J Appl Physiol 103: 1688-1695, 2007.
First published August 30, 2007; doi:10.1152/japplphysiol.00255.2007
8750-7587/07 $8.00
Applicability of a segmental bioelectrical impedance analysis for predicting the whole body skeletal muscle volume
Noriko I. Tanaka,1
Masae Miyatani,2
Yoshihisa Masuo,3
Tetsuo Fukunaga,3 and
Hiroaki Kanehisa4
1Department of Sport System, Kokushikan University, Tokyo, Japan; 2Rehabilitation Engineering Laboratory, Lyndhurst Centre Toronto Rehabilitation Institute, Toronto, Ontario, Canada; 3Department of Sport Sciences, School of Human Sciences, Waseda University, Saitama, Japan; and 4Department of Life Sciences (Sports Sciences), University of Tokyo, Tokyo, Japan
Submitted 5 March 2007
; accepted in final form 23 August 2007
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ABSTRACT
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This study aimed to test the hypothesis that a segmental bioelectrical impedance (BI) analysis can predict whole body skeletal muscle (SM) volume more accurately than a whole body BI analysis. Thirty males (19–34 yr) participated in this study. They were divided into validation (n = 20) and cross-validation groups (n = 10). The BI values were obtained using two methods: whole body BI analysis, which determines impedance between the wrist and ankle; and segmental BI analysis, which determines the impedance of every body segment in both sides of the upper arm, lower arm, upper leg and lower leg, and five parts of the trunk. Using a magnetic resonance imaging method, whole body SM volume was determined as a reference (SMVMRI). Simple and multiple regression analyses were applied to (length)2/Z (BI index) for the whole body and for every body segment, respectively, to develop the prediction equations of SMVMRI. In the validation group, there were no significant differences between the measured and estimated SMV and no systematic errors in either BI analysis. In the cross-validation group, the whole body BI analysis produced systematic errors and resulted in the overestimation of SMVMRI, but the segmental BI analysis was cross-validated. In the pooled data, the segmental BI analysis produced a prediction equation, which involves the BI indexes of the trunk and upper thigh as independent variables, with a SE of estimation of 1,693.8 cm3 (6.1%). Thus the findings obtained here indicated that the segmental BI analysis is superior to the whole body BI analysis for estimating SMVMRI.
human body composition; magnetic resonance imaging; muscle distribution; validation; cross-validation
THE QUALITATIVE ASSESSMENT of human skeletal muscle (SM) mass helps us to evaluate physical resources in relation to physical performance in daily life and/or sporting activities (16). There is increasing interest in the use of bioelectrical impedance (BI) analysis to estimate SM mass because it is safe, noninvasive, convenient, easy, and inexpensive (3). However, little information on the validity of BI analyses for estimating whole body SM mass is available. To our knowledge, only Janssen et al. (14) have tried to estimate whole body SM mass using a BI analysis in which the BI value between the right wrist and right leg was obtained. In their results, however, the developed prediction equation produced a systematic error and overestimated whole body SM mass. The BI analysis taken in the prior study has been referred to as "whole body BI analysis" (3, 5, 9, 14, 19), although the electric current in this technique has been shown to be passed thorough the whole trunk and one side of the extremities (9). When a whole body BI analysis is used to estimate whole body SM mass, the human body is assumed to be a cylindrical and isotrophic conductor with a uniform cross-sectional area (CSA). However, the whole body BI value depends strongly on the variation in the CSA of the lower arm and lower leg (4, 8, 9). Moreover, it has been reported that the change in the trunk SM volume hardly affects the whole body BI value (9). Considering these points, it is hypothesized that the BI value obtained by the whole body BI analysis may be mostly affected by SM mass in the distal parts of limbs, and so this would be a reason for the systematic error in the estimates of whole body SM mass with the whole body BI analysis (14). However, no study has examined this assumption.
