Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults.

BACKGROUND: Bioelectrical impedance vector analysis (BIVA) has been used for prediction of muscle performance. However, little is known about BIVA in Asian adults, and even less is known about using standing BIVA devices. Standing impedance analyzer allows quicker and more convenient way to gather data than conventional supine analyzer and is more suitable for clinical practice. This study aimed to investigate the relations between muscle function and BIVA parameters measured with a standing impedance analyzer in healthy Taiwanese adults.
METHODS: A total of 406 healthy subjects (age 34.5 ± 17.3 years, body mass index 24.1 ± 4.1 kg/m2) were recruited for BIVA and handgrip strength (HGS) measurements. Impedance parameters, including resistance (R) and reactance (Xc), were measured and normalized to body size by dividing by height (H). The resulting phase angle (PhA) was calculated. HGS in the dominant, left, and right hands were referred to as HGSDH, HGSLH, and HGSRH, respectively. All subjects were divided into 5 grades according to HGS.
RESULTS: Muscle strength in the dominant, right, and left arms was correlated with variables in the order of sex, weight, age, height, Xc/H, and R/H (all, p < 0.001). Using all 6 variables, the determination coefficients were 0.792, 0.782, and 0.745, respectively, whereas the standard errors of estimates were 56.89, 58.01, and 56.67 N for HGSDH, HGSLH, and HGSRH, respectively. HGS was positively correlated with PhA, and negatively correlated with Xc/H and R/H.
CONCLUSIONS: BIVA parameters measured with a standing impedance analyzer and anthropometric variables can predict and discriminate muscle function with good performance in healthy Asian adults.

Introduction

Skeletal muscle is the largest tissue in human body which accounts for 30% of body weight in women and greater than 40% of body weight in men [1, 2]. It controls physical activity through the generation of force and plays a major role in human health. The health of skeletal muscle is determined by its mass and function, and is regulated by skeletal muscle protein synthesis and breakdown [3]. Imbalance of the dynamic process of skeletal muscle protein metabolism in response to pathologic conditions and chronic disease may affect muscle mass and function [4]. Skeletal muscle function can be expressed in terms of muscle power, muscle strength, and local muscle endurance [5, 6]. Methods for muscle function evaluation include manual muscle testing, electrophysiological studies, and a handheld dynamometer. Handgrip strength (HGS) measurement using a dynamometer is a relatively inexpensive, portable, and simple method which provides information about overall muscle function [7]. The sex- and age-specific reference curves for HGS are well-established for healthy children and adults [8-10]. These reference curves provide normative values for physical fitness in general populations. Deviation of HGS values from the reference values may indicate disease [11] or the aging process [12, 13]. In the general population, a lower HGS is associated with higher risk of mortality and morbidity [11, 12].

Malnutrition is defined as a state resulting from lack of uptake or intake of nutrients. According to global consensus statements [14, 15], HGS is one of the four recommended types of measurements to assess nutritional state [14]. Studies have shown that lower HGS is associated with a longer length of hospital stay [16, 17] and higher mortality rate in critically ill patients [18]. HGS has also been used to monitor the outcome of nutritional intervention [19, 20]. Since muscle function may be altered prior to a change in muscular volume during disease progress or intervention, HGS is a more sensitive indicator to changes in nutritional status compared to body composition analysis [21, 22].

Bioelectrical impedance analysis (BIA) is a commonly used method for body composition analysis [23] and has been accepted as an option for estimating skeletal muscle mass [24, 25]. Another approach of BIA is bioelectrical impedance vector analysis (BIVA) introduced by Piccoli et al. [26]. Impedance analyzer measures tissue electrical properties such as resistance (R) and reactance (Xc) using an alternating current. Body fluids are highly conductive, and the resistance of the conductive fluids is defined as R [27]. Xc is the opposition to current flow due to the capacitive nature of cell membranes. Human cells are surrounded by phospholipid bilayers and act as an electrical insulator and capacitor. Therefore, Xc reflects the integrity of cell membranes, which is correlated with body cell mass [26, 28]. Phase angle (PhA) reflects the relationship of R and Xc, and is calculated as PhA = arctan(Xc/R) [29]. Impedance (Z) is a function of two impedance components, R and Xc, and is calculated as Z2 = R2 + Xc2 [28]. If the two height-normalized impedance components (R/H and Xc/H) are plotted as a bivariate vector in the RXc graph, the length of the vector (Mahalanobis distance) is related to hydration status [26]. Therefore, BIVA may be a one-stop shop solution for skeletal muscle health, providing information on both skeletal muscle mass and function. Currently, BIVA is a validated tool for assessing hydration and nutritional status [30] and has also been validated as a predictor for sports performance [31, 32] and muscular fitness [33].

Currently, BIVA measurements are typically made with subjects in a supine position, and data are acquired at the whole-body level. Moreover, there is very little published BIVA data on muscle function in healthy Asian adults. Indeed, standing impedance analyzer is a more attractive model in research and clinical settings owing to its simple and convenient measure. However, few studies have investigated BIVA data with subjects in standing position. Furthermore, there is a need to validate segmental BIVA for exploring muscle health in individual extremity. Consequently, the objective of this study was to investigate a modified standing impedance analyzer as a tool for skeletal muscle function at whole-body and segmental levels using HGS as a reference method in healthy Taiwanese adults.

Materials and methods

This cross-sectional study was approved by the Institutional Review Board of the Jen-Ai Hospital (No. IRB-97-01). Written informed consent was obtained from all subjects. All experiments were conducted at the Jen-Ai Hospital in the Taichung, Taiwan between January 2016 and May 2017.

