Estimation equation of limb lean soft tissue mass in Asian athletes using bioimpedance analysis

doi:10.21203/rs.3.rs-1849511/v1

The lean soft tissue mass (LSTM) of the limbs is approximately 63% of total skeletal muscle mass. For athletes, measurement of limb LSTM is the basis for rapid estimation of skeletal muscle mass. This study aimed to establish the estimation equation of limb lean soft tissue mass in Asian athletes using bioimpedance analysis (BIA). A total of 198 athletes (121 males, 77 females; mean age 22.04 ± 5.57 years) from different sports in Taiwan were enrolled. A modeling group (MG) of 2/3 (n = 132) of subjects and a validation group (VG) of 1/3 (n = 68) were randomly assigned. Resistance (R) and reactance (Xc) were measured using 50KHz current measurement in whole-body mode. Predictor variables were height, weight, age, gender, Xc, resistance index (RI; RI = h² / R). Lean soft tissue mass of upper and lower limbs measured by dual-energy X-ray absorptiometry (DXA) was the response variable. Multivariate stepwise regression analysis method was used to establish BIA estimation equations as ArmsLSTM_BIA-Asian and LegsLSTM_BIA-Asian. Measurement equations performance was confirmed by cross-validation. The established single-frequency BIA estimation equation quickly and accurately measures lean soft tissue mass of the arms and legs of Asian athletes.

In the field of sports science, research on the body composition of athletes has developed vigorously in recent years. Many studies have classified athletes by different phenotypes, including shape, weight, body fat percentage, fat mass, fat-free mass (FFM), lean soft tissue mass (LSTM), muscle mass (MM) and other body components. Due to different sports categories, genders, and competitive levels, early research focused on body fat percentage¹. Current related research has been extended to total and regional skeletal muscle mass in athletes. More recently, these measurements, especially LSTM, is related to improving athletic performance and reducing the risk of injury². However, assessments of body composition in individual athletes still need to be targeted as factors for problem-solving among athletes. In this way, individual athletes can obtain corresponding personal benefits when measuring body composition³. This innovative approach goes beyond previous fundamental issues of body composition and emphasizes movement-specific performance. For example, the characteristics of the body composition of arms, legs and torso are directly related to training progress⁴.

Coaches and athletes know that skeletal muscle mass and fat mass are both important aspects of athletic ability. Skeletal muscle mass has traditionally been measured by FFM, and recently it has been focused on LSTM. LSTM represents the positive relationship and contribution of muscle function and strength, thereby improving sports performance⁵. Conversely, fat mass (FM) is considered a non-functional component in the field of exercise biomechanics. As FM increases, it impedes exercise performance and adversely affects thermoregulation⁶. The latest development of body composition technology divides the body into three-components, namely LSTM, FM and bone mineral content (BMC). LSTM includes whole body water, protein, carbohydrates, non-fat lipids and soft tissue minerals⁷. The three-component body composition model can be used with dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), and computerized tomography (CT). Although CT and MRI provide information on cross-sectional areas (CSA) of muscle and muscle volume (MV), these methods are costly, require relatively in-depth research expertise, and analysis of the results can be difficult. Therefore, these methods are not practical in the sports performance setting. Compared with the above methods, although the cost of DXA is lower, it still has limitations in application fields. Another measurement method used commonly in the three-component model is bioimpedance analysis (BIA). Because BIA is convenient, safe, non-penetrating, and fast, it provides athletes with instant information on body composition measurements, and LSTM changes can be tracked conveniently during athlete training.

Since 1990, several important studies have been published on the use of BIA for body composition measurement in athletes^8,9. The measurement, comparison and validation studies of commercial BIA devices in LSTM or FFM for the whole body and limbs of athletes have been proposed and have gradually captured increasing attention^10-12. The physical characteristics of athletes are different from those of the general population based on long-term trends in specific sports¹³. Therefore, special mechanisms must be applied or dedicated regression equations to accurately measure body composition¹⁴. Recently, body composition estimation equations have been proposed to measure the body composition of athletes using BIA to distinguish gender differences^15,16. A recent study by Sardinha et al.¹⁷ proposed a BIA estimation equation for athletes using LSTM for upper and lower limbs, although the subjects of this study were Europeans and Americans. Published research has already shown racial differences in the estimation of body composition by BIA¹⁸. Therefore, the LSTM estimation equation of the upper and lower limbs of athletes established by BIA is applied to measure the body composition of athletes in Asia. Whether applicable or not, it is still necessary to further explore or establish a suitable LSTM estimation equation. Therefore, this study aimed to establish and verify the estimation equation of limb lean soft tissue mass by using BIA in Asian athletes. A single-frequency BIA estimation equation was established to quickly and accurately measure the lean soft tissue mass of the arms and legs of Asian athletes.