As another technique of the BI analysis, Organ et al. (19) developed various combinations of electrodes to determine the BI value of every body segment, i.e., a segmental BI analysis. A prior study (12) that used a subject sample with a large variation in muscularity found that, compared with the whole body BI analysis, a segmental BI analysis that measured BI values from proximal segments of the human body (i.e., upper arm, upper leg, and whole trunk) could predict lean body mass without influence from differences in the lean tissues between the proximal and distal parts (lower arms and lower legs) of the body segments. The segmental BI analysis can be used to estimate the limb SM volume through comparison with that determined by magnetic resonance imaging (MRI) (2, 17, 18). In addition, Ishiguro et al. (13) indicated that the segmental BI analysis could be applicable to the estimation of trunk SM volume. Taking these findings into account, it may be assumed that the prediction equation developed from a segmental BI analysis, which involves the BI indexes of the upper arm, upper leg, and trunk as the independent variables, can predict whole body SM mass with a higher degree of accuracy compared with that developed from a whole body BI analysis. The present study aimed to test this hypothesis. To this end, we measured BI values using the whole body and segmental BI analyses in young adult men, including athletes, who formed a heterogeneous sample with respect to body physique and muscular development. Some data on the physical characteristics of subjects and the trunk SM volume have been reported elsewhere (13).
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METHODS
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Subjects.
Thirty healthy Asian males (19–34 yr) voluntarily participated in this study. Fourteen of the subjects were athletes (8 American football players, 3 power lifters, 1 weight lifter, 1 triathlete, and 1 baseball player) who had participated in competitive meets in their own events at the college level within a year preceding the measurements. The remainder were either sedentary or mildly active, but none was currently involved in any type of exercise program (
30 min/day, 2 days/wk). To confirm the cross-validity of the predicting equation, the subjects were randomly separated into a validation group (n = 20) and a cross-validation group (n = 10), in which the percentage of the number of athletes to the total number of subjects was almost the same, i.e., 10 athletes in the validation group and 4 athletes in the cross-validation group. Physical characteristics of each subject group are listed in Table 1. Data for the athletes were collected during preseason training. Therefore, none of the athletes were dehydrated to control their body mass for competition. All measurements for the athletes were performed more than 40 h after completion of a training session. This study was approved by the ethics committee of the Department of Life Sciences, Graduate School of Arts and Sciences, University of Tokyo, and was consistent with their requirements for human experimentation. The subjects were fully informed about the procedures and the purpose of this study. Written informed consent was obtained from all participants.
Anthropometric measurements.
Body height was measured to the nearest 0.1 cm on a standard physician's scale. The body mass was measured to the nearest 0.1 kg on a calibrated electric scale. The lengths of the limb on the right side of the body were measured to the nearest 0.5 cm with a flexible metal tape (Flat rule, KDS). In this study, the length of every body segment was defined as the distance between the electrodes placed to determine the segmental BI values in accordance with the prior study (11): upper arm, distance between the acromion process and the lateral epicondyle of the humerus (Lupper arm); lower arm, distance between the head of the radius and the processus styloideus (Llower arm); upper leg, distance between the greater trochanter of the femur and articular cleft between the femoral and tibial condyles (Lupper leg); lower leg, distance between the malleolus lateralis and the articular cleft between the femoral and tibial condyles (Llower leg). The distance between the acromion process of the right shoulder and the greater trochanter of the right femur was measured from MRI images and defined as the trunk length (LTR).
MRI measurements.
With the use of MRI scans taken with a body coil (Airis, Hitachi Medco), a series of transverse images from the acromion process to the malleolus lateralis was obtained. The image condition was T1 weighted, spin-echo, multislice sequences with a slice thickness of 10 mm and a slice interval of 20 mm, with a repetition time of 200 ms and an echo time of 20 ms. Each subject lay supine in the body coil with his arms and legs extended and relaxed. We defined the whole body SM volume as the sum of trunk and limb SM volumes (4). The trunk SM was separated from limbs by using slices between specific landmarks, the acromion process of the shoulder and the greater trochanter of the femur (4). Therefore, some SM located in the shoulder and/or gluteus (i.e, triangular and/or gluteal muscle) were partially analyzed as the trunk SMs. From each cross-sectional image, outlines of tissues (SM, subcutaneous fat, bone, visceral, and others) were traced and digitized by personal computer (Power Macintosh G4, Apple) to calculate the anatomic CSA of every tissue. Adipose and tendinous tissues, which were imaged in different tones from the muscle tissue, were excluded when digitizing. We removed as much of intramuscular adipose tissue areas as possible from the SM and categorized those as "others." By summing the anatomic SM CSA and then multiplying the sum by the interval of 20 mm, whole body SM volume was determined and referred to as SMVMRI.
