the prospective incidence of falls among community-dwelling seniors age 65 and older.
Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 48 , Donini, L. Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO consensus statement. Heber, D.
Clinical detection of sarcopenic obesity by bioelectrical impedance analysis. Kalinkovich, A. Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis.
Ageing Res. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Batsis, J. Sarcopenia, sarcopenic obesity and mortality in older adults: Results from the National Health and Nutrition examination survey III. Variation in the prevalence of sarcopenia and sarcopenic obesity in older adults associated with differente research definitions: dual-energy X-ray absorptiometry data from the National health and nutrition examination survery: — Zamboni, M.
Sarcopenic obesity: A new category of obesity in the elderly. Studenski, S. The FNIH sarcopenia project: Rationale, study description, conference recommendations, and final estimates.
Tian, S. Age-related changes in segmental body composition by ethnicity and history of weight change across the adult lifespan. Maimoun, L. Body composition in individuals with obesity according to age and sex: A cross-sectional study.
J Clin Med 9 , Article PubMed Central Google Scholar. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Google Scholar. Obesity and Overweight - Fact sheet Nº Ray, K.
Effects of alirocumab on cardiovascular and metabolic outcomes after acute coronary syndrome in patients with or without diabetes: A prespecified analysis of the ODYSSEY OUTCOMES randomised controlled trial. Lancet Diabetes Endocrinol. Geldsetzer, P. The state of hypertension care in 44 low-income and middle-income countries: A cross-sectional study of nationally representative individual-level data from 1.
Lancet , — Body composition changes in the first month after sleeve gastrectomy based on gender and anatomic site. Heymsfield, S. Appendicular skeletal muscle mass: Measurement by dual-photon absorptiometry. Epidemiology of sarcopenia among the elderly in New Mexico.
Kwon, H. The reference value of skeletal muscle mass index for defining the sarcopenia of women in Korea. Bone Metab. Wen, X. Are current definitions of sarcopenia applicable for older Chinese adults?.
Health Aging 15 , — McLean, R. Criteria for clinically relevant weakness and low lean mass and their longitudinal association with incident mobility impairment and mortality: the foundation for the National Institutes of Health FNIH sarcopenia project.
Fielding, R. Sarcopenia: An undiagnosed condition in older adults. Current consensus definition: Prevalence, etiology, and consequences. International working group on sarcopenia. Prevalence of sarcopenia based on different diagnostic criteria using DEXA and appendicular skeletal muscle mass reference values in an Italian population aged 20 to Garcia, J.
The evolution of adult height in Europe: A brief note. Comparisons of sarcopenia prevalence based on different diagnostic criteria in Chinese older adults. Health Aging 19 , — Shafiee, G. Prevalence of sarcopenia in the world: A systematic review and meta- analysis of general population studies.
Diabetes Metab. Newman, A. Sarcopenia: Alternative definitions and associations with lower extremity function. x Scott, D. The epidemiology of sarcopenia in community living older adults: What role does lifestyle play?.
Cachexia Sarcopenia Muscle 2 , — Gonzalez-Correa, C. Bioelectrical impedance analysis and dual X-ray absorptiometry agreement for skeletal muscle mass index evaluation in sarcopenia diagnosis.
Diz, J. Prevalence of sarcopenia in older Brazilians: A systematic review and meta-analysis. Reijnierse, E. The impact of different diagnostic criteria on the prevalence of sarcopenia in healthy elderly participants and geriatric outpatients.
Gerontology 61 , — Park, Y. Appendicular lean mass is lower in late- compared to early- perimenopausal women: Potential role of FSH. Iannuzzi-Sucich, M. Prevalence of sarcopenia and predictors of skeletal muscle mass in healthy, older men and women.
Morley, J. Bahat, G. Associations of sarcopenic obesity versus sarcopenia alone with functionality. Zoico, E. Physical disability and muscular strength in relation to obesity and different body composition indexes in a sample of healthy elderly women.
Gao, Q. Global prevalence of sarcopenic obesity in older adults: A systematic review and meta-analysis. Kim, K. Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia.
Korean J. Impact of obesity on bone mass throughout adult life: Influence of gender and severity of obesity. Bone 90 , 23— Goodpaster, B.
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