Subjects

Subjects were recruited by community advertisements. Inclusion criteria were healthy Taiwanese adults 18 to 80 years of age. Exclusion criteria were individuals with a pacemaker, metal implants, limb deformities, upper limb neuropathies or arthropathies, those taking medications for chronic conditions and taking vitamin supplements long-term, and those with a history of alcohol abuse and systemic diseases, e.g., malignancy, diabetes, hypertension, hypo- or hyperthyroidism, cardiovascular disease. A total of 406 subjects who met the inclusion criteria and completed each measure were included in the final analysis.

Study design

All subjects were asked to refrain from alcoholic beverages for at least 48 hours and avoid diuretics for 7 days prior to study. Female subjects were not scheduled during menstruation. On the test day, subjects were registered between 1 pm and 5 pm after fasting for 4 hours and were instructed to void, remove all objects which may affect the exam, and change into a light cotton gown prior to measurements.

Anthropometric measurements

Height was measured to the nearest 0.1 cm using a mechanical device (Stadiometer, Holtain, Crosswell, Wales, UK), and weight was measured to the nearest 0.1 kg using an electronic scale (BC-418MA, Tanita Corporation, Tokyo, Japan) by skilled operators with subjects not wearing shoes. Technical errors for height and weight measurements were 0.021% and 0.520%, respectively. Body mass index (BMI) was calculated as weight (kg) divided by height squared (kg/m2).

Body composition measurements

Body composition measurements were acquired using a DXA scanner (GE, Lunar Prodigy, USA) by experienced radiology technicians. For the examination, subjects were placed supine on the scanning table with the upper limbs stretched and placed flatly on the side of the body, with the feet slightly parallel and the toes facing upwards. The total scan time was approximately 20 min. Regional cut lines were placed using enCore Version 7.0 software according to the manufacturer’s protocol. The lean body mass and body fat percentage of the whole body, right arm, and left arm were obtained.

Handgrip strength

A digital handgrip dynamometer (MG4800, Charder Electronical Co., Ltd., Taichung, Taiwan) was used measure HGS, after subjects were given verbal instructions and a brief demonstration. Subjects were instructed to stand upright with their shoulder adducted and neutrally rotated, elbow fully extended, and forearm and wrist neutrally positioned during the study. When correctly positioned, 3–5 second maximum grip strengths were obtained twice for each hand. No verbal encouragement was given during the test. The average values of the 2 trials in the dominant hand, right hand, and left hand were calculated, and represented as HGSDH, HGSRA, and HGSLA, respectively.

To ensure the accuracy of the test, all testers were trained in the test procedures and calibration procedures, and instrument calibration data were recorded to ensure reproducibility of the test. All testers practiced the testing procedure in a subgroup of 35 subjects (age 35.2 ± 12.3 years, body weight 68.3 ± 10.2 kg, height 1.65 ± 0.1 m, BMI 23.9 ± 3.3 kg/m2) prior to the study assessments. The test-retest reliability intra-class correlation coefficient (ICC) for HGS was r = 0.98 (95% CI: 0.93, 0.99). For criterion-related validity, the MG4800 dynamometer was validated against the standard Jamar dynamometer (J. A. Preston Corporation, Clifton, NJ). The results produced by the 2 devices were highly correlated (r = 0.954 by ICC), and strongly in agreement (bias = 12.0 N, limit of agreement = -58.5 to 85.5 N by Bland-Altman Plot).

Impedance measurements

BIA measurements were carried out with the subject standing on a modified Quadscan 4000 (Bodystat Ltd, Doubles, Isle of Man, UK) with circuit switching switches and measuring lines [34]. The device was calibrated at the beginning of each day using a 500 ohm test resistor provided by the manufacturer, with R and Xc variations within 1% (R = 500 ± 5 ohm, Xc = 0 ± 5 ohm).The reliability and validity of the measuring device have been previously verified [34, 35].

The R, Xc, Z, and PhA for each subject were measured at a single frequency (50 kHz) with 3 modes: whole body (WB), right arm (RA), and the left arm (LA) modes (). The method of BIVA was developed by Professor Antonio Piccoli in 1994 [26]. R and Xc were normalized to height (H), and expressed as R/H and Xc/H, respectively [26]. Then, R/H and Xc/H were used to plot a bivariate RXc graph. The 95% CIs, which represent the mean vector distribution, were calculated for the HGS measurements in the different groups. PhA was defined as arctan(Xc/R).

Statistical analysis

We determined that this study needed a minimum sample size of 374 subjects for six estimate variables, using an effect size of 0.035 (f2, medium), a 0.05 probability of error, and a power of 0.95 (1 − β error probability). All statistical analyses were performed using SPSS version 19.0 software (SPSS Inc., Chicago, IL, USA). Data were expressed as mean ± standard deviation (SD). The ICC was used to evaluate test-retest reliability. Repeated-measures ANOVA was used to test differences among group means between men and women. Pearson correlation (r) was used to assess the correlation between 2 variables. Stepwise regression analysis was used to fit possible regression models of muscle strength using sex, body weight, age, height, Xc/H, and R/H as independent variables (Fin = 4.00, Fout = 3.99). Vector plots and analyses were performed using BIVA Software 2002 [36]. Mean vector lengths of groups were tested using the Hotelling's T-squared test in the BIVA Software 2002. A value of p < 0.05 was considered to indicate statistical significance.

