# Prediction Equations of the Multifrequency Standing and Supine Bioimpedance for Appendicular Skeletal Muscle Mass in Korean Older People

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## Abstract

**:**

^{2}/resistance, sex, and reactance as predictors (R

^{2}= 92.7% and 92.8%, SEE = 1.02 kg and 1.01 kg ASM for the standing and supine MF-BIA). The new MF-BIA equations had a specificity positive predictive value and negative predictive value of 85% or more except for a sensitivity of about 60.0%. The new standing and supine MF-BIA prediction equation are useful for epidemiological and field settings as well as a clinical diagnosis of sarcopenia. Future research is needed to improve the sensitivity of diagnosis of sarcopenia using MF-BIA.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Subjects

#### 2.2. Appendicular Skeletal Muscle Mass from Dual-Energy X-Ray Absorptiometry

#### 2.3. Multifrequency 8-Electrodes Bioimpedance Analysis

^{2}/resistance. The devices were calibrated every morning using the standard control circuit supplied by the manufacture. The precision error of fat-free mass is less than 2%.

#### 2.4. Published Prediction Equations

_{Vermeiren}(kg) = 0.827 + 0.19Ht

^{2}/Z + 2.101Sex + 0.079Wt; (R

^{2}= 0.888, SEE = 1.45 kg)

_{Scafoglieri}(kg) = 1.821 + 0.168 Ht

^{2}/R + 0.132Wt − 1.931Sex + 0.017Xc; (R

^{2}= 0.89, SEE = 1.45 kg)

_{Sergi}(kg) = 3.964 + 0.227Ht

^{2}/R + 0.095Wt + 1.384Sex + 0.064Xc; (R

^{2}= 0.92, SEE = 1.14 kg)

_{Kyle}(kg) = 4.211 + 0.267Ht

^{2}/R + 0.095Wt + 1.909Sex − 0.012age; (R

^{2}= 0.95, SEE = 1.12 kg)

_{Kim}(kg) = 5.663 + 0.104Ht

^{2}/R − 0.050age + 2.954Sex + 0.055Wt; (R

^{2}= 0.88, SEE = 1.35 kg)

_{Peniche}(kg) = −0.05376 + 0.2394Ht

^{2}/R + 2.708Sex + 0.065Wt; (R

^{2}= 0.91, SEE = 1.01 kg)

_{Yamada_for_men}(kg) = 51.33 + 0.6947ZI

_{@50kHz}) − 55.24(Z

_{@z250kHz}/Z

_{@5kHz}) − 10940(1/Z

_{@50kHz}); (R

^{2}= 0.87, SEE = 1.46 kg)

_{Yamada_for_women}(kg) = 37.91 + 0.6144ZI

_{@50kHz}− 36.61(Z

_{@z250kHz}/Z

_{@5kHz}) − 9332(1/Z

_{@50kHz}); (R

^{2}= 0.86, SEE = 1.22 kg)

_{@50kHz}is the impedance index at 50 kHz, Z

_{@5kHz}is impedance at 5 kHz, Z

_{@50kHz}is impedance at 50 kHz, Z

_{@250kHz}is impedance at 250 kHz derived from BIA; for sex men = 1 and women = 0.

#### 2.5. Statistical Analysis

_{@50kHz}, RI

_{@50kHz}, RI

_{@250kHz}, Xc

_{@5kHz}, Xc

_{@50kHz}, age (yr), weight (kg) and sex (dummy coded with women = 0 and men = 1). These equations were developed on 2/3 of the total sample selected randomly (development sample) with the remaining 1/3(validation sample) used for cross-validation. The developmental equations were selected by measures of goodness-of-fit statistics, including r

^{2}, the standard error of estimate (SEE), acceptable subjective rating of SEE (i.e., good to excellent) according to the minimally acceptable standard for prediction errors [11,24,25], the coefficient of variation (CV), and the variance inflation factor (VIF). The SEE measures the variation in the actual values from the predicted values. The SEE represents the degree of deviation of individual scores form the regression line. It is computed as the following formula: SEE = $\sqrt{{\displaystyle \sum}{\left(Measured\text{}ASM-Estimated\text{}ASM\right)}^{2}/\left(N-p-1\right)}$ where p = number of predicter variables. The CV is a measure of dispersion of the SEE from the mean of the actual values for the accuracy that is computed as following: CV = (SEE ÷ mean of the DXA-measured ASM) × 100. The VIF assesses how much the variance of an estimated regression coefficient increases when predictors are correlated for estimating collinearity/multicollinearity. Higher values of more than 10 can be assumed that the regression coefficients are poorly estimated due to muticollinearity to remove predictors from the model. In our study, with values less than 10, we are in good shape and can process with our regression. In the internal cross-validation, the group predictive accuracy of the ASM

_{BIA}equations was tested by calculating r

^{2}, total error (TE: The TE represents the degree of deviation from the line of identity using the formula: Total Error = $\sqrt{{\displaystyle \sum}{\left(MeasuredaASM-EstimatedaASM\right)}^{2}/N}$), and acceptable subjective rating of TE. The individual predictive accuracy of these equations was also tested by Bland–Altman plots that includes the constant error (CE; the bias of the mean difference between measured and predicted values) tested against zero using paired T-Test, 95% limits of agreement between equations, concordance correlation efficient (r

_{y-y’,mean}) and percentage of individual agreement (PIA). In the PIA, the minimum acceptable standard for prediction errors within ±1.45 kg and ±1.16 kg SEE or TE of ASM for men and women (i.e., good rating) [11,24,25] is plotted on the Bland–Altman plot and the percentage of individuals falling inside of these limits is calculated as following: PIA = (the number of residual scores within ±1.45 kg for men and ±1.16 kg for women ÷ total residual scores) × 100. The residual score is an individual difference between a DXA-measured ASM and a BIA-predicted ASM. For the external cross-validation study, the group and individual predictive accuracy were calculated for the published and built-in BIA equations. Overall agreement for the classification of sarcopenia by BIA equations and DXA measurements was performed by the 2 × 2 contingency table using a Cohen’s Kappa. The 5% level was chosen for statistical significance. Data were analyzed using SPSS version 21.0 (IBM, USA).

