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A more accurate method to estimate muscle mass: A new estimation equation
BACKGROUND: Measurement of muscle mass is important in the diagnosis of sarcopenia. Current measurement equipment are neither cost‐effective nor standardized and cannot be used in a variety of medical settings. Some simple measurement tools have been proposed that are subjective and unvalidated. We...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401528/ https://www.ncbi.nlm.nih.gov/pubmed/37203296 http://dx.doi.org/10.1002/jcsm.13254 |
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author | Shi, Shanshan Chen, Weihua Jiang, Yizhou Chen, Kaihong Liao, Ying Huang, Kun |
author_facet | Shi, Shanshan Chen, Weihua Jiang, Yizhou Chen, Kaihong Liao, Ying Huang, Kun |
author_sort | Shi, Shanshan |
collection | PubMed |
description | BACKGROUND: Measurement of muscle mass is important in the diagnosis of sarcopenia. Current measurement equipment are neither cost‐effective nor standardized and cannot be used in a variety of medical settings. Some simple measurement tools have been proposed that are subjective and unvalidated. We aimed to develop and validate a new estimation equation in a more objective and standardized way, based on current proven variables that accurately reflect muscle mass. METHODS: Cross‐sectional analysis with The National Health and Nutrition Examination Survey database for equation development and validation. Overall, 9875 participants were included for development (6913 participants) and validation (2962 participants), for whom the database included demographic data, physical measurements, and main biochemical indicators. Appendicular skeletal muscle mass (ASM) was estimated by dual‐energy x‐ray absorptiometry (DXA) and low muscle mass was defined by reference to five international diagnostic criteria. Linear regression was used to estimate the logarithm of the actual ASM from demographic data, physical measurements, and biochemical indicators. RESULTS: This study of 9875 participants comprised 4492 females (49.0%), with a weighted mean (SE) age of 41.83 (0.36) years and range of 12 to 85 years. The estimated ASM equations performed well in the validation data set. The variability in estimated ASM was low compared with the actual ASM (R (2): Equation 1 = 0.91, Equation 4 = 0.89), with low bias (median difference: Equation 1 = −0.64, Equation 4 = 0.07; root mean square error: Equation 1 = 1.70 [1.69–1.70], Equation 4 = 1.85 [1.84–1.86]), high precision (interquartile range of the differences: Equation 1 = 1.87, Equation 4 = 2.17), and high efficacy in diagnosing low muscle mass (area under the curve: Equation 1 = 0.91 to 0.95, Equation 4 = 0.90 to 0.94). CONCLUSIONS: The estimated ASM equations are accurate and simple and can be routinely applied clinically to estimate ASM and thus assess sarcopenia. |
format | Online Article Text |
id | pubmed-10401528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104015282023-08-05 A more accurate method to estimate muscle mass: A new estimation equation Shi, Shanshan Chen, Weihua Jiang, Yizhou Chen, Kaihong Liao, Ying Huang, Kun J Cachexia Sarcopenia Muscle Original Articles BACKGROUND: Measurement of muscle mass is important in the diagnosis of sarcopenia. Current measurement equipment are neither cost‐effective nor standardized and cannot be used in a variety of medical settings. Some simple measurement tools have been proposed that are subjective and unvalidated. We aimed to develop and validate a new estimation equation in a more objective and standardized way, based on current proven variables that accurately reflect muscle mass. METHODS: Cross‐sectional analysis with The National Health and Nutrition Examination Survey database for equation development and validation. Overall, 9875 participants were included for development (6913 participants) and validation (2962 participants), for whom the database included demographic data, physical measurements, and main biochemical indicators. Appendicular skeletal muscle mass (ASM) was estimated by dual‐energy x‐ray absorptiometry (DXA) and low muscle mass was defined by reference to five international diagnostic criteria. Linear regression was used to estimate the logarithm of the actual ASM from demographic data, physical measurements, and biochemical indicators. RESULTS: This study of 9875 participants comprised 4492 females (49.0%), with a weighted mean (SE) age of 41.83 (0.36) years and range of 12 to 85 years. The estimated ASM equations performed well in the validation data set. The variability in estimated ASM was low compared with the actual ASM (R (2): Equation 1 = 0.91, Equation 4 = 0.89), with low bias (median difference: Equation 1 = −0.64, Equation 4 = 0.07; root mean square error: Equation 1 = 1.70 [1.69–1.70], Equation 4 = 1.85 [1.84–1.86]), high precision (interquartile range of the differences: Equation 1 = 1.87, Equation 4 = 2.17), and high efficacy in diagnosing low muscle mass (area under the curve: Equation 1 = 0.91 to 0.95, Equation 4 = 0.90 to 0.94). CONCLUSIONS: The estimated ASM equations are accurate and simple and can be routinely applied clinically to estimate ASM and thus assess sarcopenia. John Wiley and Sons Inc. 2023-05-18 /pmc/articles/PMC10401528/ /pubmed/37203296 http://dx.doi.org/10.1002/jcsm.13254 Text en © 2023 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Shi, Shanshan Chen, Weihua Jiang, Yizhou Chen, Kaihong Liao, Ying Huang, Kun A more accurate method to estimate muscle mass: A new estimation equation |
title | A more accurate method to estimate muscle mass: A new estimation equation |
title_full | A more accurate method to estimate muscle mass: A new estimation equation |
title_fullStr | A more accurate method to estimate muscle mass: A new estimation equation |
title_full_unstemmed | A more accurate method to estimate muscle mass: A new estimation equation |
title_short | A more accurate method to estimate muscle mass: A new estimation equation |
title_sort | more accurate method to estimate muscle mass: a new estimation equation |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10401528/ https://www.ncbi.nlm.nih.gov/pubmed/37203296 http://dx.doi.org/10.1002/jcsm.13254 |
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