Results

The demographic characteristics of the 406 subjects included in the study are presented in Table 1. Impedance parameters, with and without adjustment for height, are shown for the whole body and segmental levels. Mean age of the subjects was 34.5±17.3 years, and mean BMI was 24.1±4.1 kg/m2. Of the 406 subjects, 235 (58%) were men. Anthropometric indices, HSG, and impedance parameters were significantly greater for men compared to women, except for age and BMI. The majority of subjects were right-handed (94.3%). The mean HGS of the dominant hand was 366.8 ± 108.0 N for men and 251.0 ± 58.5 N for women. The correlation coefficients between muscle mass and function in the dominant hand, right arm, and left arm were 0.866, 0.810, and 0.823, respectively (all, p < 0.001).

Table 1

Subject demographics.

	Total (n = 406)	Female (n = 171)	Male (n = 235)
Age (year)	34.5±17.3 (18.7, 79.0)	36.9±18.7 (18.8, 79.8)	32.8±15.4 (18.7, 79.8)
Height (m)	1.68±0.10 (1.45, 1.97)	1.61±0.06 (1.46, 1.74)	1.74±0.08 (152, 197)**
Weight (kg)	67.9±14.1 (42.0, 120.0)	60.8±11.9 (42.0, 106.0)	75.3±12.2 (45.0, 120.0) **
BMI (kg/m2)	24.1±4.1 (16.2, 39.9)	23.5±4.6 (16.2, 38.0)	24.6±3.6 (16.3, 39.9)
HGSDH (N)	366.8±108.0 (86.3, 601.7)	251.0±58.5 (127.5, 402.5)	482.7±82.8 (188.7, 721.0) **
HGSRH (N)	363.5±108.2 (86.3, 601.7)	249.5±58.5 (127.5, 388.2)	482.7±82.8 (188.7, 721.0) **
HGSLH (N)	337.8±99.1 (78.2, 552.3)	231.4±59.3 (112.2, 402.5)	437.7±77.7 (178.9, 638.6) **
ZWB (ohm)	559.6±99.4 (376.7, 908.6)	650.0±96.0 (476.7, 908.6)	514.0±63.7 (376.7, 671.7) **
ZRA (ohm)	312.5±68.0 (203.5, 509.4)	379.4±61.8 (263.6, 509.4)	278.8±40.9 (203.5, 389.9) **
ZLA (ohm)	319.4±70.7 (194.9, 522.3)	386.8±64.7 (268.2, 522.2)	285.5±44.6 (194.9, 423.7) **
RWB	556.5±99.4 (374.6, 905.8)	647.8±96.0 (474.2, 905.8)	510.7±63.4 (374.6, 668.1) **
RRA	311.0±68.1 (202.5, 508.0)	378.1±61.8 (262.5, 508.0)	277.3±40.7 (202.5, 388.4) **
RLA	318.0±70.7 (193.9, 520.9)	385.5±64.8 (267.1, 520.9)	284.0±44.5 (193.9, 422.2) **
XcWB	58.1±7.7 (40.1, 79.6)	58.4±7.2 (44.9, 79.6)	57.9±8.0 (40.1, 78.9) **
XcRA	30.1±4.3 (19.9, 40.2)	31.7±3.9 (23.4, 40.0)	29.3±4.3 (19.9, 40.2) **
XcLA	29.9±4.2 (19.4, 41.2)	31.0±3.9 (23.0, 40.3)	29.3±4.2 (19.4, 41.2) **
RWB/H (ohm/m)	332.1±68.2 (214.6, 595.9)	401.9±62.9 (283.3, 595.9)	297.1±36.7 (214.6, 370.7) **
RRA/H (ohm/m)	185.8±45.8 (116.1, 334.2)	236.4±39.9 (158.1, 334.2)	161.3±23.6 (116.1, 218.2) **
RLA/H (ohm/m)	189.9±47.2 (111.1, 342.7)	239.1±41.6 (160.9, 342.7)	165.2±25.6 (111.1, 237.2) **
XcWB/H (ohm/m)	34.6±5.0 (22.5, 48.2)	36.3±4.9 (26.4, 48.2)	33.7±4.8 (22.5, 46.5) **
XcRA /H (ohm/m)	18.0±2.9 (11.1, 25.1)	19.7±2.7 (13.8, 25.1)	17.1±2.6 (11.1, 24.5) **
XcLA /H(ohm/m)	17.8±2.8 (11.1, 25.1)	19.2±2.7 (14.1, 24.4)	17.0±2.5 (11.1, 25.1) **
PhAWB (˚)	6.0±0.8 (3.5, 7.2)	5.2±0.6 (3.9, 6.4)	6.2±0.5 (4.9, 7.2) **
PhARA (˚)	5.5±0.9 (4.1, 8.6)	4.8±0.6 (3.5, 6.1)	6.0±0.7 (4.5, 8.2) **
PhALA (˚)	5.4±0.9 (3.8, 8.2)	4.6±0.7 (3.5, 6.1)	5.9±0.7 (4.9, 8.6) **
LeanWB (kg)	48.0±11.6 (24.7, 82.3)	37.2±4.7 (24.7, 53.4)	55.8±8.0 (33.4, 82.2) **
LeanRA (kg)	2.7±0.9 (1.1, 5.2)	1.8±0.4 (1.1, 3.2)	3.3±0.6 (1.6, 5.1) **
LeanLA (kg)	2.6±0.9 (1.1, 4.9)	1.8±0.5 (1.1, 3.4)	3.2±0.6 (1.7, 4.9) **
BF% (%)	25.3±11.4 (5.1, 54.3)	33.1±9.4 (10.4, 54.3)	19.6±9.2 (5.1, 40.4) **

a Data are expressed as mean ± standard deviation (min, max).

b HGS, hand grip strength; Z, impedance; R, resistance; Xc, reactance; R/H, resistance standardized for height; Xc/H, reactance standardized for height; PhA, phase angle; Lean, lean body mass; BF%, percentage body fat

*, p < 0.05

**, p < 0.001.

c Subscript DH, RH, LH, WB, RA, and LA denote dominant head, right hand, left hand, whole body, right arm and left arm, respectively.

