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How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study

We aimed to examine associations between skeletal muscle deficits and indices of poor health. Cut-points for skeletal muscle deficits were derived using data from the Geelong Osteoporosis Study and definitions from the revised European Consensus on Definition and Diagnosis and the Foundation for the...

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Autores principales: Sui, Sophia X., Holloway-Kew, Kara L., Hyde, Natalie K., Williams, Lana J., Tembo, Monica C., West, Emma, Pasco, Julie A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143052/
https://www.ncbi.nlm.nih.gov/pubmed/35629032
http://dx.doi.org/10.3390/jcm11102906
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author Sui, Sophia X.
Holloway-Kew, Kara L.
Hyde, Natalie K.
Williams, Lana J.
Tembo, Monica C.
West, Emma
Pasco, Julie A.
author_facet Sui, Sophia X.
Holloway-Kew, Kara L.
Hyde, Natalie K.
Williams, Lana J.
Tembo, Monica C.
West, Emma
Pasco, Julie A.
author_sort Sui, Sophia X.
collection PubMed
description We aimed to examine associations between skeletal muscle deficits and indices of poor health. Cut-points for skeletal muscle deficits were derived using data from the Geelong Osteoporosis Study and definitions from the revised European Consensus on Definition and Diagnosis and the Foundation for the National Institutes of Health. Participants (n = 665; 323 women) aged 60–96 year had handgrip strength measured by dynamometry and appendicular lean mass by whole-body dual-energy X-ray absorptiometry. Physical performance was assessed using the Timed Up and Go test. Sex-specific cut-points were equivalent to two standard deviations below the mean young reference range from the Geelong Osteoporosis Study. Indices of poor health included fractures, falls, and hospitalisations. Low trauma fractures since age 50 year (excluding skull, face, digits) were self-reported and confirmed using radiological reports. Falls (≥1 in the past 12 months) and hospitalisations (past month) were self-reported. Logistic regression models (age- and sex-adjusted) were used to examine associations. Receiver Operating Characteristic curves were applied to determine optimal cut-points for handgrip strength, Timed Up and Go, appendicular lean mass/height(2), and appendicular lean mass/body mass index that discriminated poor health outcomes. There were 48 participants (6.9%) with hospitalisations, 94 (13.4%) with fractures, and 177 (25.3%) with at least one fall (≥1). For all cut-points, low handgrip strength was consistently associated with falls. There was little evidence to support an association between low appendicular lean mass, using any cut-point, and indices of poor health. Optimal cut-offs for predicting falls (≥1) were: handgrip strength 17.5 kg for women and 33.5 kg for men; Timed Up and Go 8.6 s for women and 9.9 s for men; appendicular lean mass/height(2) 6.2 kg/m(2) for women and 7.46 kg/m(2) for men; and appendicular lean mass/body mass index 0.6 m(2) for women and 0.9 m(2) for men. In conclusion, muscle strength and function performed better than lean mass to indicate poor health. These findings add to the growing evidence base to inform decisions regarding the selection of skeletal muscle parameters and their optimal cut-points for identifying sarcopenia.
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spelling pubmed-91430522022-05-29 How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study Sui, Sophia X. Holloway-Kew, Kara L. Hyde, Natalie K. Williams, Lana J. Tembo, Monica C. West, Emma Pasco, Julie A. J Clin Med Article We aimed to examine associations between skeletal muscle deficits and indices of poor health. Cut-points for skeletal muscle deficits were derived using data from the Geelong Osteoporosis Study and definitions from the revised European Consensus on Definition and Diagnosis and the Foundation for the National Institutes of Health. Participants (n = 665; 323 women) aged 60–96 year had handgrip strength measured by dynamometry and appendicular lean mass by whole-body dual-energy X-ray absorptiometry. Physical performance was assessed using the Timed Up and Go test. Sex-specific cut-points were equivalent to two standard deviations below the mean young reference range from the Geelong Osteoporosis Study. Indices of poor health included fractures, falls, and hospitalisations. Low trauma fractures since age 50 year (excluding skull, face, digits) were self-reported and confirmed using radiological reports. Falls (≥1 in the past 12 months) and hospitalisations (past month) were self-reported. Logistic regression models (age- and sex-adjusted) were used to examine associations. Receiver Operating Characteristic curves were applied to determine optimal cut-points for handgrip strength, Timed Up and Go, appendicular lean mass/height(2), and appendicular lean mass/body mass index that discriminated poor health outcomes. There were 48 participants (6.9%) with hospitalisations, 94 (13.4%) with fractures, and 177 (25.3%) with at least one fall (≥1). For all cut-points, low handgrip strength was consistently associated with falls. There was little evidence to support an association between low appendicular lean mass, using any cut-point, and indices of poor health. Optimal cut-offs for predicting falls (≥1) were: handgrip strength 17.5 kg for women and 33.5 kg for men; Timed Up and Go 8.6 s for women and 9.9 s for men; appendicular lean mass/height(2) 6.2 kg/m(2) for women and 7.46 kg/m(2) for men; and appendicular lean mass/body mass index 0.6 m(2) for women and 0.9 m(2) for men. In conclusion, muscle strength and function performed better than lean mass to indicate poor health. These findings add to the growing evidence base to inform decisions regarding the selection of skeletal muscle parameters and their optimal cut-points for identifying sarcopenia. MDPI 2022-05-20 /pmc/articles/PMC9143052/ /pubmed/35629032 http://dx.doi.org/10.3390/jcm11102906 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sui, Sophia X.
Holloway-Kew, Kara L.
Hyde, Natalie K.
Williams, Lana J.
Tembo, Monica C.
West, Emma
Pasco, Julie A.
How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study
title How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study
title_full How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study
title_fullStr How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study
title_full_unstemmed How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study
title_short How Well Do Low Population-Specific Values for Muscle Parameters Associate with Indices of Poor Physical Health? Cross-Sectional Data from the Geelong Osteoporosis Study
title_sort how well do low population-specific values for muscle parameters associate with indices of poor physical health? cross-sectional data from the geelong osteoporosis study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143052/
https://www.ncbi.nlm.nih.gov/pubmed/35629032
http://dx.doi.org/10.3390/jcm11102906
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