A total of 198 male and female athletes participated in this study, including 121 male athletes (basketball: 11, swimming: 18, powerlifting: 8, judo: 10, long-distance running: 4, football: 26, wrestling: 37, track and field: 7), and 77 female athletes (basketball: 7, dance: 10, judo: 15, long-distance running: 2, tug-of-war: 9, soccer: 17, wrestling: 2, track and field: 15). The age, height, weight and body fat percentage of male athletes were 22.67 ± 5.82 years, 174.87 ± 8.25 cm, 74.9 ± 11.87 kg and 15.26 ± 6.83%, respectively. For female athletes, mean age, height, weight and body fat percentage were 21.02 ± 5.00 years, 161.64 ± 6.86 cm, 58.15 ± 9.35 kg and 27.15 ± 8.06%, respectively. For all subjects, MG and VG personal characteristics parameters and body composition data are shown in Table 1.

In the present study, all subjects’ data were entered into the upper limb LSTM equation of Sardhina et al.¹⁷, and compared with the present DXA measurement results. The distribution plots and regression line are shown in Figure 1.a. The LOA and trend line of the two Bland-Altman Plots are shown in Figure 1.b. The same procedure and data were entered into the lower limb LSTM equation and compared with the present DXA measurement results. The distribution plots and regression line are shown in Figure 1.c. The LOA and trend line of the two Bland-Altman Plots are shown in Figure 1.d.

Height, age, sex, resistivity index, reactance, and weight were used as predictor variables in MG. ArmsLSTM was the response variable. Sequentially selected variables entered into the measurement equation using multivariate regression analysis were resistance factor (H²/R), sex (Sex), weight (W), Xc, and height (H). The increase in predictor variables and the corresponding coefficient of determination, standard error of the estimate (SEE), standardized coefficient (β), and variance inflation factor (VIF) are shown in Table 2. The best measurement equation for ArmsLSTM is as follows:

ArmsLSTM_BIA-Asian = 0.096 H²/R – 1.132 Sex + 0.030 W + 0.022 Xc – 0.022 H + 0.905, （r² = 0.855, SEE = 0.757 kg, n = 132,　p ＜ 0.01） (1)

Using the same measurement variables as for MG, LegsLSTM was the response variable, and applying multivariate stepwise regression analysis, the sequentially selected variables entered into the measurement equation were resistance factor (H²/R), H, Sex, W, Age, and Xc. The corresponding coefficients of determination, SEE, VIF, and β are shown in Table 3. The optimal measurement equation of LegsLSTM is as follows:

LegsLSTM_BIA-Asian = 0.197H²/R + 0.120 H – 1.242 Sex + 0.055 W – 0.052 Age + 0.033 Xc – 16.136, (r² = 0.916, SEE = 1.431 kg, n = 132; p < 0.001) (2)

VG data are entered into the formula (1) to obtain ArmsLSTM_BIA-Asian, and compared with the ArmsLSTM_DXA. The scatter diagram of ArmsLSTM_BIA-Asian, ArmsLSTM_DXA, regression line, Bland-Altman Plots and LOA calculation, were drawn, respectively, as Figure 2.a, Figure 2.b. VG data entered into equation (2) to obtain LegsLSTM_BIA-Asian, and its distribution and Bland-Altman Plots are shown in Figure 2.c and Figure 2d, respectively.

For males, females, and overall subjects, the resistance, reactance, and anthropometric parameters corresponded to the equations by Sardhina et al.¹⁷. The data of ArmsLSTM_BIA-Asian, LegsLSTM_BIA-Asian and DXA in this study are shown in Table 4.