Possible associations between BIVA variables PhA, R/H, and Xc/H obtained using whole body and segmental modes were examined (Table 2). All BIVA variables obtained from whole body and segmental modes were very strongly correlated in both sexes (r = 0.910–0.985), except for PhA in males (r = 0.852–0.897). The best association was found with R/H (r = 0.943–0.985). In general, the correlations between whole body and right arm modes were better than the correlations between whole body and left arm modes for all the BIVA variables.

Table 2

Regression analysis of impedance parameters using whole body and segmental modes.

	Dependent Variable	Independent Variable	Intercept	Coefficient	r2	SEE
Total (n = 406)	PhWB	PhARA	0.686±0.127**	0.939±0.022**	0.960	0.240
		PhALA	1.217±0.142**	0.871±0.025**	0.941	0.290
	RWB/H	RRA/H	64.055±3.784**	1.447±0.020**	0.985	11.885
		RLA/H	66.924±4.291**	1.400±0.022**	0.981	13.560
	XcWB/H	XcRA/H	5.434±0.923**	1.623±0.051**	0.962	0.755
		XcLA/H	1.098±1.001**	0.929±0.056**	0.931	1.829
Female (n = 171)	PhWB	PhARA	0.744±0.228**	0.915±0.047**	0.939	0.210
		PhALA	1.557±0.219**	0.777±0.046**	0.920	0.241
	RWB/H	RRA/H	40.748±11.247**	1.539±0.047**	0.976	13.481
		RLA/H	50.328±12.174**	1.469±0.050**	0.971	14.901
	XcWB/H	XcRA/H	2.154±1.859*	1.734±0.094**	0.933	1.779
		XcLA/H	3.714±2.099*	1.693±0.108**	0.910	2.051
Male (n = 235)	PhWB	PhARA	1.339±0.252**	0.838±0.040**	0.897	0.243
		PhALA	1.992±0.276**	0.751±0.045**	0.852	0.288
	RWB/H	RRA/H	65.642±6.662**	1.443±0.042**	0.959	10.731
		RLA/H	79.702±7.506**	1.322±0.046**	0.943	12.650
	XcWB/H	XcRA/H	3.243±1.006**	1.784±0.058**	0.948	1.537
		XcLA/H	3.443±1.166**	1.776±0.068**	0.932	1.762

Data are presented as regression coefficient estimate ± standard error of estimate.

b r2, coefficient of determination.; H, height; PhA, phase angle; R, resistance; Xc, reactance; R/H, resistance standardized for height; Xc/H, reactance standardized for height; Subscript RA, LA, WB = right arm, left arm, whole body, respectively.

*, p < 0.05

**, p < 0.001.

The results of the multiple regression analyses for HGS using basic indices and height-adjusted BIVA variables (R/H and Xc/H) as predictors are shown in Table 3. The variables were included in the stepwise regression analysis in the following order: sex, weight, age, height, Xc/H, and R/H. Model 1 was the regression model to predict HGSDH from basic indices and whole-body mode BIVA parameters. Model 2 was the regression model to predict HGSRH based on basic indices and right hand mode BIVA parameters. Model 3 was the regression model to predict HGSLH from basic indices and left hand mode BIVA parameters. The variance inflation factor (VIF) values were all < 10 (range 1.25–8.26), indicating no multicollinearity. The correlation coefficients between HGS and Xc/H in the whole body, right arm, and left arm modes were 0.663, 0.690, and 0.651, respectively. The correlation coefficients between muscle strength and R/H in whole body, right arm, and left arm modes were 0.773, 0.775 and 0.747, respectively. In general, Xc/H was a better predictor for muscle strength than R/H in all 3 modes.

Table 3

Multiple regression analyses for predicting handgrip strength in the dominant, right, and left hands.

