Literature DB >> 10865760

Modeling leg sections by bioelectrical impedance analysis, dual-energy X-ray absorptiometry, and anthropometry: assessing segmental muscle volume using magnetic resonance imaging as a reference.

M Elia1, N J Fuller, C R Hardingham, M Graves, N Screaton, A K Dixon, L C Ward.   

Abstract

This study aimed to assess the value of different DXA and BIA models for predicting muscle volume in mid-thigh segments obtained by MRI. Three DXA models were used: in model A, muscle was taken to be equivalent to fat-free soft tissue; in model B the thigh segment was divided into its constituent tissues using fixed assumptions about tissue composition; in model C the assumptions were similar to model B, but with variable distribution of fat and fat-free soft tissue, depending on body mass index. The two BIA models (both parallel tissue resistance models) involved impedance measurements at 50 kHz, and assumptions about either the specific resistivities of all the constituent tissues (model A), or resistivities of only adipose tissue and muscle (model B). Anthropometric estimates (thigh circumference and skinfold thickness) assumed that both limb and muscle circumference were circular. Compared to MRI estimates of muscle mass, those obtained by DXA model A (fat-free soft tissue) were not as good as those obtained using models B and C, although the standard deviations of the differences were similar with all three models. The BIA models were superior to the anthropometric estimates of muscle volume (relative to MRI) with respect to bias, but the standard deviations of the differences were large for both. The intraobserver repeatabilities for muscle volume were < 0.5% for MRI, < 1% for DXA, 1.8% for BIA, and 1.7% for anthropometry (interobserver value for BIA was 3.8% and for anthropometry 3.5%). The study suggests that DXA modeling provides a promising approach for assessing muscle mass in thigh segments, and suggests the potential value of parallel BIA models for groups of individuals but not for individual subjects, possibly because muscle resistivity is influenced not only by its composition but also by the direction of current flow in muscle.

Mesh:

Year:  2000        PMID: 10865760     DOI: 10.1111/j.1749-6632.2000.tb06471.x

Source DB:  PubMed          Journal:  Ann N Y Acad Sci        ISSN: 0077-8923            Impact factor:   5.691


  12 in total

1.  The accuracy of volume estimates using ultrasound muscle thickness measurements in different muscle groups.

Authors:  Masae Miyatani; Hiroaki Kanehisa; Masamitsu Ito; Yasuo Kawakami; Tetsuo Fukunaga
Journal:  Eur J Appl Physiol       Date:  2003-10-21       Impact factor: 3.078

2.  CD34 regulates the skeletal muscle response to hypoxia.

Authors:  Mélissa Pagé; Catherine Maheux; Anick Langlois; Julyanne Brassard; Émilie Bernatchez; Sandra Martineau; Cyndi Henry; Marie-Josée Beaulieu; Ynuk Bossé; Mathieu C Morissette; Richard Debigaré; Marie-Renée Blanchet
Journal:  J Muscle Res Cell Motil       Date:  2019-06-20       Impact factor: 2.698

3.  Muscle strength and its relationship with skeletal muscle mass indices as determined by segmental bio-impedance analysis.

Authors:  Omid Alizadehkhaiyat; David H Hawkes; Graham J Kemp; Anthony Howard; Simon P Frostick
Journal:  Eur J Appl Physiol       Date:  2013-11-01       Impact factor: 3.078

4.  Comparison of DXA and CT in the assessment of body composition in premenopausal women with obesity and anorexia nervosa.

Authors:  Miriam A Bredella; Reza Hosseini Ghomi; Bijoy J Thomas; Martin Torriani; Danielle J Brick; Anu V Gerweck; Madhusmita Misra; Anne Klibanski; Karen K Miller
Journal:  Obesity (Silver Spring)       Date:  2010-01-28       Impact factor: 5.002

5.  Estimation of maximal oxygen uptake by bioelectrical impedance analysis.

Authors:  Alexander Stahn; Elmarie Terblanche; Sven Grunert; Günther Strobel
Journal:  Eur J Appl Physiol       Date:  2005-11-01       Impact factor: 3.078

6.  Active core rewarming avoids bioelectrical impedance changes in postanesthetic patients.

Authors:  Alma Rebeca Gutiérrez-Cruz; Bernardo Soto-Rivera; Bertha Alicia León-Chávez; Ernesto Suaste-Gómez; Daniel Martinez-Fong; Juan Antonio González-Barrios
Journal:  BMC Anesthesiol       Date:  2011-02-16       Impact factor: 2.217

7.  Aged-Related Changes in Body Composition and Association between Body Composition with Bone Mass Density by Body Mass Index in Chinese Han Men over 50-year-old.

Authors:  Ying Jiang; Ying Zhang; Mengmeng Jin; Zhaoyan Gu; Yu Pei; Ping Meng
Journal:  PLoS One       Date:  2015-06-19       Impact factor: 3.240

8.  Predicting body composition using foot-to-foot bioelectrical impedance analysis in healthy Asian individuals.

Authors:  Chun-Shien Wu; Yu-Yawn Chen; Chih-Lin Chuang; Li-Ming Chiang; Gregory B Dwyer; Ying-Lin Hsu; Ai-Chun Huang; Chung-Liang Lai; Kuen-Chang Hsieh
Journal:  Nutr J       Date:  2015-05-19       Impact factor: 3.271

9.  Total body composition estimated by standing-posture 8-electrode bioelectrical impedance analysis in male wrestlers.

Authors:  M-F Cheng; Y-Y Chen; T-R Jang; W-L Lin; J Chen; K-C Hsieh
Journal:  Biol Sport       Date:  2015-11-10       Impact factor: 2.806

10.  The Effect of Subcutaneous Fat on Electrical Impedance Myography: Electrode Configuration and Multi-Frequency Analyses.

Authors:  Le Li; Xiaoyan Li; Huijing Hu; Henry Shin; Ping Zhou
Journal:  PLoS One       Date:  2016-05-26       Impact factor: 3.240

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