The test-retest variability of SMVMRI was assessed with 10 men (22–26 yr) on two separate days. The intraclass correlation coefficient for the test-retest measurements was 0.990 and the coefficient of variation (CV) was 1.8. There was no significant difference between the mean values of the two tests. Again, the intraobserver reproducibility was assessed by analyzing the MRI images of 5 men (22–26 yr) two times. The intraclass correlation coefficient and the CV of SMVMRI values from the two trials were 0.951 and 2.9, respectively. There was no significant difference between the mean values of the two trials.
BI measurements.
A BI acquisition system (Muscle 
, Art Haven 9) and the disposable electrodes (Red Dot 2330, 3M) were used to determine the BI values of the whole body and each body segment. This system applies a constant current of 500 µA and frequency of 50 kHz through the body. The measured BI value was referred to as Z. The BI measurements were performed on different days from the MRI measurements with an interval of 1 or 2 days. The subjects refrained from vigorous exercise and alcohol intake for 24 h, and from taking a meal for 4 h, preceding the experiments. All BI measurements were carried out in the supine position, with the arms relaxed at the side but not touching the body and the legs separated at least 25.0 cm at the ankles so that there was no contact between the upper legs. The subjects were instructed to keep breathing quietly because the respiratory cycle affected the trunk Z (7). During the measurements, room temperature was kept at 23°C (8).
The electrode placement is shown in Fig. 1. The source electrodes were placed at the dorsal surface of the third metacarpal bone of the right hand and the dorsal surface of the third metatarsal bone of the right foot for the whole body BI analysis, and the dorsal surface of the third metacarpal bone of both hands and the dorsal surface of the third metatarsal bone of both feet for the segmental BI analysis. The detector electrode placement was as follows: for the measurement of whole body Z (Zwhole body), at the dorsal surface of the right wrist at the level of the hand of radial and ulnar bones and anterior surface of the right ankle between the protruding portions of the tibial and fibular bones; for the upper arm Z (Zupper arm), at the dorsal surface of both elbows between the lateral epicondyles of the humerus and the head of the radius and the acromion process of both shoulders; for the lower arm Z (Zlower arm), at the dorsal surfaces of both wrists at the level of the head of radial and ulnar bones and the dorsal surface of both elbows between the lateral epicondyles of the humerus and the head of the radius; for the upper leg Z (Zupper leg), at the articular cleft between the femoral and tibial condyles of both legs and the greater trochanter of both femurs; and for the lower leg Z (Zlower leg), at the anterior surface of both ankles between the protruding portions of the tibial and fibular bones and the articular cleft between the femoral and tibial condyles of both legs. For the trunk BI measurement, the detector electrodes were placed at the acromion process of both shoulders and the greater trochanter of both femurs. This combination of electrodes can measure Z from five regions: both sides of the upper trunk (ZTRur and ZTRul), the middle trunk (ZTRm), and both sides of the lower trunk (ZTRlr and ZTRll) (13). The whole trunk Z (ZTRwhole) can be calculated with the following equation using each BI measurement
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The BI indexes of the whole body and each body segment were calculated as follows
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The test-retest variability of the Z values and BI indexes was assessed with 23 men (19–30 yr) on two separate days. The intraclass correlation coefficients and the %CV were 0.839–0.978 and 1.6–2.9% for each Z value. There were no significant differences in each Z value between the two tests.
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Fig. 1. Schematic representations of the positions of electrodes for bioelectrical impedance (BI) analyses.
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Data analysis.