The present study established BIA estimation equations of LSTM for upper and lower limbs for Asian athletes in Taiwan. Accordingly, this study is the first to compare the differences and applicability of BIA to LSTM estimation equations for Asian and European athletes. Cross-validation results showed that the BIA estimation equation established for athletes' LSTM in this study has good performance and can be applied practically to the limb LSTM measurement of Asian athletes.

Through the measurement of limb LSTM, measuring whole-body skeletal muscle mass can be estimated indirectly, overcoming the difficulty associated with other whole-body measurement methods. Accurately measuring the LSTM of the limbs is exceptionally important for measuring the whole-body skeletal muscle mass of athletes¹⁹. Therefore, some scholars have conducted validation studies on the LSTM or FMM measurements of athletes' limbs for commercial BIA devices. Brewer et al.¹⁰ used DXA as a standard for comparison among male athletes at Division I College in the United States. The results showed that the LOA of Inbody770 measured by FFM (free fat mas, FFM) of the upper and lower limbs of male athletes was -3.1 to 0.5 kg, -15.3 kg to 2.2 kg, respectively. Female athletes were -1.5 to 0.5 kg, -5.9 to 0.4 kg¹⁰. Raymond et al.¹¹ also compared the DXA of male college football players, and the correlation coefficients between the LOA and the FFM of the upper and lower extremities of the men's athletes in Inbody770 were -0.43 to 3.23 kg (r = 0.82), 0.73 to 9.97 kg (r = 0.78)¹¹. The results of Esco et al.¹² showed that the LOA of Inbody720 in upper and lower extremity FFM of female college athletes was -0.74 to 0.84 kg (r = 0.89) and -3.03 to 2.21 kg (r = 0.83), respectively¹². In addition, Sardinha et al.¹⁷ developed the estimation equation for limb LSTM for single-frequency BIA in athletes. The results of the LSTM estimation equation in the VG of that study were that the LOA and correlation coefficient of the lower limb LSTM were -1.11 to 1.32 kg (r = 0.90) and -3.78 to 3.87 kg (r = 0.94), respectively. Using the resistance, reactance and anthropometric parameters of athletes in Taiwan as entered into the estimation equation of Sardinha et al.¹⁷, the correlation coefficients between the estimated results of ArmsLSTM, LegsLSTM with DXA were r = 0.81 and 0.86, respectively. The LOAs were -2.70 to 2.14 kg and -6.96 to 2.32 kg, respectively. Whether in ArmsLSTM or LegsLSTM, the experimental data in the present study showed a slight difference compared with the measurement accuracy in Sardinha et al.¹⁷.

Stepwise regression analysis was applied in the present study, and the predictor variables used to reflect ArmsLSTM were H²/R, Sex, Xc and H. The predictor variables of LegsLSTM were H²/R, h, Sex, W, Age and Xc. Compared with the predictor variables of ArmsLSTM in Sardinha et al.¹⁷, there was one more H variable, while LegsLSTM had three more variables of H, Age, and Xc. The BIA estimation equations ArmsLSTM_BIA-Asian and LegsLSTM_BIA-Asian obtained had LOA and correlation coefficients in the cross-validation group of -1.53 to 1.43 kg (r = 0.921), -2.68 to 2.90 kg (r = 0.957). Compared with the LSTM estimation equation developed by Sardinha et al., the LSTM estimation equation developed in the present study is not only suitable for Asian athletes, but also has better overall performance in lower limb estimation.

Body composition can be used for the assessment of athletic performance in different groups of athletes, especially in the application of LSTM¹⁴. In women's softball²⁰, men's hockey²¹ and soccer²², the variability of the whole body and limb segment was the least. Among male college basketball players, centers players reflect that their arms and legs have the highest LSTM and FM compared to players in all other positions (with the exception of power forwards)²³. American football players also have the same body composition as men's basketball, with significant differences between the different positions^24,25. Results of these studies suggest that there may be a relationship between limb segments, body composition and exercise programs, positions, and specific functional needs (e.g., shooting, kicking, and speed). This is a research direction that needs to be further explored. In a competitive season of hockey (and likely in football and other sports), the LSTM of the legs of excellent players will increase significantly as the season time increases²⁶. For football players, the LSTMs on the legs and torso are also noted to increase significantly as the season progresses²⁷. These studies have shown that body composition changes over time. Changes in body composition of the whole body or limb region may provide additional information on exercise science. In particular, it can be used to formulate an effective training program for different sports and positions. Therefore, in the future research direction, it is necessary to measure the body composition of each limb, especially the measurement of and research results for LSTM, in addition to the measurement of athletes’ body composition. This prospective observational study applied supine bioimpedance measurements to validate the LSTM reported by Sardinha et al.¹⁷ for extremities. Corrections for resistance and reactance were also performed before the experiment, and the study was replicated with reference to its experimental protocol. However, one limitation of the present study is that uncontrollable differences may still exist. In addition, the study subjects were all elite athletes recruited in Taiwan whose data contributed to formulating the limbs LSTM estimation equations, which limits the extent to which study results can be generalized to other populations. Whether results of the present study are applicable to other races still needs to be verified in further studies.