## 3. Results

#### 3.1. Characteristics of the Study Population

#### 3.2. Development and Cross-Validation of BIA Prediction Equations for ASM

_{@50kHz}, RI

_{@250kHz}, RI

_{@250kHz}, Xc

_{@50kHz}, Xc

_{@5kHz}, age, sex, and body weight as the dependent variables were analyzed through the stepwise multiple regression model. Within the standing and supine BIA measurements, RI, Xc, and sex (with body weight) were significant contributors to the BIA best-fitting regression model with maximum adjusted R

^{2}= 92.1% and 91.7% and minimum SEE = 1.06 kg and 1.08 kg for the standing vs. supine BIA regression equation, which resulted in the same subjective rating (men = “excellent”, women = “good”) as shown in Table 2. The coefficients of variation (CV) for the two regression equations were 6.4% and 6.1%, respectively. The variance inflation factor (VIF) of all independent variables in each regression equation was less than 5 with no multicollinearity among variables. The two regression equations for the development group were used to predict ASM in the cross-validation group. In Table 2, the results showed that there was no significant mean difference in ASM between the DXA measure and each BIA prediction in the standing and supine equation. The group predictive accuracy of R

^{2}, TE and subjective rating was given as 93.4% and 1.00 kg with the “excellent” in men and “very good” rating in women and 94.8% and 0.93 kg with the “excellent” in men and “very good” rating in women for the standing and supine BIA prediction equation, indicating these equations as acceptable. Thus, the stepwise multiple regression analysis of the whole sample performed the final BIA prediction equation in both of the standing and supine BIA measure (Table 3).

#### 3.3. The Final Standing and Supine BIA Prediction Equations for ASM

^{2}= 90.1% and R

^{2}= 91.5% of variability in ASM and accumulated R

^{2}(combined with Xc and sex in InBodyS10 and with Xc, Sex, and body weight in InBody770) explained variability up to 92.5% and 92.7% in the standing and supine BIA measure, respectively. The group predictive accuracy of SEE and subjective rating was following as 1.01~1.02 kg with the “excellent” rating in men and “good” or “very good” rating in women for the final standing and supine BIA prediction equation. The two newly developed equations for VIF showed no multicollinearity among variables. As shown in Figure 2A,B, there were no significant mean difference in ASM between DXA and BIA measurements in each BIA equation. The slope and intercept from the line of identity were not significantly different from 1 and 0 (p > 0.899). The Bland–Altman plots for the individual predictive accuracy are shown in Figure 3A,B. They showed no significant R

_{y-y’, mean}= 0.138 (p > 0.05). The CE was − 0.02 ± 1.01 kg and 0.02 ± 1.00 kg with all no significant differences (p = 0.758 for the standing, p = 0.835 for the supine). The two standard deviations (±2 SD) of the limits of agreement were between 1.96 kg and −2.01 kg and between 1.98 kg and −1.95 kg, with the percentage of agreement individual (PAI) calculated into 80% and 84% for the standing and for the supine, respectively.

#### 3.4. External Cross-Validation of Published and Built-in Equations for ASM

_{BIA}and ASM

_{DXA}(R

^{2}= 0.891~0.923, p < 0.001) equations. However, the total error (TE) of all published and built-in equations exceeded the acceptable subjective range except for the built-in BIA

_{InBody770}equation and BIA

_{Kyle}equation. The BIA

_{Kyle}equation shows a high R

^{2}and its TE was within the acceptable subjective range of the standard error. In addition, it has no significant difference in the bias and PIA value was 74.4%. The built-in BIA

_{InBody770}equation shows acceptable accuracy with a high R

^{2}, precise TE with subject ratings as “Good” for men and “Very good” for women, no difference in the bias, and 77.9% of PIA, whereas the built-in BIA

_{InbodyS10}equation showed poor subject ratings with poor TE, significant difference in the bias and 37.4% of PIA. The two new BIA equations performed best in estimating the ASM with much higher R

^{2}and TE with the acceptable subject ratings.

#### 3.5. The Agreement of Sarcopenia between DXA-Measured and BIA-Predicted ASMI

_{Kyle}and built-in BIA

_{InBody770}was significantly rated as moderate. Those new BIA prediction equations had a specificity, a positive predictive value (PPV), and a negative predictive value (NPV) of 80% or more. The sensitivity was below 60% in all the acceptable BIA equations, except that the new BIA prediction equations had a sensitivity of 60.0% in the standing and 63.3% in the supine with its agreement as substantial.