Descriptive values were presented as means and standard deviations (SDs). In the validation group, first, the equations were developed for predicting the measured SMVMRI with the use of the BI indexes as independent variables, determined in each of the whole body and the segmental BI analyses. For the whole body BI analysis, a simple regression analysis was applied to develop a prediction equation for SMVMRI with (height)2/Zwhole body as an independent variable. For the segmental BI analysis, the multiple regression analysis was used to develop the prediction equation for SMVMRI using the BI indexes in the upper arm, upper leg, and trunk as the independent variables. The estimated whole body SM volume was referred to as SMVBI; SMVwhole body BI refers to the whole body BI analysis and SMVsegmental BI for the segmental BI analysis. For every independent variable selected, the product of the standard regression coefficient in the multiple regression equation and the simple correlation coefficient in the relationship with SMVMRI, expressed as a percentage, was calculated as an index presenting its relative contribution to the estimation of SMVMRI. Second, it was confirmed that the regression slope and intercept for the relationship between the SMVMRI and SMVBI values did not significantly differ from 1 and 0, respectively. Again, the significance of the difference between SMVMRI and SMVBI was confirmed using Student's paired t-test. The SE of the estimate (SEE) was calculated to evaluate the accuracy of SMVBI. The SEE was expressed as an absolute value and relative to the mean of SMVMRI. Third, the residual (SMVMRI – SMVBI) was plotted against the mean SMV for the two methods to examine for systematic error, as described by Bland and Altman (6). When the three conditions mentioned above were satisfied, SMVBI was calculated for the individuals of the cross-validation group using the equation derived from the validation group. The cross-validity of the prediction equation was examined by the same three steps as used for the validation group. If either or both of the prediction equations were cross-validated, the data from the two groups were pooled to generate the final equation, and the standard regression coefficient of each independent variable was calculated. With regard to the final equation, too, the accuracy was confirmed by the same three steps as mentioned above. A simple linear regression analysis was used to calculate the correlation coefficient (r). The probability level for statistical significance was set at P < 0.05.
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RESULTS
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Baseline characteristics of the validation and cross-validation groups.
Table 1 shows the descriptive data on the physical characteristics in the validation and cross-validation groups. There were no significant differences between the two groups in any variables except for body mass.
Figure 2 shows the distribution of the measured SM CSA in every body segment, plotted at every 10% of the segment length. The largest SM CSA was observed at 10% LTR, and the second one at 90% LTR.
The SM volumes of the whole body and every body segment determined by MRI did not differ between the validation and cross-validation groups (Table 2). Moreover, there were no significant differences between the groups in the measured Zs and BI indexes (Table 3).
Prediction equation derived from the validation group.
The whole body BI index was significantly correlated to the SMVMRI (r = 0.883, P < 0.05) in the validation group. This relationship produced an equation, SMVwhole body BI = 422.2 x [(height)2/Zwhole body] – 1,201.4, with R2 and SEE values of 0.779 and 2,180.6 cm3 (7.7%), respectively.
In the segmental BI analysis, the BI indexes of the upper leg and trunk were selected as significant contributors to predict SMVMRI (Fig. 3) and produced an equation, SMVsegmental BI = 129.1 x [(LTR)2/ZTRwhole + 1,241.3 x (Lupper leg)2/Zupper leg] – 6,844.1, with R2 and SEE values of 0.852 and 1,866.0 cm3 (6.6%), respectively. The relative contribution of each of the two BI indexes to the prediction of SMVMRI was 51.9% for the upper leg and 33.7% for the trunk. Even if the BI index of the upper arm was entered as the predictive variable, R2 (0.856) and SEE (1,844.8 cm3, 6.5%) were similar as those in the equation using the BI indexes of the upper leg and trunk.
The regression analyses indicated that the slopes and intercepts of the regression equations for the relationship between SMVMRI and SMVBI were not significantly different from 1 and 0, respectively, in the whole body and segmental BI analyses (Fig. 4, A and B). In addition, there were no significant differences between the measured and estimated SMVs in the two BI analyses. Again, no significant systematic errors were found in the Bland-Altman plots for the whole body [r = 0.257, nonsignificant (NS)] and segmental (r = 0.204, NS) BI analyses (Fig. 4, C and D).
