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Obesity Research Center, Department of Medicine, St. Luke's-Roosevelt Hospital, Columbia University College of Physicians and Surgeons, New York, New York 10025
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Although magnetic resonance
imaging (MRI) can accurately measure lower limb skeletal muscle (SM)
mass, this method is complex and costly. A potential practical
alternative is to estimate lower limb SM with dual-energy X-ray
absorptiometry (DXA). The aim of the present study was to develop and
validate DXA-SM prediction equations. Identical landmarks (i.e.,
inferior border of the ischial tuberosity) were selected for separating
lower limb from trunk. Lower limb SM was measured by MRI, and lower
limb fat-free soft tissue was measured by DXA. A total of 207 adults
(104 men and 103 women) were evaluated [age 43 ± 16 (SD) yr,
body mass index (BMI) 24.6 ± 3.7 kg/m2]. Strong
correlations were observed between lower limb SM and lower limb
fat-free soft tissue (R2 = 0.89, P < 0.001); age and BMI were small but significant SM predictor variables. In the cross-validation sample, the differences between MRI-measured and DXA-predicted SM mass were small (
0.006 ± 1.07 and 0.016 ± 1.05 kg) for two different proposed
prediction equations, one with fat-free soft tissue and the other with
added age and BMI as predictor variables. DXA-measured lower limb
fat-free soft tissue, along with other easily acquired measures, can be used to reliably predict lower limb skeletal muscle mass.
regional skeletal muscle; body composition; nutritional assessment
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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SKELETAL MUSCLE (SM) plays an important role in many physiological processes, and more than one-half (~55%) of total body SM is distributed in the lower extremities (15). The interest level in estimating lower limb SM is increasing (9), because, for example, exercise physiologists are presently relating limb SM estimates to the effects of physical training on work capacity and physical performance. Investigators in a wide range of disciplines share an interest in the kinetics of lower limb SM change in relation to growth, development, and aging (8).
Despite an important role in physiological and pathological processes, practical and accurate lower limb SM measurement methods are not well developed. Two methods are available for field studies to assess lower limb SM that are based on anthropometric and bioimpedance techniques (2, 9-11). Although noninvasive and inexpensive, these two methods may not be accurate in individual subject evaluations and in monitoring small changes in muscle mass. At present, the most accurate in vivo methods of measuring SM mass are multiscan magnetic resonance imaging (MRI) and computerized axial tomography (CT) (9, 18). Although MRI and CT are often used as "criterion" methods, their application in routine practice and body composition research is limited because of expense and lack of instrument access. In addition, the CT method exposes subjects to radiation, and CT, therefore, cannot be used in evaluating healthy children and premenopausal women.
The recent availability of dual-energy X-ray absorptiometry (DXA) provides a new opportunity for estimating lower limb SM mass in vivo. Whole body DXA systems allow the investigator to identify specific regions for analysis, and this permits separation of lower limb from trunk composition analyses. DXA software also allows separation of lower limb mass into bone mineral and soft tissue components. Lower limb soft tissue can be further divided into fat and fat-free soft tissue by using the ratio of X-ray attenuation at DXA's two main energy peaks (13). DXA thus has potential as an accurate method for predicting lower limb SM mass.
Three DXA models have been derived for estimating lower limb SM mass (3, 5, 16). The first model (5) assumes that lower limb skin is negligible in mass relative to the SM component. According to this model, lower limb fat-free soft tissue is equivalent to lower limb SM with a small correction for bone mineral content. This early model did not consider the unexpectedly large contribution of skin and adipose tissue to the fat-free soft tissue compartment. Compared with the multiscan CT method as the criterion, the model systematically overestimates lower limb SM by ~30% (16).
The second approach, proposed by Fuller et al. (3), advanced SM estimation by DXA as the derived model adjusted fat-free soft tissue for the contribution of skin, and this reduced SM overestimation by DXA. This model, however, was not validated by using a criterion method.
