Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1785120511133483008 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10573638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kimtaewook backextensorstrengthasapotentialmarkeroffrailtyusingpropensityscorematchingandmachinelearning AT kimgowun backextensorstrengthasapotentialmarkeroffrailtyusingpropensityscorematchingandmachinelearning AT parkheewon backextensorstrengthasapotentialmarkeroffrailtyusingpropensityscorematchingandmachinelearning AT kangeunkyoung backextensorstrengthasapotentialmarkeroffrailtyusingpropensityscorematchingandmachinelearning AT baeksora backextensorstrengthasapotentialmarkeroffrailtyusingpropensityscorematchingandmachinelearning |