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Fig. 4. Relationship between the measured skeletal muscle volume (SMVMRI) and estimated SMV (A and B) and between the residual (difference between the measured and estimated SMV) and mean SMV determined by 2 methods (C and D) in the validation group. A and C indicate the corresponding relationship for the whole body BI analysis, and B and D for the segmental BI analysis. SEE, SE of estimate. Solid lines, regression lines. Dashed lines in A and B are lines of identity. Horizontal dashed lines in C and D are lines of ±2SD.
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Cross-validation of the prediction equation.
The prediction equation derived from the validation group was used to estimate SMVMRI in the cross-validation group. The slopes and intercepts of the regression equations for the relationships between SMVMRI and either SMVwhole body BI or SMVsegmental BI were not significantly different from 1 and 0, respectively (Fig. 5, A and B). However, the Bland-Altman plot for the whole body BI analysis indicated that SMVwhole body BI tended to be influenced by the magnitude of SMVMRI (r = –0.635, P < 0.05) (Fig. 5C). SMVsegmental BI (26,031.4 ± 3,312.2 cm3) did not significantly differ from SMVMRI (26,738.3 ± 3,120.8 cm3), but SMVwhole body BI (27,498.3 ± 3,694.3 cm3) was significantly greater (Fig. 6). Consequently, the data obtained by the whole body BI analysis were omitted from the analysis for developing the prediction equation using the pooled data.
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Fig. 5. Relationship between measured and estimated SMV (A and B) and between the residual (difference between the measured and estimated SMV) and mean SMV determined by 2 methods (C and D) in the cross-validation group. A and C indicate the corresponding relationship for the whole body BI analysis, and B and D for the segmental BI analysis. Solid lines: regression lines. Dashed lines in A and B are lines of identity. Horizontal dashed lines in C and D are lines of ±2SD.
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Prediction equation derived from the pooled data.
In the pooled data, too, the BI indexes of the upper leg and trunk were selected as significant contributors to predict SMVMRI and produced an equation, SMVsegmental BI = 116.1 x [(LTR)2/ZTRwhole + 1,220.8 x (Lupper leg)2/Zupper leg] – 4,913.1, with R2 and SEE values of 0.842 and 1,693.8 cm3 (6.1%), respectively. The relative contribution of two BI indexes to the prediction of the SMVMRI was 52.6% for the upper leg and 32.8% for the trunk. The regression analysis indicated that the slope and intercept of the regression equation for the relationship between SMVMRI and SMVsegmental BI were not significantly different from 1 and 0, respectively (Fig. 7A). There was no significant difference between SMVMRI and SMVBI. In addition, no significant systematic error (r = 0.239, NS) was found in the Bland-Altman plot (Fig. 7B).
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Fig. 7. Relationship between the measured and estimated SMV (A) and between the residual (difference between the measured and estimated SMV) and mean SMV determined by 2 methods (B) with the pooled data. Both indicate the corresponding relationship for the segmental BI analysis. Solid line, regression line. Dashed line in A is line of identity. Horizontal dashed lines in B are lines of ±2SD.
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DISCUSSION
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The present study is the first to compare the accuracy of SMVBI between whole body and segmental BI analyses. In the validation group, the whole body and segmental BI analyses produced equations with a similar accuracy for estimating SMVMRI. In the cross-validation group, however, SMVwhole body BI was significantly greater than SMVMRI, and so only the segmental BI analysis was cross-validated. The SEE value (6.3%) obtained from the application of the segmental BI analysis to the pooled data was lower than that (9%) reported in a prior study (14) that used the whole body BI analysis to estimate whole body SM mass. The present results indicated that the segmental BI analysis could predict SMVMRI more accurately than the whole body BI analysis.