On the basis of the relationships between tissue-level components (adipose tissue-free SM, adipose tissue, skeleton, and skin) and the corresponding molecular-level components (fat-free soft tissue, fat, and bone mineral), Wang et al. (16) recently developed a third DXA-SM model that considers the contributions of skin, adipose tissue, and skeleton to the fat-free soft tissue component. Compared with multiscan CT as the criterion, the sum of three regional SM estimates (i.e., upper arm, thigh, and calf) with use of this DXA model is accurate to within a mean of ~4% (16). A limitation of this model is that it includes lower limb skin mass estimates based on measurement of several DXA frontal scanogram extremity lengths and diameters. Although conceptually important, the complex model of Wang et al. may not be practical for clinical application. The model of Fuller et al. (3) is formulated on similar considerations and is not easily applied in the clinical setting.
Despite differences in model formulas, these three published DXA models are similar: all three are based on the common principle that most lower limb fat-free soft tissue is SM. This observation suggests a strong link between fat-free soft tissue and SM in the lower limbs (16).
The goal of the present investigation was to develop practical DXA models in quantifying lower limb SM mass. Specifically, the present study protocol was designed to accomplish two objectives: to explore the determinants of lower limb fat-free soft tissue mass by DXA, with the underlying hypothesis that SM is the main contributor to fat-free soft tissue of the lower limb; and to develop and cross-validate lower limb DXA-SM prediction equations. Multislice MRI was used as a reference for quantifying leg SM mass.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Subjects.
Subjects were healthy men and women >18 yr of age. Subjects with a
body mass index (BMI) 
35 kg/m2 were excluded because of
technical limitations of DXA in markedly overweight subjects. Inclusion
into the study required that all subjects be ambulatory with no
orthopedic problems. Each subject completed a medical history, physical
examination, and routine blood studies to exclude the presence of
underlying diseases. The study protocol and methods were approved by
the Institutional Review Board of St. Luke's-Roosevelt Hospital
Center, and all subjects gave written consent before participation. The
subjects of the present investigation participated in other unrelated
studies of body composition (12).
DXA. Total body and regional body composition was estimated by using DXA (software version 3.6, Lunar DPX, Madison, WI). The system software provided the mass of fat-free soft tissue, fat, and bone mineral content for both whole body and specific regions. Repeated measurements on consecutive days in five subjects showed a technical error of 1.7% for lean mass and 3.4% for fat mass (6, 7, 14).
Lower limb composition was evaluated on completion of the scan by using manual DXA-analysis software. The lower limb was defined in the present study as the region extending from the inferior border of the ischial tuberosity to the distal tip of the toes. Landmark selection met two requirements. First, we selected a landmark that did not include any organs because DXA cannot separate organs from skeletal muscle. Second, we chose a landmark that could be clearly visualized on the DXA system terminal. Therefore, the inferior border of the ischial tuberosity is a useful and reliable landmark that met our requirements.MRI. Lower limb SM was measured by using cross-sectional MRI. Subjects were placed on the 1.5-T MRI scanner (model 6X Horizon, General Electric, Milwaukee, WI) platform with their arms extended above their heads. A T1-weighted, spin-echo sequence with a 210-ms repetition time and a 17-ms echo time was used to obtain the image data. A scan of 1.0-cm thickness was performed from the interlumbar gap between the lumbar 4 and 5 (L4-L5) to the tip of the toes, with a 4.0-cm space between scans.