Cumulative dependent variables used in model
Whole body mode for predicting HGSDH
Sex	Weight	Age	H	Xc/H	R/H	Intercept	SEE	r2	VIF	β
192.16±10.66**	-	-	-	-	-	294.83± 7.55**	74.79	0.625	3.45	0.42
149.40±10.89**	2.96±0.39**	-	-			114.72± 24.39**	65.67	0.712	2.82	0.23
141.01±10.04**	3.08±0.35**	-1.91±0.30**	-	-	-	166.37± 23.75**	60.00	0.761	1.48	-0.11
118.38±11.97**	2.60±0.37**	-1.55±0.31**	2.18±0.66**	-	-	-165.73±103.70	58.53	0.774	2.66	0.22
126.38±13.79**	2.82±0.42**	-1.50±0.32**	2.23±0.66**	0.78±0.67*	-	-232.56±118.42	57.47	0.784	6.69	0.29
102.90±15.06**	1.95±0.48**	-.96±0.35**	2.65±0.66**	3.42±1.01*	-0.66±0.19**	-1 48.70±117.77	56.89	0.792	7.26	-0.38
Right arm mode for predicting HGSRH
191.68±10.81**	-	-	-	-	-	291.48±7.66**	75.82	0.617	3.49	0.40
150.38±11.19**	2.86±0.39**	-	-	-	-	117.54±25.07**	67.51	0.698	2.49	0.18
141.73±10.31**	2.98±0.36**	-1.97±0.31**	-	-	-	170.86±24.39**	61.62	0.750	1.25	-0.14
116.69±12.24**	2.45±0.38**	-1.58±0.32**	2.41±0.68**	-	-	-196.57±106.03	59.84	0.765	2.64	0.23
113.27±15.29**	2.39±0.41**	-1.57±0.32**	2.41±0.68**	0.28±0.74*	-	-181.21±113.88	59.97	0.766	5.60	0.18
96.44±15.45**	1.51±0.46**	-1.22±0.33**	2.83±0.67**	2.26±.98*	-0.94±0.25**	-101.23±112.17	58.01	0.782	7.21	-0.34
Left arm mode for predicting HGSLH
170.26±12.42**	-	-	-	-	-	267.28±7.10**	70.29	0.773	3.55	0.42
134.51±10.56**	2.47±0.37**	-	-	-	-	116.71±23.64**	63.65	0.671	2.58	0.18
126.66±9.79**	2.59±0.34**	-1.78±0.29**	-	-	-	165.05±23.18**	58.55	0.719	1.25	-0.16
111.33±.998**	2.26±0.37**	-1.54±0.32**	1.48±0.66*	-	-	-59.87±102.26	57.95	0.730	2.71	0.18
109.44±14.69**	2.23±0.40**	-1.55±0.31**	1.48±0.66**	-0.15±0.67*	-	-52.12±108.69	58.09	0.730	4.93	0.16
92.39±15.21**	1.43±0.46*	-1.25±0.32**	1.98±0.66*	1.75±0.87*	-0.76±0.23**	-102.47±107.26	56.67	0.745	6.76	-0.31

a Data are presented as regression coefficient estimate ± standard error of estimate.

b SEE, standard error of estimate; r2, coefficient of determination; VIF, variance inflation factor; β, standardized coefficient.

c Using all 6 variables, the determination coefficients were 0.792, 0.782, and 0.745, respectively, whereas the SEE were 56.89, 58.01, and 56.67 N for the dominant, right, and left arms, respectively.

d *, p < 0.05

**, p < 0.001.

The subjects were divided into 5 equal groups (group I to V) depending on their HGS level, with group V representing the lowest quintile. Graphical comparisons of impedance vectors and confidence ellipses are shown in Fig 2: HGSDH using whole body mode (Fig 2A); HGSRH using right arm impedance measuring mode (Fig 2B); HGSLH using left arm mode (Fig 2C). A significant displacement of the vector was observed between groups with increasing HGS in all 3 models (p = 0.0001–0.0112 in the whole body model, p = 0.0001–0.002 in the right arm model, p = 0.0001–0.0245 in the left arm model). With increasing level of HGS, a decreasing PhA was also noted in all 3 models ().

Fig 2

The RXc graph with 95% confidence ellipses for the handgrip strength quintile groups.

(a) Whole body model (b) Right hand model (c) Left hand model. a Hand grip strength (HGS) is presented as mean ± standard deviation. b R, resistance; H, height; Xc, reactance; T2, Hotelling's T-squared test; D, Mahalanobis distance; N, newton.

Discussion

The results of this study showed that HGS can be predicted by BIVA parameters of the same limb, and the whole body using a modified standing impedance analyzer. Standing BIA analyzers have attracted a growing interest due to their convenience; however, there are concerns about impedance variability due to fluid shift toward the leg during the day [37, 38]. Our study provides evidence that standing BIVA can be used to predict and discriminate muscle function in healthy adults. BIVA references for the healthy Asian adult population are limited because most BIVA studies have been conducted with Caucasian subjects [26, 31, 39–43]. The current study fills this knowledge gap by providing references ranges for the Asian population.

Compared to body composition analysis, vector analysis uses height-adjusted raw impedance components (R/H and Xc/H), and involves fewer assumptions and is free of prediction equations for total body water or fat free mass [32]. Therefore, vector analysis should exhibit less error than body composition analysis using an impedance analyzer, making it a more valuable tool. However, BIVA approach involves the use of raw bioelectrical impedance parameters and thus it is still sensitive to variability in tissue electric properties, such as body position, hydration status, electrolyte concentration, exercise, skin temperature and phase of menstrual cycle [28, 44–46]. This study was performed to test the potential application of BIVA in assessing skeletal muscle function and had the advantage of testing on healthy subjects under a careful control of hydration status.

The most common application of bioelectric impedance measure is whole body mode, which measures the total body electrical parameters with electrodes placed on the ipsilateral arm and foot. Whole body BIVA result is well-known to correlate with many diseases, such as renal diseases, critically ill patients, obesity, sarcopenia, and cachexia [40, 41, 47–49]. However, whole body mode regards the body as a uniform cylinder and this assumption is not entirely correct as the human body has a complex shape [28]. Furthermore, this method cannot provide information about body segment individually, limiting its application.

Segmental body measure of bioelectric impedance is a less common application, which can be acquired by two methods. For the bioelectric impedance of the arm, segmental impedance components can be acquired using two pairs of electrodes attached to the ipsilateral hand and shoulder [49]. The distance between receiving electrodes is then used for adjusting the impedance parameters. This method has been used to assess the skeletal muscle of right arm in Alzheimer’s disease and healthy control, showing a lower phase angles and longer specific vectors in patients with Alzheimer’s disease [50]. However, this approach involves complicated process for electrode placement and is less practical in large scale study. Another segmental approach is proposed by Kushner R.F. [28] who made the assumptions that the human body is composed of five cylinders (two upper limb cylinders, one trunk cylinder and two lower lib cylinders) and tissue electric properties in each cylinder can be directly measured. For BIVA of the arm, impedance components are measured with electrodes placed on the ipsilateral arm and foot, and voltage electrodes on both hands [28, 51], as done in our study. With this method, the measured values of Xc and R are divided by standing height, which assumes a fixed proportion of limb length to body height. This approach is simple and convenient but less discussed.