## 4. Discussion

^{2}= 92.5~92.7%, SEE = 1.01 kg~1.02 kg) but also at the individual level (Bias = 0.01~0.02, 95% LOA = ±1.96~±1.98, PIA = 80~88%), thus indicating that these equations are valid and applicable to clinical settings as well as large-scale epidemiological studies. The sensitivity, specificity and overall agreement for the diagnosis of sarcopenia by the two new prediction equations were reasonably applicable to clinical diagnosis, except that a little caution on sensitivity is required.

^{2}= 75.7~90.0%, SEE = 1.22 kg~1.46 kg) [14,17,19,21]. It also was comparable to the result of R

^{2}= 91.0% and 95.2% and SEE = 1.01 kg and 1.12 kg from Peniche and Kyle’s large-scale equation [16,18]. The main reasons for the improved group predictive accuracy (R

^{2}and SEE) in this study may be due to the large sample size, sufficient magnitude of sample-to-predictor ratio, and using the high-frequency resistance (R) of 250 kHz. Generally, large samples (N = 100~400 subjects) are needed to ensure that the data are representative of the population for whom the equation was developed, and statisticians recommend a minimum of 20 to 40 subjects per predictor variable [23,24]. The new equations are based on a large sample of 199 in older Koreans and the ratio of the sample size to the number of predictors in this study was 50 to 67 subjects per predictor, which is larger than the recommended minimum ratio of 20 to 40 subjects per predictor. This large sample size and sufficient ratio may have led to have more stable regression weights for each predictor in the equation [23,24]. Meanwhile, the measurements of the resistance from 250 kHz are derived from the intracellular conductor as well as extracellular conductor in in the skeletal muscle. Unlike the model of the single-frequency (50 kHz) whole-body bioimpedance that predominately estimates the ASM from the extracellular conductor, predictive accuracy can be increased when estimating the ASM from the high-frequency 250 kHz bioelectrical impedance that conducts intracellular fluids of the skeletal muscle cells. Our findings showed the higher predictive accuracy of R

^{2}and SEE from use of the high-frequency resistance (250 kHz) as opposed to the lower-frequency resistance 50 kHz (e.g., R

^{2}= 91.5%, SEE = 1.09 kg ASM for the RI at 250 kHz vs. R

^{2}= 90.6%, SEE = 1.49 kg for the RI at 50 kHz of InBodyS10). The result from this study is consistent with Segal’s findings that the measurements of the resistance from the high-frequency (100 kHz) can estimate both the intracellular fluid and extracellular fluids (i.e., total body water), with the highest accuracy among the 5k Hz and 100 kHz [26,27]. Therefore, the frequency-specific measurements that induce the suitable conductivity of the intracellular fluid in the skeletal muscle cells are considered to be very important factors to improve the accuracy of prediction equations. On the other hand, the individual predictive accuracy in this study showed no bias (CE), the allowable range of the limit of agreement, and the percentage of individual agreement (PIA) being more than 80%. Especially, compared to the LoA of ±2.12~±2.78 in the previous studies, the LoA of ±1.96~±2.04 in the present study was improved and suitable for predicting the group and individual level of ASM.

^{2}= 80% or more, no bias, no more than 1.45 kg SEE for men and no more than 1.16 kg SEE for women, and more than 70% for PIA. Our results indicated that the built-in BIA prediction equation of InBody770 and Kyle’s supine-position prediction equation was found to be acceptably accurate at the group and individual level. Conversely, previous published and built-in prediction equations only had good explanatory values (R

^{2}) and were found unacceptable for the SEEs and individual accuracy. The BIA

_{Kim}and the BIA

_{Yamata}regression equation developed for Korean or Asian older populations were found to be the least accurate in the present study, owing to low accuracy when they were initially developed. In fact, the TEs for two new BIA prediction equations in this study was 1.00~1.04 kg, and the TEs from BIA

_{Kim}and BIA

_{Yamata}were 5.91 kg and 1.86 kg, respectively. Therefore, both Kim and Yamata’s prediction equation for Korean or Asian populations should be selectively replaced by the new prediction equations put forth by the present study. Finally, the main reason for inaccuracies in other published prediction equations is the difference in body shape by population. Unlike the long and short torso of Caucasians and African-Americans, the short and long torso of Asians makes a different relation of muscle volume to current flow and resistance [11,12]. Therefore, the need to develop a population-specific prediction equation was confirmed again. Overall, the results of external cross-validation study showed that Kyle’s prediction equation and InBody770′s built-in prediction equation can perform accurate prediction at the group and individual levels for the Korean older population.

_{InBody770_new}, BIA

_{InBodyS10_new}, BIA

_{Kyle}and BIA

_{InBodyS10}, to determine the applicability to the diagnosis of sarcopenia. Among the four equations, the two new standing and supine BIA prediction equations had the highest sensitivity, specificity, positive predictive value, negative predictive value, and overall agreement, which was higher than the size of the sensitivity and specificity of the BIA