The L4-L5 intervertebral space was identified on the DXA image. This landmark was selected for consistency with previous and ongoing related studies at the Obesity Research Center. The distance between the L4-L5 interspace and the ischial tuberosity was then established on the DXA frontal image. This distance was then used to select appropriate magnetic resonance images for inclusion as SM mass consistent with that of the DXA scan. The protocol involved the acquisition of ~20 axial images over the length of the legs. All MRI scans for SM were read by a single trained observer. Image data were transferred to a Silicon Graphics workstation (Mountain View, CA) for analysis by using Tomovision image-reconstruction software (Montreal, PQ, Canada).SM measurement by MRI. A multiple-step procedure was used to segment images into SM and other tissue areas. Either a threshold was selected for adipose tissue and SM on the basis of image gray-level histograms or a filter was used to discriminate between different image gray-level regions and lines were circumscribed around the SM compartment by using a watershed algorithm. The observer then labeled the different tissues by assigning them specific codes. The selected cross-sectional image was next reviewed by an interactive slice-editor program that permitted verification and, if needed, correction of the segmented results. The original gray-level image was superimposed on the segmented binary image by using a transparency mode to facilitate corrections. The segmentation procedure included removal of all visible adipose tissue within the SM compartment. The measured SM component, by necessity, included a small amount of residual adipose tissue interspersed between muscle fibers.
SM area in each image was computed by summing the muscle pixels and multiplying by the individual pixel surface area. The technical error for measurements of the same SM scan on 2 separate days by the same observer in our laboratory is 0.7 ± 0.1% (4). Lower limb SM mass can be considered as the sum of two separate muscle mass components. First, the muscle mass present between the landmark and the adjacent scan was calculated as
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(1) |
![SM (kg)<IT>=0.00104×</IT><LIM><OP>∑</OP></LIM> [<IT>5×</IT>(<IT>B<SUB>n</SUB>+B</IT><SUB><IT>n</IT>+<IT>1</IT></SUB>)<IT>/2</IT>]](/content/vol89/issue4/fulltext/1380/img002.gif)
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Statistical analysis. The subjects were equally randomized into a validation group and a cross-validation group stratified by gender (i.e., 50% from each gender). Results by group are expressed in terms of means ± SD. Differences in lower limb SM, age, body mass, height, BMI, and DXA body composition measurements between groups were tested by using Student's t-test, and P < 0.05 was considered statistically significant. Pearson's correlation coefficients were used to explore the associations between fat-free soft tissue by DXA- and MRI-measured SM.
Four multiple linear prediction models were investigated, with lower limb SM measured by MRI as the dependent variable. The possible independent variables (i.e., predictors) were lower limb fat-free soft tissue, fat mass, bone mineral content, age, BMI, ethnicity (Caucasian, African-American, Asian, and Hispanic), and gender (0 = women, 1 = men). A regression equation for each model was developed from the validation group by using the selected predictors, and then the adjusted multiple R2 was obtained to quantify fitting performance. The predicted values of lower limb SM were calculated for individuals in the cross-validation group by using the estimated regression equations. The Pearson correlation coefficient r2 between the predicted values and the MRI-observed values were then obtained from the cross-validation group. To compare the fitting and prediction performances of the regression models, the value of R2 + r2 was applied as the comparison criterion. The model with R2 = 0 and r2 = 0 represents the poorest equation, whereas a model with R2 = 1 and r2 = 1 represents the best equation. The closer to the combination of R2 + r2 = 2, the better the prediction equation. To view the agreement between MRI-observed and predicted SM values in the cross-validation sample, Bland-Altman graphs were plotted (1). Pearson correlation coefficients were used to measure the associations between the difference and the average of the two, measured and predicted, lower limb SM values.| |
RESULTS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Baseline characteristics.
The baseline characteristics of the population are presented in Table
1. A total of 207 subjects were
recruited, 104 men (49 Caucasian, 25 African-American, 17 Asian, and 13 Hispanic) and 103 women (41 Caucasian, 39 African-American, 14 Asian,
and 9 Hispanic). The total subject population was relatively young (age, 43 ± 16 yr) and had a mean BMI of 24.6 ± 3.7 kg/cm2. There were no significant differences
in age and BMI between men and women, although men were heavier and
taller than women. Men had significantly (all P < 0.001) greater fat-free soft tissue and bone mineral in the lower limb
compared with women. Lower limb fat percentage was smaller in men
(19.2 ± 7.5%) than in women (35.4 ± 8.2%;
P < 0.001).
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Prediction equations from validation group.