Maximum handgrip force is mainly determined by the muscle function of the upper limb; therefore, HSG should be associated with BIVA of the ipsilateral limb rather than BIVA of the contralateral limb or the whole body BIVA. Interestingly, whole body Xc/H and R/H showed similar performance in predicting HGS compared to segmental Xc/H and R/H of the same limb. This may due to the very strong correlation between whole body and segmental Xc/H and R/H in our healthy subjects. However, our results may not be applicable to individuals with diseases, or the general, non-Asian population. Further research is required to explore the used direct measurement of segmental BIVA for the evaluation of regional muscle function.

In this study, a significant migration of the mean vectors with increasing HGS was observed due to decreased Xc/H and R/H in the healthy adults. A similar finding was reported in a study of healthy young adults by Rodriguez-Rodriguez et al [33]. A study of in inpatient subjects showed a different trend for the vector shift; the average vector displaced with increasing HSG due to decreased R/H, but increased Xc/H [29]. Interestingly, our study showed an increase in PhA with increasing HSG in healthy adults using whole body, right limb, and left limb modes, which is consistent with previous studies of healthy young adult [33] and inpatient subjects [29]. Moreover, PhA has been validated as a good predictor of nutritional and functional status [52-54].

In this study, we developed a regression model for HGS using basic indices and BIVA components (Xc/H and R/H). Similar study has been performed by Norman et al. in hospitalized European patients [29]. Their study included the same independent variables in the regression analysis for muscle function as ours, but our models performed better with higher R-squared values (r2 = 0.745–792) than theirs (r2 = 0.708). Additionally, the exact order the variables were entered into the equation were different in the 2 studies. In the Norman et al. study, the order was height, age, sex, weight, Xc/H, and R/H; whereas, in our study the order was sex, weight, age, height, Xc/H, and R/H. Although adjusted BIVA components were entered into the models later than the anthropometric indices, correlations between HGS with Xc/H and with R/H were moderate to strong in both studies.

There are limitations of this study that should be considered. First, our study was conducted with healthy Asian adults, and thus the results may not be applicable to different populations or individuals with diseases. Second, migration of the tolerate ellipses in the RXc plots were correlated with group-level differences, and the BIVA method may not be sensitive enough to evaluate vector shifts at the individual level. Third, the segmental BIVA in this study was not measured with electrodes placed on the upper limbs. However, this modified method is more convenient, and yielded good results. Fourth, HGS measured by hand dynamometer may be affected by instrument-, operator- and subject-related errors.

Conclusions

Our study showed that BIVA parameters measured by a standing impedance analyzer and anthropometric variables can predict muscle function as measured by HGS with good performance in healthy Asian adults. Our results may facilitate clinical applications of standing BIVA technology in assessing skeletal muscle function.

1 May 2020

PONE-D-20-07678

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 59 - Insert period.

69 - Insert 'the' before general population.

70-79 - Specific reported data from other studies is not necessary to include in the Introduction. This can be distracting to the reader. I suggest only including your study data reported in the Results section.

89 - Insert 'an' before alternating.

227-228 - Include parenthesis when describing 20% for group I and V, or not at all. Regardless, stay consistent.

283-284 - Which goodness of fit test was used.

Reviewer #2: With this study authors aimed to determine if bioelectrical impedance vector analysis assessment can predict muscle function in a specific healthy adult population. The methods used are appropriate but some details should be added to increase the completeness of the manuscript as well as additional discussion points that will aid in reader comprehension and interpretation.

Introduction should include a better justification of the study rational and clarify the actual knowledge about bioelectrical impedance vector analysis to discriminate muscle function as well as BIVA assessment positions.

Discussion should include appraisal with other methodologies and results used in previous studies. It also should be discussed how the circumstances BIVA was tested (refrain from alcoholic beverages for at least 48 hours, avoid diuretics for 7 days fast for 4 hours prior to study, and not asses females during menstruation) can interfere with the results.

Specific Comments:

Line

55 “Skeletal muscle is the largest organ in human body”: please review the statement and add the bibliographic reference

59 “and chronic disease may affect muscle mass and function [2] Muscle mass can be indirectly”: add punctuation mark period

62 “local muscle endurance [3].” Please verify the reference used

69 “In general population, a lower HSG is”: Correct HGS

81, 82 “HGS is one of the recommended tools for assessing nutritional state [13].”: Consider altering to: HGS is one of the four recommended types of measurements to assess nutritional state. [13].

226 “strsu3ength than R/H in all 3 modes.”: Change to: strength

258, 259 “Alternatively, segmental BIVA components may be measured with electrodes placed on the ipsilateral arm and foot, and voltage electrodes on both hands..”: Please add reference

Table 1: verify legend

Figure 1: add figure title and verify legend

**********

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Reviewer #1: No

Reviewer #2: No

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13 May 2020

Comments from reviewers:

Reviewer #1

59 - Insert period.

[Answer]:

Thanks for pointing out the error. We have corrected the mistake as below:

(line 59) and chronic disease may affect muscle mass and function [2].

69 - Insert 'the' before general population.

[Answer]:

Thanks for pointing out the error. We have added the article “the” before the noun as below:

(line 69) In “the” general population, a lower HSG is……..