_{Kyle}and BIA

_{InBody770}prediction equations. In this study, the overall agreement, sensitivity, specificity, PPV and NPV were also analyzed to determine whether ASMI by MF-BIA is feasible for individual testing and clinical application. All of the indices were very good, but the sensitivity was moderate, around 60%. The sensitivity of about 60% was higher than the previously reported 37~55% [27], but it still needs to be improved. One possible explanation for the low sensitivity in the present study is that MF-BIA-based ASMI in false-negative cases overestimated only 0.02 to 0.27 from the ASMI cutoff values of 7.00 in men and 0.06 to 0.49 from ASMI cutoff values of 5.40 in women (data not shown). When the newly developed MF-BIA prediction equations are applied in the clinical settings, ASMI that ranges from 7.02 to 7.27 in men and 5.46 to 5.89 in women should be considered as false-negative and perform an additional diagnosis by DXA. For other ASMI ranges, the results of ASMI by MF-BIA can be considered as accurate diagnostic results in clinical settings. However, further research is needed to improve sensitivity. In future studies, it would be effective to increase the explanatory power of the skeletal muscle impedance by passing the current of the intracellular fluid using a higher frequency [11,12,13,23,28,29]. Therefore, sensitivity should be taken this into account in clinical settings.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Meynial-Denis, D. Sarcopenia: Molecular, Cellular, and Nutritional Aspects—Applications to Humans; CRC Press: Boca Raton, FL, USA, 2020. [Google Scholar]
- Anker, S.D.; Morley, J.E.; von Haehling, S. Welcome to the ICD-10 code for sarcopenia. J. Cachexia Sarcopenia Muscle
**2016**, 7, 512–514. [Google Scholar] [CrossRef] [PubMed] - Rosenberg, I.H. Sarcopenia: Origins and clinical relevance. Clin. Geriatr. Med.
**2011**, 27, 337–339. [Google Scholar] [CrossRef] [PubMed] - Baumgartner, R.N.; Koehler, K.M.; Gallagher, D.; Romero, L.; Heymsfield, S.B.; Ross, R.R.; Garry, P.J.; Lindeman, R.D. Epidemiology of sarcopenia among the elderly in New Mexico. Am. J. Epidemiol.
**1998**, 147, 755–763. [Google Scholar] [CrossRef] [PubMed] - Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyere, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing
**2019**, 48, 601. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Cruz-Jentoft, A.J.; Baeyens, J.P.; Bauer, J.M.; Boirie, Y.; Cederholm, T.; Landi, F.; Martin, F.C.; Michel, J.P.; Rolland, Y.; Schneider, S.M.; et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European working group on sarcopenia in older people. Age Ageing
**2010**, 39, 412–423. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Chen, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Chou, M.Y.; Iijima, K.; Jang, H.C.; Kang, L.; Kim, M.; Kim, S.; et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J. Am. Med. Dir. Assoc.
**2020**, 21, 300–307 e2. [Google Scholar] [CrossRef] - Chen, L.K.; Liu, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Bahyah, K.S.; Chou, M.Y.; Chen, L.Y.; Hsu, P.S.; Krairit, O.; et al. Sarcopenia in Asia: Consensus report of the Asian working group for sarcopenia. J. Am. Med. Dir. Assoc.
**2014**, 15, 95–101. [Google Scholar] [CrossRef] - Chen, L.K.; Lee, W.J.; Peng, L.N.; Liu, L.K.; Arai, H.; Akishita, M.; Asian working group for sarcopenia. Recent advances in sarcopenia research in asia: 2016 update from the Asian working group for sarcopenia. J. Am. Med. Dir. Assoc.
**2016**, 17, 767.e1–767.e7. [Google Scholar] [CrossRef] - Tosato, M.; Marzetti, E.; Cesari, M.; Savera, G.; Miller, R.R.; Bernabei, R.; Landi, F.; Calvani, R. Measurement of muscle mass in sarcopenia: From imaging to biochemical markers. Aging Clin. Exp. Res.
**2017**, 29, 19–27. [Google Scholar] [CrossRef] - Buckinx, F.; Landi, F.; Cesari, M.; Fielding, R.A.; Visser, M.; Engelke, K.; Maggi, S.; Dennison, E.; Al-Daghri, N.M.; Allepaerts, S.; et al. Pitfalls in the measurement of muscle mass: A need for a reference standard. J. Cachexia Sarcopenia Muscle
**2018**, 9, 269–278. [Google Scholar] [CrossRef] - Gonzalez, M.C.; Barbosa-Silva, T.G.; Heymsfield, S.B. Bioelectrical impedance analysis in the assessment of sarcopenia. Curr. Opin. Clin. Nutr. Metab. Care
**2018**, 21, 366–374. [Google Scholar] [CrossRef] [PubMed] - Gonzalez, M.C.; Heymsfield, S.B. Bioelectrical impedance analysis for diagnosing sarcopenia and cachexia: What are we really estimating? J. Cachexia Sarcopenia Muscle
**2017**, 8, 187–189. [Google Scholar] [CrossRef] [PubMed] - Vermeiren, S.; Beckwee, D.; Vella-Azzopardi, R.; Beyer, I.; Knoop, V.; Jansen, B.; Delaere, A.; Antoine, A.; Bautmans, I.; Scafoglieri, A.; et al. Evaluation of appendicular lean mass using bio impedance in persons aged 80+: A new equation based on the BUTTERFLY-study. Clin. Nutr.
**2019**, 38, 1756–1764. [Google Scholar] [CrossRef] - Scafoglieri, A.; Clarys, J.P.; Bauer, J.M.; Verlaan, S.; Van Malderen, L.; Vantieghem, S.; Cederholm, T.; Sieber, C.C.; Mets, T.; Bautmans, I.; et al. Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline—The PROVIDE study. Clin. Nutr.
**2017**, 36, 869–875. [Google Scholar] [CrossRef] [Green Version] - Sergi, G.; De Rui, M.; Veronese, N.; Bolzetta, F.; Berton, L.; Carraro, S.; Bano, G.; Coin, A.; Manzato, E.; Perissinotto, E. Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free-living Caucasian older adults. Clin. Nutr.
**2015**, 34, 667–673. [Google Scholar] [CrossRef] - Kyle, U.G.; Genton, L.; Hans, D.; Pichard, C. Validation of a bioelectrical impedance analysis equation to predict appendicular skeletal muscle mass (ASMM). Clin. Nutr.
**2003**, 22, 537–543. [Google Scholar] [CrossRef] - Kim, J.H.; Choi, S.H.; Lim, S.; Kim, K.W.; Lim, J.Y.; Cho, N.H.; Park, K.S.; Jang, H.C. Assessment of appendicular skeletal muscle mass by bioimpedance in older community-dwelling Korean adults. Arch. Gerontol. Geriatr.
**2014**, 58, 303–307. [Google Scholar] [CrossRef] - Rangel Peniche, D.B.; Raya Giorguli, G.; Alemán-Mateo, H. Accuracy of a predictive bioelectrical impedance analysis equation for estimating appendicular skeletal muscle mass in a non-Caucasian sample of older people. Arch. Gerontol. Geriatr.
**2015**, 61, 39–43. [Google Scholar] [CrossRef] - Yamada, Y.; Nishizawa, M.; Uchiyama, T.; Kasahara, Y.; Shindo, M.; Miyachi, M.; Tanaka, S. Developing and validating an age-independent equation using multi-frequency bioelectrical impedance analysis for estimation of appendicular skeletal muscle mass and establishing a cutoff for sarcopenia. Int. J. Environ. Res. Public Health
**2017**, 14, 809. [Google Scholar] [CrossRef] - Bosy-Westphal, A.; Jensen, B.; Braun, W.; Pourhassan, M.; Gallagher, D.; Müller, M.J. Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices. Eur. J. Clin. Nutr.
**2017**, 71, 1061–1067. [Google Scholar] [CrossRef] - Heymsfield, S.B.; Smith, R.; Aulet, M.; Bensen, B.; Lichtman, S.; Wang, J.; Pierson, R.N., Jr. Appendicular skeletal muscle mass: Measurement by dual-photon absorptiometry. Am. J. Clin. Nutr.
**1990**, 52, 214–218. [Google Scholar] [CrossRef] [PubMed] - Lohman, T.G. Advances in Body Composition Assessment; Human Kinetics: Champaign, IL, USA, 1992. [Google Scholar]
- Heyward, V.H.; Wagner, D.R. Applied Body Composition Assessment, 2nd ed.; Human Kinetics: Champaign, IL, USA, 2004. [Google Scholar]
- Watson, P.F.; Petrie, A. Method agreement analysis: A review of correct methodology. Theriogenology
**2010**, 73, 1167–1179. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Reiter, R.; Iglseder, B.; Treschnitzer, W.; Alzner, R.; Mayr-Pirker, B.; Kreutzer, M.; Pirich, C.; Kässmann, H.; Dovjak, P.; Reiss, J. Quantifying appendicular muscle mass in geriatric inpatients: Performance of different single frequency BIA equations in comparison to dual X-ray absorptiometry. Arch. Gerontol. Geriatr.
**2019**, 80, 98–103. [Google Scholar] [CrossRef] - De Rui, M.; Veronese, N.; Bolzetta, F.; Berton, L.; Carraro, S.; Bano, G.; Trevisan, C.; Pizzato, S.; Coin, A.; Perissinotto, E.; et al. Validation of bioelectrical impedance analysis for estimating limb lean mass in free-living Caucasian elderly people. Clin. Nutr.
**2017**, 36, 577–584. [Google Scholar] [CrossRef] [PubMed] - Segal, K.R.; Burastero, S.; Chun, A.; Coronel, P.; Pierson, R.N., Jr.; Wang, J. Estimation of extracellular and total body water by multiple-frequency bioelectrical-impedance measurement. Am. J. Clin. Nutr.
**1991**, 54, 26–29. [Google Scholar] [CrossRef] - Lukaski, H.C. Biological indexes considered in the derivation of the bioelectrical impedance analysis. Am. J. Clin. Nutr.
**1996**, 64, 397S–404S. [Google Scholar] [CrossRef] - Erlandson, M.C.; Lorbergs, A.L.; Mathur, S.; Cheung, A.M. Muscle analysis using pQCT, DXA and MRI. Eur. J. Radiol.
**2016**, 85, 1505–1511. [Google Scholar] [CrossRef] - Clark, R.V.; Walker, A.C.; Miller, R.R.; O’Connor-Semmes, R.L.; Ravussin, E.; Cefalu, W.T. Creatine (methyl-d3) dilution in urine for estimation of total body skeletal muscle mass: Accuracy and variability vs. MRI and DXA. J. Appl. Physiol.
**2018**, 124, 1–9. [Google Scholar] [CrossRef] [Green Version] - Freda, P.U.; Shen, W.; Reyes-Vidal, C.M.; Geer, E.B.; Arias-Mendoza, F.; Gallagher, D.; Heymsfield, S.B. Skeletal muscle mass in acromegaly assessed by magnetic resonance imaging and dual-photon x-ray absorptiometry. J. Clin. Endocrinol. Metab.
**2009**, 94, 2880–2886. [Google Scholar] [CrossRef] - Fuller, N.J.; Hardingham, C.R.; Graves, M.; Screaton, N.; Dixon, A.K.; Ward, L.C.; Elia, M. Assessment of limb muscle and adipose tissue by dual-energy X-ray absorptiometry using magnetic resonance imaging for comparison. Int. J. Obesity
**1999**, 23, 1295–1302. [Google Scholar] [CrossRef] [Green Version] - Koster, A.; Ding, J.; Stenholm, S.; Caserotti, P.; Houston, D.K.; Nicklas, B.J.; You, T.; Lee, J.S.; Visser, M.; Newman, A.B.; et al. Does the amount of fat mass predict age-related loss of lean mass, muscle strength, and muscle quality in older adults? J. Gerontol. A Biol. Sci. Med. Sci.
**2011**, 66, 888–895. [Google Scholar] [CrossRef] [PubMed]