Janssen et al. (14) reported that the prediction equation derived from data on Caucasians obtained using the whole body BI analysis overestimated the whole body SM mass in an Asian cohort. They speculated that the biological differences between Caucasians and Asians would influence the relationship between Z value and the whole body SM mass. Meanwhile, the present study indicated that the whole body BI analysis overestimated SMVMRI even though Asians were used as the subjects to develop the prediction equation. Certainly, there is a possibility that the poor performance of the whole body BI analysis in the cross-validation group might be attributed to the subject sample size. However, if the volume units are converted to mass units by multiplying the volumes by the assumed constant density for adipose-free SM (1.04 kg/l) (20), one can find a similar average value (27.5 ± 5.9 kg) for the subjects in the present study as that (26.4 ± 7.6 kg) examined by Janssen et al. (14). Regardless of the subject sample size taken in the present study, therefore, it seems that the whole body BI analysis itself has a potential to overestimate SMVMRI.
A prior study (12) suggested that the application of the whole body BI analysis to the estimation of the lean body mass did not reflect the relative development of lean tissue mass in the upper arms and upper legs within the arms and legs, respectively, to the BI measurements. In general, SM volume is less in the distal than the proximal segment in each of the arms and legs. From the findings of Kanehisa and Fukunaga (15), the SM CSA of the upper leg was greater in the strength-trained athletes than in the untrained subjects, but that of the lower leg was similar between the two groups, when the difference in lean body mass was normalized. In the subject sample including athletes, therefore, it was expected that the relative difference in the SM volume between the segments in either arms or legs would be a factor explaining the residual of the whole body BI analysis. In the pooled data of the present study, however, there were no significant relationships between the residual of the whole body BI analysis and the SM volume ratios of the upper arm to the arm (r = –0.117, NS) and the upper leg to the leg (r = 0.271, NS). This implies that the accuracy of the whole body BI analysis in the estimates of SMVMRI was independent of the differences in SM distribution between the proximal and distal parts in each of the upper and lower extremities. On the other hand, the percentage of the sum of SM volumes of the upper arm, upper leg, and trunk to the SMVMRI was 84.4%. Compared with the SM CSAs and volumes of these segments, those of the lower arm and lower leg were considerably smaller as shown in Fig. 2 and Table 2. Therefore, if the Z value measured by the whole body BI analysis would reflect the SM volume of these distal segments rather than that of the upper arm, upper leg, and trunk, it might be a reason why the predicting equation was not cross-validated.
To test the assumption mentioned above, we applied a multiple regression analysis using the whole body BI value as the dependent variable and the BI values in the upper arm, lower arm, upper leg, lower leg, and trunk as the independent variables in the pooled data. As a consequence, the relative contribution of the BI values in the lower arm and lower leg for determining the whole body BI was 60.0%. In addition, the residual in the estimate of SMVMRI using the BI indexes of the lower arm and lower leg as the independent variables was significantly correlated with that of the whole body BI analysis (r = 0.831, P < 0.05) in the pooled data (Fig. 8). These results indicate that the whole body BI value is largely influenced by the distal extremities, and consequently it may be a factor producing the error in the estimate of SMVMRI by the whole body BI analysis. On the other hand, it may be that the segmental BI analysis used in the present study resulted in a higher accuracy for estimating SMVMRI compared with the whole body BI analysis by selecting the BI indexes of the upper leg and trunk, which have higher percentages of the SM volume in the whole body (33.8% and 41.3%, respectively, in the pooled data).
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Fig. 8. Relationship between the residuals (difference between the measured and estimated SMV) in the predicting equation using the BI indexes of the lower arm and lower leg as independent variables (y-axis) and in the whole body BI analysis (x-axis) with the pooled data. Solid line, regression line.