70-79 - Specific reported data from other studies is not necessary to include in the Introduction. This can be distracting to the reader. I suggest only including your study data reported in the Results section.

[Answer]:

Thanks for the suggestion. We have deleted line 70-79 in the revised manuscript. In addition, the rest of 2nd paragraph was merged with the 1st paragraph.

89 - Insert 'an' before alternating.

[Answer]:

Thanks for pointing out the error. We have corrected it in the revised manuscript as below:

(line 89) properties such as resistance (R) and reactance (Xc) using “an” alternating current.

227-228 - Include parenthesis when describing 20% for group I and V, or not at all. Regardless, stay consistent.

[Answer]:

The sentence has been rephased as below:

(line 227-229) The subjects were divided into 5 equal groups (group I to V) depending on their HGS level, with group V representing the lowest quintile.

283-284 - Which goodness of fit test was used.

[Answer]:

To make the sentence clear, we have change “goodness of fit” to R-squared values in the revised manuscript as below:

Compared to the regression model for HGS developed using inpatient subjects by Norman et al. [25], the independent variables included in our regression analysis for muscle function were the same, but our models exhibited better R-squared values (r2 = 0.745-792) than theirs (r2 = 0.708).

Reviewer #2

With this study authors aimed to determine if bioelectrical impedance vector analysis assessment can predict muscle function in a specific healthy adult population. The methods used are appropriate but some details should be added to increase the completeness of the manuscript as well as additional discussion points that will aid in reader comprehension and interpretation.

Introduction should include a better justification of the study rationale and clarify the actual knowledge about bioelectrical impedance vector analysis to discriminate muscle function as well as BIVA assessment positions.

Discussion should include appraisal with other methodologies and results used in previous studies. It also should be discussed how the circumstances BIVA was tested (refrain from alcoholic beverages for at least 48 hours, avoid diuretics for 7 days fast for 4 hours prior to study, and not asses females during menstruation) can interfere with the results.

[Answer]:

We thank the reviewer for the suggestions. We have revised the introduction and discussion sections in accordance with the comments from the reviewer. The main changes in the introduction can be found in the last two paragraphs of then section.

The main reason for the subject preparation before BIVA such as refrain from alcoholic beverages for at least 48 hours, avoid diuretics for 7 days fast for 4 hours prior to study, and not asses females during menstruation is to have a good control of hydration status of the subject as variation in hydration status may affect the accuracy and precision of electric properties of biological tissues. We have done a literature review and summarized the methodologies in the end of the 2nd paragraph in discussion as below:

However, BIVA approach still involves the use of raw bioelectrical impedance parameters and is sensitive to physiological changes associated with the tissue conductivity. Factors known to introduce variability in tissue electric properties include body position, hydration status, electrolyte concentration, exercise, skin temperature and phase of menstrual cycle [28, 44-46]. This study was performed to test the potential application of BIVA in assessing skeletal muscle function and had the advantage of testing on healthy subjects under a careful control of hydration status.

Also, we have included appraisal with the other BIVA methodologies and results in the 3rd and 4th paragraphs of discussion in the revised manuscript as below:

The most common application of bioelectric impedance measure is whole body mode, which measures the total body electrical parameters with electrodes placed on the ipsilateral arm and foot. Whole body BIVA result is well-known to correlate with many diseases, such as renal diseases, critically ill patients, obesity, sarcopenia, and cachexia [40, 41, 48-50]. However, whole body mode regards the body as a uniform cylinder and this assumption is not entirely correct as the human body has a complex shape [28]. Furthermore, this method cannot provide information about body segment individually, limiting its application.

Segmental body measure of bioelectric impedance is a less common application, which can be acquired by two methods. For the bioelectric impedance of the arm, segmental impedance components can be acquired using two pairs of electrodes attached to the ipsilateral hand and shoulder [50]. The distance between receiving electrodes is then used for adjusting the impedance parameters. This method has been used to assess the skeletal muscle of right arm in Alzheimer’s disease and healthy control, showing a lower phase angles and longer specific vectors in patients with Alzheimer’s disease [51]. However, this approach involves complicated process for electrode placement and is less practical in large scale study. Another segmental approach is proposed by Kushner R.F. [28] who made the assumptions that the human body is composed of five cylinders (two upper limb cylinders, one trunk cylinder and two lower lib cylinders) and tissue electric properties in each cylinder can be directly measured. For BIVA of the arm, impedance components are measured with electrodes placed on the ipsilateral arm and foot, and voltage electrodes on both hands [28, 52], as done in our study. With this method, the measured values of Xc and R are divided by standing height, which assumes a fixed proportion of limb length to body height. This approach is simple and convenient but less discussed.

Specific Comments:

Line

55 “Skeletal muscle is the largest organ in human body”: please review the statement and add the bibliographic reference

[Answer]:

We have added reference to the sentence as below:

(line 55) Skeletal muscle is the largest organ in human body which accounts for 30% of body weight in women and greater than 40% of body weight in men [1,2].

References:

1. Janssen I, Heymsfield SB, Wang ZM, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18-88 yr. J Appl Physiol (1985). 2000;89(1):81-8.

2. Frontera W, Ochala J. Skeletal Muscle: A Brief Review of Structure and Function. Calcified tissue international. 2014;96.

59 “and chronic disease may affect muscle mass and function [2] Muscle mass can be indirectly”: add punctuation mark period

[Answer]:

Thanks for pointing out the error. We have corrected the mistake as below:

(line 59) and chronic disease may affect muscle mass and function [2].