Single-frequency BIA estimation equations of LSTM for upper and lower limbs of Asian athletes in Taiwan have been established and have demonstrated good performance, allowing them to quickly and accurately measure lean soft tissue mass of the arms and legs of Asian athletes. Nevertheless, BIA estimation equations of the limbs LSTM for Caucasian athletes should be carefully evaluated when applied to the measurement of the limb LSTM of Asian athletes. It is suggested that Asian athletes should apply the BIA estimation equation of the proprietary LSTM for limbs, so that the measurement results can be of practical value.

Study design and sample

The subjects of this prospective observational study were all active high-level athletes in Taiwan. Eligible participants engaged in at least 12 hours of physical or specialty training activities per week. Professional training time was 9.6 ± 2.5 years. Subjects did not drink alcoholic beverages 48 hours before the test, did not use diuretics for 7 days before the test, and did not participate in intense training 24 hours before the test. Females with menstrual periods were excluded. All subjects had no history of nutritional, endocrine or growth disorders and had no more than 5 kg body weight change before the six experiments.

Ethical considerations

This study was conducted in the Department of Radiology, Dali Jen-Ai Hospital, Taichung City. Before the six experiments, the research protocol and experimental procedure were approved by the Human Experiment Committee of Taso-Tun Psychiatric Center, Ministry of Health and Welfare (IRB-11001). Before the tests began, the research assistant explained the experimental precautions to the subjects. Once the subjects understood the experimental process and their rights, they agreed to participate in the experiment and each subject provided signed informed consent. We conducted experiments in accordance with relevant guidelines and regulations.

Procedure and Anthropometry

Three days before the experiment, the subjects were informed of the precautions. The subjects reported to the experimental site at 1:00 p.m. on the day of the experiment, filled out the questionnaire on basic personal information and training process, and changed into cotton robes for testing. The bladder of each subject was emptied 20 minutes before the experiment. Subjects were measured using Tanita BC-418MA (Tanita Co., Tokyo, Japan) with an accuracy of 0.1 kg. A height ruler (Holtain, Crosswell, Wales, UK) was used to measure the subjects' height without shoes, providing accuracy of 0.5 cm. Body mass index (BMI) was calculated by dividing the subjects’ individual body weight by the square of the height in kg/m². A cloth tape measure was used to measure subjects’ waist and hip circumferences. When measuring waist circumference, subjects placed feet together, relaxed the abdominal muscles, placed arms naturally at each side of the body, breathed normally, and allowed measurement of the narrowest position of the body below the ribs and above the navel. Hips were measured at the widest point. Waist and hip circumferences were measured twice and the average values were recorded.

Impedance measurement

Right hand to right foot resistance and reactance measurements were performed using a bioimpedance analyzer QuadScan 4000 (Bodystat, Douglas, UK) at a frequency of 50 KHz. Resistors supplied by the manufacturer were used for calibration every day before the experiment. Bioimpedance measurement is done after confirming that accuracy of the bioimpedance measuring instrument meets the requirements. The subject lies on his/her back on a hospital bed. The right wrist and the back of the right foot were connected with silver electrode patches, which were the pair of receiving and transmitting electrodes, respectively. Subjects lay on their backs calmly for five minutes before the measurement was taken. The resistance index (RI) was defined as the ratio of the square of the height to the measured resistance at a frequency of 50KHz (H²/R). For 5 male and 5 female subjects, impedance measurements were repeated 10 times within one hour of the day. Impedance measurements were performed by 5 subjects for each of the same male and female subjects in the same period for 4 consecutive days. The coefficient of variation (CV = standard deviation/mean x 100%) of impedance measurements was evaluated for within-day and between-day results, respectively. CVs were 0.2%-0.7% and 0.8%-1.6%, respectively.