**Figure 1.**The testing postures and the electrode placements (

**A**) BIA in the standing position (

**B**) BIA in the supine position [permitted from the manufacture].

**Figure 2.**Bivariate regression analysis; (

**A**) The line of best fit and standard error of estimate for the standing BIA prediction equation (

**B**) The line of best fit and standard error of estimate for the supine BIA prediction.

**Figure 3.**Bland–Altman plot of residual scores and mean difference between the measured and predicted ASM; (

**A**) Residuals and mean difference from the standing BIA prediction equation (

**B**) Residuals and mean difference from the supine BIA prediction equation; R

_{y-y’, mean}= concordance correlation coefficient between the residuals and the means of the measured ASM and predicted ASM; LoA = Limits of Agreement in ±1.96 SD; PIA = Percentage of individual agreement; = women within 1.16 kg ASM, = men within 1.45 kg ASM, = individuals who are out of the minimum acceptable standard for prediction errors (i.e., good rating) [11,24,25].

Variables | Development Group | Cross-Validation Group | ||
---|---|---|---|---|

Men (n = 63) | Women (n = 70) | Men (n = 31) | Women (n = 35) | |

Age (years) | 76.4 ± 4.2 | 76.1 ± 4.1 | 75.9 ± 4.1 | 75.6 ± 4.3 |

Height (cm) | 166.5 ± 5.1 | 152.7 ± 5.0 * | 167.6 ± 4.3 | 153.3 ± 4.2 * |

Weight (kg) | 65.6 ± 7.7 | 55.4 ± 5.8 * | 66.0 ± 6.8 | 54.8 ± 7.3 * |

BMI (kg·m^{−2}) | 23.7 ± 2.3 | 23.8 ± 2.2 | 23.5 ± 2.3 | 23.3 ± 2.7 |

FFM (kg) | 50.2 ± 4.6 | 36.9 ± 3.1 * | 51.1 ± 4.0 | 37.0 ± 3.9 * |

FM (kg) | 15.7 ± 5.3 | 17.3 ± 4.6 * | 15.1 ± 5.1 | 18.6 ± 4.1 * |

PBF (%) | 23.2 ± 6.9 | 32.5 ± 4.9 * | 22.4 ± 5.0 | 31.5 ± 5.5 * |

ASM (kg) | 20.6 ± 2.4 | 14.4 ± 1.4 * | 21.1 ± 2.0 | 14.3 ± 1.8 * |

Standing mode of BIA | ||||

R_{@50kHz} | 528 ± 55 | 616 ± 50 * | 532 ± 42 | 663 ± 61 * |

R_{@250kHz} | 480 ± 50 | 564 ± 46 * | 484 ± 39 | 571 ± 57 * |

Xc_{@5kHz} | 24.5 ± 4.6 | 24.8 ± 4.0 | 24.7 ± 3.6 | 24.9 ± 4.1 |

Xc_{@50kHz} | 47.2 ± 7.4 | 48.1 ± 6.1 | 47.5 ± 4.7 | 49.2 ± 6.5 |

RI_{@50kHz} | 53.1 ± 6.7 | 38.1 ± 3.9 * | 53.1 ± 4.8 | 38.1 ± 4.5 * |

RI_{@250tand} | 58.4 ± 7.3 | 41.6 ± 4.3 * | 58.4 ± 5.3 | 41.6 ± 5.0 * |

Supine mode of BIA | ||||

R_{@50kHz} | 488 ± 49 | 575 ± 46 * | 487 ± 41 | 582 ± 57 * |

R_{@250kHz} | 438 ± 44 | 521 ± 41 * | 437 ± 38 | 527 ± 54 * |

Xc_{@5kHz} | 24.3 ± 4.7 | 25.1 ± 4.3 | 25.0 ± 2.9 | 25.0 ± 3.6 |

Xc_{@50kHz} | 47.9 ± 7.2 | 49.3 ± 6.9 | 47.9 ± 4.5 | 50.0 ± 5.7 |

RI_{@50kHz} | 57.3 ± 40.8 | 40.8 ± 4.0 * | 58.1 ± 5.3 | 40.8 ± 4.8 * |

RI_{@250tand} | 64.0 ± 7.9 | 45.0 ± 4.4 * | 64.8 ± 6.0 | 45.1 ± 5.5 * |

^{‡}= significantly different from the development group at p < 0.05.

**Table 2.**Development and validation of predictive bioimpedance analysis (BIA) equations for appendicular skeletal muscle mass (ASM) on Korean older people.