62 “local muscle endurance [3].” Please verify the reference used

[Answer]:

Thanks for pointing out the error. We have used Endnote to cite and format the references and make sure their correctness and formatting. The correct references here were as below:

62 “local muscle endurance [5,6].”

5. Jones DA, Rutherford OM, Parker DF. Physiological changes in skeletal muscle as a result of strength training. Q J Exp Physiol. 1989;74(3):233-56.

6. Reid KF, Fielding RA. Skeletal muscle power: a critical determinant of physical functioning in older adults. Exerc Sport Sci Rev. 2012;40(1):4-12.

69 “In general population, a lower HSG is”: Correct HGS

[Answer]:

We have added “the” prior to general population as suggested by Reviewer 1. We have also done the corrections as below:

(line 69) In the general population, a lower HGS .………..

(line 84) , HGS has also been used to monitor.………..

(line 86) , HGS is a more sensitive indicator to.………..

81, 82 “HGS is one of the recommended tools for assessing nutritional state [13].”: Consider altering to: HGS is one of the four recommended types of measurements to assess nutritional state. [13].

[Answer]:

We have revised the manuscript as suggested by the reviewer as below:

(line 81-82) HGS is one of the four recommended types of measurements to assess nutritional state.

226 “strsu3ength than R/H in all 3 modes.”: Change to: strength

[Answer]:

We have corrected the error spelling of strength in line 226.

258, 259 “Alternatively, segmental BIVA components may be measured with electrodes placed on the ipsilateral arm and foot, and voltage electrodes on both hands.”: Please add reference

[Answer]:

We have added references to the sentence below.

(line 258-259) Alternatively, segmental BIVA components may be measured with electrodes placed on the ipsilateral arm and foot, and voltage electrodes on both hands [24, 46], as done in our study.

24. Kushner RF. Bioelectrical impedance analysis: a review of principles and applications. J Am Coll Nutr. 1992;11(2):199-209.

46. Organ LW, Bradham GB, Gore DT, Lozier SL. Segmental bioelectrical impedance analysis: theory and application of a new technique. J Appl Physiol (1985). 1994;77(1):98-112.

Table 1: verify legend

[Answer]:

Thanks for pointing out the error. We have corrected the error in the footnote as below:

HGS, hand grip strength; Z, impedance; R, resistance; Xc, reactance; R/H, resistance standardized for height; Xc/H, reactance standardized for height; PhA, phase angle; Lean, lean body mass; BF%, percentage body fat. Subscript DH, RH, LH, WB, RA, and LA denote dominant head, right hand, left hand, whole body, right arm and left arm, respectively.

Figure 1: add figure title and verify legend

[Answer]:

1. Asselin MC, Kriemler S, Chettle DR, Webber CE, Bar-Or O, McNeill FE. Hydration status assessed by multi-frequency bioimpedance analysis. Appl Radiat Isot. 1998;49(5-6):495-7.

2. Gudivaka R, Schoeller D, Kushner RF. Effect of skin temperature on multifrequency bioelectrical impedance analysis. J Appl Physiol (1985). 1996;81(2):838-45.

3. Kushner RF. Bioelectrical impedance analysis: a review of principles and applications. J Am Coll Nutr. 1992;11(2):199-209.

4. Gleichauf CN, Roe DA. The menstrual cycle’s effect on the reliability of bioimpedance measurements for assessing body composition. The American Journal of Clinical Nutrition. 1989;50(5):903-7.

For better demostration of Figure 1, we have revised the figure and legend as below:

Figure 1. Illustration of impedance measurement methods for the respective body parts.

Submitted filename: rebutting letter_2020.5.6 (3).docx

Click here for additional data file.

28 May 2020

PONE-D-20-07678R1

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults

PLOS ONE

Dear Dr. Hsieh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Prior to the final recommendation for publication of the manuscript, it is necessary for the authors to address the observation made by reviewer #2 in regard to possible incorrectly incorporated bibliographical information.

Please submit your revised manuscript by Jul 12 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Jose M. Moran

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: Most of my concerns have been adequately addressed.

I ask you to review Line 55 “Skeletal muscle is the largest organ in human body which accounts for 30% of body weight in women and greater than 40% of body weight in men [1,2].” None of the cited references defines skeletal muscle an organ as it is a tissue so please change it accordingly.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Maria António Castro

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

31 May 2020

Comments from reviewers:

Reviewer #2

Most of my concerns have been adequately addressed.

I ask you to review Line 55 “Skeletal muscle is the largest organ in human body which accounts for 30% of body weight in women and greater than 40% of body weight in men [1,2].” None of the cited references defines skeletal muscle an organ as it is a tissue so please change it accordingly.

[Answer]:

We thank the reviewer for her careful and professional review. We agreed with the reviewer that skeletal muscle is regarded as a tissue type instead of an organ in both original information as well as textbooks. Therefore, in our revised manuscript, the word “organ” has been replaced by “tissue”. Please see below for the change in the revised manuscript (lines 56-57):

Skeletal muscle is the largest tissue in human body which accounts for 30% of body weight in women and greater than 40% of body weight in men [1,2].

Submitted filename: rebutting letter_2020.5.31.docx

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2 Jun 2020

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults

PONE-D-20-07678R2

Dear Dr. Hsieh,

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With kind regards,

Jose M. Moran

Academic Editor

PLOS ONE

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4 Jun 2020

PONE-D-20-07678R2

Prediction and discrimination of skeletal muscle function by bioelectrical impedance vector analysis using a standing impedance analyzer in healthy Taiwanese adults

Dear Dr. Hsieh:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Jose M. Moran

Academic Editor

PLOS ONE