Dual energy X-ray absorptiometry

Body composition was measured by DXA (Lunar Prodigy; GE Medical Systems, Madison, WI, USA) and the Microsoft software, Encore 2003 Version 7.0. The subjects performed the DXA measurement within thirty minutes immediately after the impedance measurement was completed. Each subject wore a light cotton robe and lay on the measuring bed in a relaxed supine position. The upper limbs were stretched and placed on both sides of the body, with toes facing upward and feet slightly side by side. The procedure took about 20 minutes for each test subject. Complete DXA measurement of body composition included LSTM, FM (fat mass), BMC (bone mineral content) on the whole body, left and right upper limbs, left and right lower limbs, trunk and other parts.

Statistical analysis

All data were expressed as means ± SDs, and range. Data were tested for normal distribution using the Kolmogorov-Smirnov test. All statistical analyses were performed using SPSS Ver.20 (Statistical Package for the Social Sciences, IBM SPSS statistics for Windows; IBM Corp., Armonk, NY, USA). The significance level was set at p < 0.05 (two-tailed).

Paired-t tests were used to compare DXA and BIA on ArmsLSTM and LegsLSTM. The correlation between BIA estimates and DXA-measured ArmsLSTM and LegsLSTM was performed using Pearson's correction. Bland-Altman plots were used to represent the mean difference and limit of agreement (LOA). The regression line was used to represent the trend.

Two-thirds and one-third of the total subjects were randomly divided into a Modeling group (MG) and a validation group (VG), respectively. Height, weight, age, gender, resistance index, and reactance were used as predictor variables, and the LSTM of upper limbs (arms) and lower limbs (legs) measured by DXA were used as response variables, which were expressed as ArmsLSTM_DXA and LegsLSTM_DXA, respectively. Stepwise regression analysis was performed with MG. Set parameters - forward (F_in = 4.00), backward (F_out = 3.99) to obtain the selected predictor variable. The resulting equations were ArmsLSTM_BIA-Asian, LegsLSTM_BIA-Asian and their corresponding regression coefficients, standard estimate error (SEE), coefficient of determination (r²), and predictor variables removed with VIF (variance inflation factor) > 4. The ArmsLSTM_BIA-Asian and LegsLSTM_BIA-Asian obtained by the VG data were brought into the MG, and were analyzed by correlation and Bland-Altman Plots, respectively, to evaluate the effectiveness of the LSTM estimation equation.

Acknowledgements

This work was supported by grants from the Tzu Hui Institute of Technology Research Program (THIT-110004).

Author Contributions

Lai YK and Ho CY performed the research. All the authors contributed to the design of the work and the acquisition and interpretation of data. Hsieh KC performed the statistical analysis and wrote the first draft. Huang AC revised the first draft. All author contributed to the further drafts and approved the submitted version.

Conflicts of Interest

All authors have no conflicts of interest to declare.

IRB Ethics statement

The research protocol and experimental procedure were approved by the Human Experiment Committee of Tsao-Tun Psychiatric Center, Ministry of Health and Welfare (IRB-11001).

Informed consent

Prior to starting the study, the experiments and experimental precautions were explained to the subjects. Once all included subjects understood the experimental process and their rights, each subject provided signed informed consent to participate.

Data Availability

Due to institutional policies governing the data examined in this work, requests for data will be reviewed and must be approved by the local IRB prior to release. Investigators should contact the corresponding author for assistance and guidance with the data request process.