Standing Mode of SMF-BIA | ||

Development group (n = 133) | Cross-validation group (n = 66) | |

Measured ASM | 17.3 ± 3.66 kg | 17.5 ± 3.92 kg |

ASM prediction equation | 0.273RI@250 kHz + 1.369sex + 0.049Xc@50 kHz + 0.032 BW − 1.118 | |

^{‡}R^{2} = 0.923, SEE = 1.10 kg, CV = 6.4%, | ||

SR(M) = Very good, SR(W) = Good | ||

VIF: RI = 6.88, Xc = 1.45, BW = 2.74, | ||

sex = 3.87 | ||

Predicted ASM | 17.5 ± 3.73 kg | 17.6 ± 3.73 kg, * p = 0.693 |

R^{2} = 0.934, TE = 1.00 kg, CV = 5.7% | ||

SR = Excellent (M), SR = Very good (W) | ||

Supine Mode of SMF-BIA | ||

Development group (n = 133) | Validation group (n = 66) | |

Measured ASM | 17.3 ± 3.66 kg | 17.5 ± 3.92 kg |

ASM prediction equation | 0.266RI@250 kHz + 1.227sex + 0.057Xc _{@5kHz} + 0.960 | |

^{‡}R^{2} = 0.919, SEE = 1.06 kg, CV = 6.1%, | ||

SR(M) = Very good, SR(W) = Good | ||

VIF: RI = 4.34, Xc = 1.40, sex = 3.73 | ||

Predicted ASM | 17.3 ± 3.51 kg | 17.4 ± 3.58 kg, * p = 0.291 R ^{2} = 0.948, TE = 0.93 kg, CV = 5.3% |

SR = Excellent (M), SR = Very good (W) |

^{2}= determinant of coefficient;

^{‡}R

^{2}= Adjusted R

^{2}; CV = coefficient variation; VIF = variation inflation factor; SEE = standard error of the estimate; SR = subject rating of standard for prediction error [ideal = 0.72~0.90(M), 0.54~0.65(W); excellent = 0.90~1.09(M), 0.65~0.83(W); very good = 1.09~1.27(M), 0.83~1.01(W); good = 1.27~1.45(M), 1.01~1.16(W); fairly good = 1.45~1.63(M), 1.16~1.30(W); fair good = 1.45~1.63(M), 1.16~1.30(W); Fair = 1.63~1.81(M), 1.30~1.44(W); poor = >1.81(M), >1.44(W); unit = kg ASM] [11,24,25]; TE = Total Error; * = p-value of paired t-test for the mean difference between measured and predicted means.

Final Prediction Equations | |
---|---|

Standing Mode of SMF-BIA (n = 199) | |

Measured ASM | 17.4 ± 3.74 kg |

ASM prediction equation | 0.286RI@250 kHz + 1.367sex + 0.054Xc@50 kHz + 0.031 BW − 1.864 |

^{‡}R^{2} = 0.925, SEE = 1.02 kg, CV = 5.9%, SR = Excellent (M), SR = Good (W) | |

VIF: RI_{@250kHz} = 7.48, sex = 4.04, Xc_{@50kHz} = 1.41, BW = 2.91 | |

Predicted ASM | 17.4 ± 3.60 kg, * p = 0.758 |

Supine Mode of SMF-BIA (n = 199) | |

Measured ASM | 17.4 ± 3.74 kg |

ASM prediction equation | 0.276RI_{@250kHz} + 1.151sex + 0.059Xc@5 kHz + 0.429 |

^{‡}R^{2} = 0.927, SEE = 1.01 kg, CV = 5.8%, SR = Excellent (M), SR = Very good (W) | |

VIF: RI = 3.91, Xc= 1.11, sex = 3.73 | |

Predicted ASM | 17.4 ± 3.60 kg, * p = 0.835 |

^{‡}R

^{2}= adjusted determinant of coefficient; CV = coefficient variation; VIF = variation inflation factor; SEE = standard error of the estimate; SR = subject rating of standard for prediction error [ideal = 0.72~0.90(M), 0.54~0.65(W); excellent = 0.90~1.09(M), 0.65~0.83(W); very good = 1.09~1.27(M), 0.83~1.01(W); good = 1.27~1.45(M), 1.01~1.16(W); fairly good = 1.45~1.63(M), 1.16~1.30(W); fair good = 1.45~1.63(M), 1.16~1.30(W); Fair = 1.63~1.81(M), 1.30~1.44(W); poor = >1.81(M), >1.44(W); unit = kg ASM] [11,24,25]; * = p-value of paired t-test for the mean difference between measured and predicted means.

**Table 4.**External cross-validation of BIA equations and devices for ASM measured by dual-energy X-ray absorptiometry (DXA).

Device | ASM (Mean ± SD) | R^{2} | TE (kg) | Subjective Rating | CE (Mean ± SD) | LoA (Kg) | r_{y-y’,mean} | PIA | |
---|---|---|---|---|---|---|---|---|---|

Women | Man | ||||||||

DXA | 17.38 ± 3.74 | ||||||||

Standing Modes of BIA | |||||||||

BIA_{standing_New} | 17.39 ± 3.59 | 0.924 | 1.04 | Good | Excellent | − 0.02 ± 1.03 | −2.04, 2.01 | −0.145 * | 81.4 |

BIA_{InBody770} | 17.35 ± 4.00 | 0.917 | 1.15 | Good | Very good | 0.03 ± 1.15 | −2.22, 2.29 | −0.223 * | 77.9 |

BIA_{Yamada} | 18.67 ± 4.07 | 0.891 | 1.86 | Poor | Poor | −1.29 ± 1.35 ** | −3.94, −1.35 | −0.252 ** | 48.7 |