The validation group consisted of 104 subjects (52 men and 52 women).
Lower limb fat-free soft tissue was the strongest lower limb SM
predictor identified (P < 0.001), explaining 89.5% of between-individual variation
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(4) |
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Cross-validation of prediction equations. The cross-validation group consisted of 103 subjects (52 men and 51 women). Each prediction equation derived from the validation group was applied to the cross-validation group for predicting SM. The correlations between MRI-measured lower limb SM (criterion) and DXA-predicted lower limb SM are presented in Table 2.
The prediction equations were compared by means of the combination of R2 and r2 values (Table 2). All four prediction equations had close R2 + r2 values (1.769, 1.782, 1.785, and 1.754). As shown in Table 2, the prediction model with fat-free soft tissue alone performed well, with a high R2 + r2 value. The inclusion of age and BMI, in addition to fat-free soft tissue, led to a slight improvement and the highest R2 + r2 value among the four models. As shown in Table 3, there were strong correlations for the first two models between MRI-observed and DXA-predicted lower limb SM (r = 0.937 and 0.940, both P < 0.001). The mean and SD of the difference between MRI-measured and predicted scores (0.006 ± 1.07 kg and 0.016 ± 1.05 kg)
were small for the two models, and neither one was significantly biased downward. In contrast, the inclusion of the other variables such as
ethnicity, lower limb fat mass, and bone mineral did not significantly improve the model as respective R2 + r2 values, correlations between the MRI-observed
and predicted lower limb SM, and the mean difference between measured
and predicted scores did not change significantly.
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Lower limb SM prediction equations.
The similar magnitudes between adjusted
R2 and correlation
r2 for each model indicates that the performance
of the regression models do not depend on the groups per se. This
suggests combining data from the two groups, validation and
cross-validation, for final prediction equation development. The
following are the composite lower limb SM prediction equations
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![[adjusted<IT> R<SUP>2</SUP>=0.885, P<0.001; </IT>SEE<IT>=1.06 </IT>kg (Fig. 2)]](/content/vol89/issue4/fulltext/1380/img011.gif)
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![[adjusted<IT> R<SUP>2</SUP>=0.893, P<0.001; </IT>SEE<IT>=1.02 </IT>kg]](/content/vol89/issue4/fulltext/1380/img016.gif)
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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A primary finding of the present study is that lower limb SM constitutes a large proportion of lower limb fat-free soft tissue and that the two components are highly correlated with each other in healthy adult subjects. Age and BMI also contributed significantly to the association between leg SM and leg fat-free soft tissue.
The strong observed component associations and low SEEs formed the basis of lower limb SM prediction model development based on the widely available DXA method. Equation 5, based solely on measured lower limb fat-free soft tissue, has a high R2 (0.885) and low SEE (1.06 kg) and should prove useful in SM prediction. Equation 6, which includes age and BMI in addition to fat-free soft tissue, improves lower limb SM prediction over that for equation 5 (R2 = 0.893 and SEE = 1.02 kg). Because both age and BMI are easily acquired independent variables, it would appear appropriate to include these two significant covariates in prediction models.
The R2 for the simplest prediction formula (Eq. 5) was 88.5%, and addition of other predictor variables explained only an additional 0.8% of the variance. Another 10-12% of the variance was unaccounted for, and several factors may be responsible. First, our multiple-regression analysis model did not include a potential skin contribution because lower limb skin mass is difficult to accurately quantify. Our laboratory found, in an earlier study, that ~13% of calf and ~8% of thigh fat-free soft tissue can be accounted for by skin mass (16). Between-subject variation in the skin contribution to fat-free soft tissue may be the basis of some unaccounted for variance in our models. A likely additional source of variability is measurement error because some technician judgment is required in establishing evaluated anatomic landmarks for both DXA and MRI.