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Table 1. Physical characteristics of the subjects¹

All Subject	Male (n =121)	Female (n =77)	Total (n = 198)
Age(year)	22.67±5.82(17.0, 46.0)	21.02±5.00(17.0, 45.0)	22.04±5.57(17.0, 46.2)
Height(cm)	174.87±8.25(155.9, 198.3)	161.64±6.86(147.1, 181.9)³	169.73±10.07 (147.1, 198.3)
Weight(kg)	74.9±11.87(48.9, 123.9)	58.15±9.35(40.8,84.7)³	68.43±13.68(40.8, 123.9)
BMI(kg/m²)	24.47±3.04(18.9, 36.0)	22.16±2.54(17.7, 29.8)²	23.57±3.06(17.7, 36.0)
R(ohm)	483.4±52.22(339.8, 696.9)	602.48±54.67(452.0, 727.0)³	529.71±83.05(339.8, 724.0)
Xc(ohm)	64.74±8.96(43.4, 92.9)	70.94±7.36(55.8, 86.7)³	67.2±8.88(43.4, 92.9)
Arms LSTM(kg)	6.99±1.38(2.7, 11.1)	3.79±0.82(2.6,7.5)³	5.74±1.97(2.6, 11.1)
Legs LSTM(kg)	22.55±3.42(12.9, 33.7)	15.03±2.74(10.9,27.5)³	19.62±4.85(10.9,33.7)
Trunk LSTM(kg)	26.43±3.63(14.5-41.7)	17.75±2.05(13.8,27.1)³	22.82±5.26(13.8, 41.7)
BF%	15.26±6.83(5.10-34.0)	27.15±8.06(9.40,46.5)³	20.23±9.41(5.1, 46.5)
WHR	0.82±0.05(0.72,0.96)	0.80±0.06(0.65,0.94)³	0.81±0.05(0.65,0.96).
Modeling Group	Male (n =70)	Female (n =52)	Total (n = 132)
Age(year)	22.94±6.11(17.0, 46.0)	20.88±4.98(17.0, 42.0)²	22.13±5.76(17.0, 46.0)
Height(cm)	174.81±7.83(157.3, 198.3)	160.85±6.38(150.3, 181.9)³	169.31±9.98(150.5, 198.3)
Weight(kg)	73.71±9.93(56.9,123.1)	57.39±8.70(40.8, 84.7)³	67.28±12.37(40.8, 123.1)
BMI(kg/m²)	24.11±2.66(18.9, 36.0)	22.11±2.54(17.7, 29.8)²	23.32±2.78(17.7, 36.0)
R(ohm)	483.84±58.79(339.8,679.5)	602.32±49.75(506.2, 704.7)³	530.51±80.16(339.8, 704.7)
Xc(ohm)	65.20±8.81(43.4, 89.9)	70.70±7.53(55.8, 86.8)³	67.34±8.73(43.4, 89.9)
Arms LSTM(kg)	6.92±1.27(2.6, 9.4)	3.75±0.67(2.6, 6,8)³	5.67±1.89(2.6, 9.4)
Legs LSTM(kg)	22.5±2.97(15.4, 32.6)	14.78±2.35(10.9, 25.7)³	19.43±4.65(10.9, 32.6)
Trunk LSTM(kg)	26.33±3.57(14.5,41.7)	17.72±2.14(13.9,27.1)³	22.73±5.23(13.9,41.7)
BF%	15.23±7.09(5.4,33.7)	27.51±8.51(9.4,46.5)³	20.36±9.80(5.4,46.5)
WHR	0.81±0.05(0.72,0.96)	0.79±0.06(0.69,0.92)³	0.81±0.05(0.69,0.96)
Validation Group	Male (n =41)	Female (n =25)	Total (n = 66)
Age(year)	22.16±5.25(18.0, 46.0)	21.32±5.15(17.0, 45.0)²	21.84±5.19(17.0, 46.0)
Height(cm)	175.00±9.13(155.8, 194.6)	163.28±7.63(147.1, 178.1)³	170.56±10.28(147.1, 194.6)
Weight(kg)	77.42±18.80(48.9, 119.1)	59.72±10.60(45.2, 81.2)³	70.72±15.85(45.2, 119.1)
BMI(kg/m²)	25.16±3.60(19.6, 33.7)	22.25±2.58(18.5, 29.4)²	24.06±3.53(18.5, 33.7)
R(ohm)	482.56±69.17(355.2, 696.9)	602.80±64.84(452.0, 724.0)³	528.10±89.17(355.2, 724.0)
Xc(ohm)	63.84±9.28(43.4, 92.8)	71.44±7.09(56.8, 83.7)³	66.72±9.24(43.4, 92.8)
Arms LSTM(kg)	7.12±1.59(3.0, 11.1)	3.87±1.07(2.6, 7.5)³	5.89±2.12(2.6, 11.1)
Legs LSTM(kg)	22.72±4.21(12.9, 33.7)	15.54±3.40(11.9, 27.5)³	20.00±5.25(11.9, 33.7)
Trunk LSTM(kg)	26.62±3.78(19.7,36.8)	17.79±1.91(13.8,23.0)³	22.93±5.38(13.8,36.8)
BF%	15.32±6.35(5.10,34.0)	26.43±7.14(13.5,40.7)³	19.96±8.64(5.1, 40.7)
WHR	0.82±0.04(0.74,0.84)	0.80±0.06(0.65,0.94)³	0.81±0.05(0.65,0.94)