Supine Modes of BIA | |||||||||

BIA_{supine_New} | 17.37 ± 3.60 | 0.928 | 1.00 | Very good | Excellent | 0.02 ± 1.10 | −1.95, 1.98 | 0.138 | 83.9 |

BIA_{InBodyS10} | 19.08 ± 4.43 | 0.914 | 2.20 | Poor | Poor | −1.71 ± 1.38 ** | −4.42, 1.00 | −0.464 ** | 37.4 |

BIA_{Vermeiren} | 15.80 ± 3.38 | 0.916 | 1.81 | Poor | Poor | 1.42 ± 1.13 ** | −0.80, 3.64 | −0.327 ** | 39.7 |

BIA_{Scaroflieri} | 17.77 ± 3.46 | 0.906 | 1.21 | Fairly good | Very good | −0.39 ± 1.16 ** | −2.66, 1.89 | −0.243 ** | 74.9 |

BIA_{Sergi} | 16.60 ± 3.45 | 0.919 | 1.33 | Fair | Good | 0.78 ± 1.08 ** | −1.34, 2.90 | −0.275 ** | 67.3 |

BIA_{Kyle} | 17.34 ± 4.09 | 0.923 | 1.15 | Good | Very good | 0.04 ± 1.16 | −2.23, 2.30 | −0.307 ** | 74.4 |

BIA_{Kim} | 11.64 ± 2.79 | 0.899 | 5.91 | Poor | Poor | 5.75 ± 1.42 ** | 2.96, 8.53 | −0.098 | 0.0 |

BIA_{Rangel} | 16.81 ± 4.06 | 0.919 | 1.30 | Fairly good | Good | 0.57 ± 1.17 ** | −1.72, 2.87 | −0.276 ** | 64.3 |

^{2}= determinant of coefficient between ASM

_{DXA}and ASM

_{BIA}; TE = Total; Limits of agreement were calculated as mean difference ± 1.96 times SD; r

_{y-y’,mean}= concordance Pearson correlation coefficient between differences (ASM

_{DXA}− ASM

_{BIA}) and means ((ASM

_{DXA}+ ASM

_{BIA})/2); SR = subject rating of standard for prediction error [ideal = 0.72~0.90(M), 0.54~0.65(W); excellent = 0.90~1.09(M), 0.65~0.83(W); very good = 1.09~1.27(M), 0.83~1.01(W); good = 1.27~1.45(M), 1.01~1.16(W); fairly good = 1.45~1.63(M), 1.16~1.30(W); fair good = 1.45~1.63(M), 1.16~1.30(W); Fair = 1.63~1.81(M), 1.30~1.44(W); poor > 1.81(M), > 1.44(W); unit = kg ASM] [11,24,25]; PIA = Percentage of individual agreement, * p < 0.05; ** p < 0.001.

**Table 5.**Prevalence, sensitivity and specificity of the acceptable BIA equations to determine sarcopenia.

Equations/Device | Overall Agreement N (%) | Cohen’s Kappa | Sensitivity | Specificity | PPV | NPV |
---|---|---|---|---|---|---|

Standing Modes of BIA | ||||||

BIA_{InBody770_NEW} | 184 (92.5) | 0.664 * | 60.0 | 98.2 | 85.7 | 93.3 |

BIA_{InBody770} | 165 (82.9) | 0.397 * | 51.5 | 89.2 | 48.6 | 90.2 |

Supine Modes of BIA | ||||||

BIA_{InBodyS10_NEW} | 185 (93.0) | 0.691 * | 63.3 | 98.2 | 86.4 | 93.8 |

BIA_{Kyle} | 168 (84.4) | 0.416 * | 48.5 | 91.6 | 53.3 | 89.9 |

^{−2}, Male < 7.0 kg·m

^{−2}, Agreement is poor if k < 0.00, slight if 0.00 < k < 0.20, fair if 0.21 < k < 0.40, moderate if 0.41 < k < 0.60, substantial if 0.61 < k < 0.80, and almost perfect if k > 0.80; * p < 0.001 [25].

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Jeon, K.C.; Kim, S.-Y.; Jiang, F.L.; Chung, S.; Ambegaonkar, J.P.; Park, J.-H.; Kim, Y.-J.; Kim, C.-H.
Prediction Equations of the Multifrequency Standing and Supine Bioimpedance for Appendicular Skeletal Muscle Mass in Korean Older People. *Int. J. Environ. Res. Public Health* **2020**, *17*, 5847.
https://doi.org/10.3390/ijerph17165847

**AMA Style**

Jeon KC, Kim S-Y, Jiang FL, Chung S, Ambegaonkar JP, Park J-H, Kim Y-J, Kim C-H.
Prediction Equations of the Multifrequency Standing and Supine Bioimpedance for Appendicular Skeletal Muscle Mass in Korean Older People. *International Journal of Environmental Research and Public Health*. 2020; 17(16):5847.
https://doi.org/10.3390/ijerph17165847

**Chicago/Turabian Style**

Jeon, Kwon Chan, So-Young Kim, Fang Lin Jiang, Sochung Chung, Jatin P. Ambegaonkar, Jae-Hyeon Park, Young-Joo Kim, and Chul-Hyun Kim.
2020. "Prediction Equations of the Multifrequency Standing and Supine Bioimpedance for Appendicular Skeletal Muscle Mass in Korean Older People" *International Journal of Environmental Research and Public Health* 17, no. 16: 5847.
https://doi.org/10.3390/ijerph17165847