SM relationship to fat-free soft tissue. On the basis of DXA measurements, the lower limb was divided into three molecular-level compartments: fat-free soft tissue, bone mineral, and fat. On the tissue level, the lower limb was divided by MRI software into four tissue-level compartments: SM, skeleton, skin, and adipose tissue. There is an overlapping relationship between lower limb fat-free soft tissue and SM. A large portion of fat-free soft tissue exists as SM, but some fat-free soft tissue is present in skin, skeleton, and adipose tissue. On the other hand, MRI-measured SM contains a small amount of interstitial fat in addition to fat-free soft tissue.
Although these two components overlap, we still observed a relatively stable ratio of lower limb SM to lower limb fat-free soft tissue (76.0 ± 6.8%; coefficient of variation = 8.9%), with no observed gender difference. The remaining 24% of fat-free soft tissue must, by necessity, derive from skin, adipose tissue, and the nonmineral portion of skeleton.Mechanistic vs. descriptive prediction methods. There are two different categories of body composition prediction methods, mechanistic and descriptive, for estimating lower limb SM (16). Three mechanistic DXA regional SM prediction models, based on assumed underlying stable component relationships, were reported by Heymsfield et al. (5), Fuller et al. (3), and, recently, Wang et al. (16). Heymsfield et al. (5) first proposed a DXA-SM model that assumes that fat-free soft tissue estimated by DXA is equal to SM in the appendages, including the lower limb. This early model did not consider the large contribution of skin and adipose tissue to the fat-free soft tissue compartment. Wang et al. (16) estimated skin masses in healthy adults as 0.67 ± 0.07 kg in calves and 1.11 ± 0.13 kg in thighs. When skin and the fat-free portion of adipose tissue are neglected for DXA model simplification purposes, the two compartments are incorporated into the SM compartment. Accordingly, Wang et al. developed an improved DXA-SM model based on the relationship between tissue and molecular-level components. This model can accurately predict regional extremity SM compared with multislice CT as the criterion. However, the model of Wang et al. includes lower limb skin mass based on measurement of selected DXA frontal scanogram extremity lengths and diameters. The complex model of Wang et al. may, therefore, not be practical for routine clinical application. The model reported by Fuller et al. (3) is formulated on similar considerations and is also complex to apply.
Descriptive prediction equations were derived in the present study as a simple alternative to mechanistic DXA-SM models. The advantage of our descriptive prediction equations is their requirement for easily obtained measurements. The only measurement requirement is fat-free soft tissue for Eq. 5 and, additionally, age and BMI for Eq. 6. Hence, these new cross-validated prediction models with high R2 and low SEE are relatively easy to apply, DXA systems are widely available, and measurement radiation exposure is low. As with all descriptive equations, our derived prediction formulas may be region specific. The proposed models should only be applied for predicting lower limb SM with the defined landmarks in healthy adults with BMI <35 kg/m2. New studies are needed to establish whether the derived prediction equations are suitable when landmark positions are changed. Also, it is unknown whether the prediction equations can be applied for estimating arm SM. Last, because the subjects in the present study were healthy adults with BMI
35
kg/m2, it is questionable whether the proposed prediction
equations are suitable for other populations such as children and very
obese subjects. Because the between-individual proportions of fat-free soft tissue, such as muscle, skin, and adipose tissue, may vary, further studies are likely needed for developing new prediction formulas for subject groups that differ from those in this study.
Conclusion. The present study's results support a direct and strong link between lower limb SM and fat-free soft tissue mass. We built on this observation to develop and cross-validate two descriptive lower limb SM prediction formulas. Future similar studies are needed to extend the current models for use in additional subject populations.
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ACKNOWLEDGEMENTS |
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This work was supported by National Institute of Diabetes and Digestive and Kidney Disease Grant PO1 DK-42618.
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FOOTNOTES |
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Address for reprint requests and other correspondence: Z. M. Wang, Weight Control Unit, 1090 Amsterdam Ave., 14th Floor New York, NY 10025 (E-mail: ZW28{at}Columbia.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 8 December 1999; accepted in final form 22 May 2000.
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DISCUSSION
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, Men;
, women.