¹All values are x ± SD; minimum and maximum in parentheses.

^2,3 Significantly different from male (one-factor ANOVA): ²P=0.005,³P<0.001

BMI, body mass index; R, resistance; Xc, reactance, LSTM, Lean soft tissue mass, WHR, Waist-to-hip ratio;

Table 2. Multiple regression analysis results for ArmsLSTM, Based on bioimpedance index and anthropometric

ArmsLSTM, Modeling Group (n = 132)
h²/R	+Sex	+Weight	+Xc	-	+h	Intercept	SEE	r²	VIF	β
-.131±.005^**	-	-	-		-	-1.663±.270^**	.878	.802	2.27	.659
.100±.006^**	-1.147±.171^**	-	-		-	-.547±.418	.797	.837	2.39	-.281
.094±.426^**	-1.184±.171^**	.026±.007^**	-		-	.094±.426^*	.775	.847	3.57	.211
.087±.010^**	-1.102±.172^**	.025±.007^**	.019±.008^**		-	-1.742±.871	.765	.848	1.66	.098
.096±.011^**	-1.132±.171^**	.030±.007^**	.022±.008^**		-.022±.010^**	.905±1.462^*	.757	.855	3.29	-.112

ArmsLSTM, Arms lean soft tissue mass; Regression coefficient estimate ± SEE; r², coefficient of determination; p*<0.01; p**<0.001; β, standardized coefficient; VIF: variance inflation factor; SEE, standard error of the estimate; h, height

Table 3. Multiple regression analysis results for LegsLSTM, Based on bioimpedance index and anthropometric

LegsLSTM, Legs lean soft tissue mass; Regression coefficient estimate ± SEE; r², coefficient of determination; p*<0.01; p**<0.001; β, standardized coefficient; VIF: variance inflation factor; SEE, standard error of the estimate; h, height

Table 4. Comparison of upper and lower limb lean muscle mass between bioimpedance analysis and DXA

Method	Male (n =121)	Female (n =77)	Total (n = 198)
		ArmsLSTM
DXA	6.99±1.38(2.69, 11.06)	3.79±0.82(2.6,7.5)	5.74±1.97(2.6, 11.1)
Sardinha[17]	5.93±1.13(3.03,9.75)³	4.67±0.76(3.52, 7.37)³	5.44±1.18(3.14,7.,79)²
Asian	6.92±1.06(4.25, 10.30)	3.74±0.65(2.72, 6.14)	5.74±1.81(2.14,9.92)
		LegsLSTM
DXA	22.55±3.42(12.86, 33.70)	15.03±2.74(10.9,27.5)	19.62±4.85(10.9,33.7)
Sardinha[17]	18.58±2.91(11.09,29.11)³	15.11±2.30(11.24,22.97)³	17.33±3.06(11.34,23.45)³
Asian	22.62±3.14(14.12,31.72)	15.03±2.74(10.98, 27.49)	19.62±4.64(10.61,28.98)

¹ All values are x±SD; minimum and maximum in parentheses.

^2,3 Significantly different from male (one-factor ANOVA): ²P=0.005,³P<0.001

No competing interests reported.

Estimation equation of limb lean soft tissue mass in Asian athletes using bioimpedance analysis

Status:

Version 1

Abstract

Figures

Introduction

Results

Discussion

Conclusion

Materials And Methods

Declarations

References

Tables

Additional Declarations

Status:

Version 1