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From the finding of Ishiguro et al. (12), the BI indexes of the upper arm, upper leg, and trunk were selected for estimating the lean body mass by segmental BI analyses. At the start of the present study, it seemed that the BI index of the upper arm would also be a significant contributor for predicting the whole body SM volume. However, the present results indicated that SMVMRI could be predicted by measuring the BI indexes of the trunk and upper leg only. Adding the BI index of the upper arm as a predictive variable did not improve the accuracy of the estimates of SM volume. One reason for this result may be the procedure used for measuring the trunk Z values. The present study measured the Z values of the trunk in five regions (both sides of the upper region, the middle region, and both sides of the lower region). On the other hand, Ishiguro et al. (13) assumed the trunk to be one cylinder and obtained the Z value using a network circuit model with the detector electrodes on both sides of knee and elbow. In their results, the contribution of the trunk BI index for predicting lean body mass was only 7.1%. This value was considerably different from the substantial percentage of the trunk lean tissue mass to that of the whole body, 
50% (19). We cannot directly compare the contribution of the trunk BI index of the present study to that of the prior study (12) because the reference value (SMVMRI vs. the lean body mass) and the subjects are different. In the present results, however, the contribution of the trunk BI index [(LTR)2/ZTRwhole] indicated a relatively high (32.8%) and closer value to the average in the percentage of the trunk SM volume (41.3 ± 2.7%) to SMVMRI in the pooled data. On the other hand, the percentage of the upper arm SM volume to the SMVMRI was lower (9.3 ± 1.0%) than that of upper leg (33.8 ± 1.9%) and trunk. The SM volume in the upper arm was significantly correlated to that of the trunk (r = 0.888, P < 0.05) and the upper leg (r = 0.816, P < 0.05). Therefore, it may be assumed that the application of the electrode placements that enabled us to obtain Z values from the five regions of the trunk improved the contribution of the trunk BI index for estimating SMVMRI and eliminated the need to enter the upper arm BI index into the prediction equation as the predictive variable.
In estimating the trunk SM volume from the segmental BI analysis, however, the influence of the visceral tissue volume on the accuracy cannot be excluded. Particularly, the visceral tissue volume at 41–50% LTR, which has high conductivity because it is mainly made up of smooth muscle and water, has a low but significant negative correlation between the residual of the trunk SM volume estimates, expressed as a percentage of the trunk SM volume (13). Meanwhile, a regression analysis for the pooled data of this study indicated that the residual of SMVMRI in the segmental BI analysis did not significantly correlate to the percentage of the visceral tissue volume to the SM volume in each part of the trunk (r = –0.259 to 0.011, NS). In contrast to the relatively high percentage of the visceral tissue volume to the total tissue volume (31.0%) in the trunk (13), the corresponding value is 7.1% of the whole body in the pooled data. This relatively low percentage might be assumed to have less influence on the accuracy of the SMVMRI estimation. However, the subjects examined here were healthy young men. With regard to the influence of visceral tissue volume on the estimate of the whole body SM, further investigation using obese and/or elderly individuals is needed.
Before summarizing the present results, we should comment on the limitations of the experimental design in the present study. The sample size was relatively small. Also, only young adult males were examined. In general, the distribution of the SM of females differs from that of males (1). Moreover, the accuracy of the predicting body composition from BI analysis is influenced by the body fat percentage (4) and age (3). Hence, we cannot deny that the accuracy of the equation developed in the present study would vary when subject samples involving females, obesity, and/or elderly are taken for analysis. In addition, the method used to analyze the MRI scans for regional areas was a bit primitive and did not exploit more advanced segmentation software being used in this research field. There remains a possibility that smaller islands of adipose tissue within the skeletal muscle bundle are not fully excluded, as they would be using newer approaches, and so the SM volume might be overestimated. Especially, the use of the software would be heightened to examine the elderly, because they have three times higher accumulation of the intramuscular fat compared with the young men (11). Further study, to clarify the influences of differences in the subject samples and the method used to analyze the MRI scans on the estimate of SM volume, is needed to generalize the findings obtained in the present study.
In summary, the findings obtained here indicated that the validity and cross-validity of the segmental BI analysis that measures Z values from both sides of the upper arm and upper leg, and five regions (both sides of the upper, the middle, and both sides of the lower region) of the trunk was confirmed. On the other hand, the whole body BI analysis significantly overestimated the whole body SM volume. The development of segmental BI technique predicting the whole body SM volume will be of benefit to lean, obese, or long-term hospitalized individuals as well as athletes for evaluating conventionally their own muscularity.
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FOOTNOTES
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Address for reprint requests and other correspondence: N. I. Tanaka, Dept. of Sport System, Kokushikan Univ., 7-3-1 Nagayama, Tama-shi, Tokyo 206-8515, Japan
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.
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ABSTRACT
METHODS
, Sum of the CSAs in both sides of the body; 
, CSAs in right side of the body.
