Hong-Qi Xu1, Jing-Min Liu2, Xin Zhang3, Yong-Tai Xue4, Ji-Peng Shi5, Wei Chen6, Xiu-Yuan Zheng7. 1. Research Center of Sports and Health Science, School of Sports Science and Physical Education, Northeast Normal University, Changchun, Jilin Province, China. Electronic address: xu_hongqi0402@126.com. 2. Research Center of Sports and Health Science, Division of Sports Science and Physical Education, Tsinghua University, Beijing, China. Electronic address: liu_jingmin2016@126.com. 3. Ergonomics Standardization Research Field, China National Institute of Standardization, Beijing, China. Electronic address: zhangx@cnis.gov.cn. 4. Research Center of Sports and Health Science, School of Sports Science and Physical Education, Northeast Normal University, Changchun, Jilin Province, China. Electronic address: xueyt442@nenu.edu.cn. 5. Research Center of Sports and Health Science, School of Sports Science and Physical Education, Northeast Normal University, Changchun, Jilin Province, China. Electronic address: shijp2006@163.com. 6. Department of Parenteral and Enteral Nutrition, Peking Union Medical College Hospital, Beijing, China. Electronic address: txchenwei@sina.com. 7. Research Center of Sports and Health Science, Division of Sports Science and Physical Education, Tsinghua University, Beijing, China. Electronic address: xyzheng34@163.com.
Abstract
BACKGROUND: It is crucial to assess age-related muscle mass changes and derived indices differences in geriatric medicine. We aimed to develop and validate four bioimpedance analysis (BIA) prediction equations against dual-energy X-ray absorptiometry (DEXA) and magnetic resonance image (MRI) in estimating skeletal muscle mass and to compare the differences among skeletal muscle mass indices, cutoff values, and corresponding prevalence rates of low muscle mass for assessing sarcopenia in Chinese adults. METHODS: We measured the height (Ht), weight (Wt), appendicular lean mass (ALM) or skeletal muscle mass (ASM), total lean body mass (LBM) or skeletal muscle mass (TSM) obtained using DEXA or MRI, and a multi-frequency BIA (BCA II;50, 250 kHz), in 371 adults aged 18.0-87.0 years. We also collected gender, age, Ht, Wt, and impedance indexes (Ht2/R50, Ht2/R250, R50/Ht2, R250/Ht2) from 30,500 adults aged 18-96 years living in China. Multiple regression analyses were used to derive four prediction equations by BIA, and double cross-validation techniques and Bland-Altman analyses were used to test agreement. Various muscle mass indices and prevalence rates were depicted by line plots in regard to age trends. RESULTS: Satisfactory results were found in the four prediction models as they had the larger R2 (0.833-0.930) values and low SEE (1.409-2.335 kg) values. The predictive variables included impedance indexes (Ht2/R50, R50/Ht2, R250/Ht2), gender, age, Wt, and Ht. The corresponding prevalence rates of low muscle mass exhibited significant differences according to the various muscle mass indices adjusted for Ht, Wt, or body mass index (BMI), in addition to the cutoff values based on two standard deviations (2SD) of young people or the lower 20% of the study group. CONCLUSIONS: The BIA equations have the potential to be applied as a practical method of quantifying skeletal muscle mass in Chinese adults. However, the operational methods that are most appropriate for determining the degree of low muscle mass that actually contributes to sarcopenia remains inconclusive.
BACKGROUND: It is crucial to assess age-related muscle mass changes and derived indices differences in geriatric medicine. We aimed to develop and validate four bioimpedance analysis (BIA) prediction equations against dual-energy X-ray absorptiometry (DEXA) and magnetic resonance image (MRI) in estimating skeletal muscle mass and to compare the differences among skeletal muscle mass indices, cutoff values, and corresponding prevalence rates of low muscle mass for assessing sarcopenia in Chinese adults. METHODS: We measured the height (Ht), weight (Wt), appendicular lean mass (ALM) or skeletal muscle mass (ASM), total lean body mass (LBM) or skeletal muscle mass (TSM) obtained using DEXA or MRI, and a multi-frequency BIA (BCA II;50, 250 kHz), in 371 adults aged 18.0-87.0 years. We also collected gender, age, Ht, Wt, and impedance indexes (Ht2/R50, Ht2/R250, R50/Ht2, R250/Ht2) from 30,500 adults aged 18-96 years living in China. Multiple regression analyses were used to derive four prediction equations by BIA, and double cross-validation techniques and Bland-Altman analyses were used to test agreement. Various muscle mass indices and prevalence rates were depicted by line plots in regard to age trends. RESULTS: Satisfactory results were found in the four prediction models as they had the larger R2 (0.833-0.930) values and low SEE (1.409-2.335 kg) values. The predictive variables included impedance indexes (Ht2/R50, R50/Ht2, R250/Ht2), gender, age, Wt, and Ht. The corresponding prevalence rates of low muscle mass exhibited significant differences according to the various muscle mass indices adjusted for Ht, Wt, or body mass index (BMI), in addition to the cutoff values based on two standard deviations (2SD) of young people or the lower 20% of the study group. CONCLUSIONS: The BIA equations have the potential to be applied as a practical method of quantifying skeletal muscle mass in Chinese adults. However, the operational methods that are most appropriate for determining the degree of low muscle mass that actually contributes to sarcopenia remains inconclusive.
Authors: Elena Graziano; Maddalena Peghin; Maria De Martino; Chiara De Carlo; Andrea Da Porto; Luca Bulfone; Viviana Casarsa; Emanuela Sozio; Martina Fabris; Adriana Cifù; Bruno Grassi; Francesco Curcio; Miriam Isola; Leonardo Alberto Sechi; Carlo Tascini Journal: Clin Nutr ESPEN Date: 2022-07-19