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Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning

This study assessed the potential of back extensor strength as an alternative marker of frailty. A total of 560 farmers were included. Computed tomography scans measured fat and muscle mass volumes at the mid-L4 vertebral level. Back extensor strength was measured in a seated posture. Multivariate l...

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Autores principales: Kim, Taewook, Kim, Gowun, Park, Hee-won, Kang, Eun Kyoung, Baek, Sora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10573638/
https://www.ncbi.nlm.nih.gov/pubmed/37834800
http://dx.doi.org/10.3390/jcm12196156
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author Kim, Taewook
Kim, Gowun
Park, Hee-won
Kang, Eun Kyoung
Baek, Sora
author_facet Kim, Taewook
Kim, Gowun
Park, Hee-won
Kang, Eun Kyoung
Baek, Sora
author_sort Kim, Taewook
collection PubMed
description This study assessed the potential of back extensor strength as an alternative marker of frailty. A total of 560 farmers were included. Computed tomography scans measured fat and muscle mass volumes at the mid-L4 vertebral level. Back extensor strength was measured in a seated posture. Multivariate linear regression was used to analyze the associations between back extensor strength and trunk muscle/fat compositions. The participants were divided into two groups based on back extensor strength. Propensity score matching, multivariate logistic regression, and Extreme Gradient Boosting (XGBoost) were employed to evaluate the relationship between Fried’s frailty criteria and back extensor strength. Back extensor strength exhibited positive associations with abdominal muscle volume (r = 1.12) as well as back muscle volume (r = 0.89) (p < 0.05). Back extensor strength was linked to more frail status, such as reduced grip strength, walking speed, and frequent self-reported exhaustion. Multivariate logistic regression indicated that back extensor strength was associated with higher frail status (OR = 0.990), and XGBoost analysis identified back extensor strength as the most important predictor (gain = 0.502) for frailty. The prediction models using grip strength produced similar results (OR = 0.869, gain = 0.482). These findings suggested the potential of back extensor strength as an alternative frailty marker.
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spelling pubmed-105736382023-10-14 Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning Kim, Taewook Kim, Gowun Park, Hee-won Kang, Eun Kyoung Baek, Sora J Clin Med Article This study assessed the potential of back extensor strength as an alternative marker of frailty. A total of 560 farmers were included. Computed tomography scans measured fat and muscle mass volumes at the mid-L4 vertebral level. Back extensor strength was measured in a seated posture. Multivariate linear regression was used to analyze the associations between back extensor strength and trunk muscle/fat compositions. The participants were divided into two groups based on back extensor strength. Propensity score matching, multivariate logistic regression, and Extreme Gradient Boosting (XGBoost) were employed to evaluate the relationship between Fried’s frailty criteria and back extensor strength. Back extensor strength exhibited positive associations with abdominal muscle volume (r = 1.12) as well as back muscle volume (r = 0.89) (p < 0.05). Back extensor strength was linked to more frail status, such as reduced grip strength, walking speed, and frequent self-reported exhaustion. Multivariate logistic regression indicated that back extensor strength was associated with higher frail status (OR = 0.990), and XGBoost analysis identified back extensor strength as the most important predictor (gain = 0.502) for frailty. The prediction models using grip strength produced similar results (OR = 0.869, gain = 0.482). These findings suggested the potential of back extensor strength as an alternative frailty marker. MDPI 2023-09-24 /pmc/articles/PMC10573638/ /pubmed/37834800 http://dx.doi.org/10.3390/jcm12196156 Text en © 2023 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
Kim, Taewook
Kim, Gowun
Park, Hee-won
Kang, Eun Kyoung
Baek, Sora
Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning
title Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning
title_full Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning
title_fullStr Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning
title_full_unstemmed Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning
title_short Back Extensor Strength as a Potential Marker of Frailty Using Propensity Score Matching and Machine Learning
title_sort back extensor strength as a potential marker of frailty using propensity score matching and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10573638/
https://www.ncbi.nlm.nih.gov/pubmed/37834800
http://dx.doi.org/10.3390/jcm12196156
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