Evidence map›Paper›PMID 40389905›Full record

ArticleBMC nephrology2025

Gender-specific sarcopenia screening in hemodialysis: insights from lower limb strength and physiological indicators.

Yujie Yang, Hualong Liao, Yang Chen, Ying Qiu, Fei Yan, Ping Fu, Jirong Yue, Yu Chen, Huaihong Yuan

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Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Yujie Yang *Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, 610041, China.
Hualong Liao *Department of Geriatrics and National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yang ChenDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, 610041, China.
Ying QiuDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, 610041, China.
Fei YanChongqing Municipality Clinical Research Center for Geriatric Diseases, School of Medicine, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, China.
Ping FuDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, 610041, China.
Jirong YueDepartment of Geriatrics and National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yu ChenDepartment of Applied Mechanics, College of Architecture and Environment, Sichuan University, Chengdu, Sichuan, China. yu_chen@scu.edu.cn.
Huaihong YuanDepartment of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, 610041, China. yuanhuaihong@wchscu.cn.

Funding

1.3.5 project for disciplines of excellence from West China Hospital of Sichuan University ZYGD23015Major Research Programs of the Science & Technology Department of Sichuan Province 2022ZDZX0032National Natural Science Foundation of China 12102072Project of Sichuan Luzhou Science and Technology Bureau 2022CDLZ-25Sichuan-Chongqing Science and Technology Innovation Cooperation Plan 2024YFHZ0072Sichuan Province science and technology innovation base project 2023ZYD0173
6 · The paper itself

Abstract

objectiveMaintenance hemodialysis (MHD) patients often suffer from sarcopenia, affecting lower limb muscle strength and increasing the risk of falls and mortality. This study aims to develop an auxiliary screening model for sarcopenia in MHD patients based on machine learning methods, utilizing lower limb muscle strength indicators, while paying attention to the gender difference and exploring its value in sarcopenia screening.

methodsThis cross-sectional study collected data from MHD patients at a hemodialysis center in China. Sarcopenia was assessed using the 2019 Asian Working Group for Sarcopenia update. A self-developed lower limb muscle strength testing device was used. Other physiological indicators, including basic information and lab findings, were collected. Participants were grouped into sarcopenia and control groups, with gender-specific binary classification models developed. Stratified shuffling and synthetic minority oversampling techniques were used to build screening classifiers.

resultsData from 164 MHD patients were ultimately collected, including 83 males (41 with possible sarcopenia or sarcopenia) and 81 females (53 with possible sarcopenia or sarcopenia). Gender-specific binary classification models were developed using lower limb muscle strength indicators, with the male model having an AUC of 79% and the female model an AUC of 80%, respectively. Combining lower limb muscle strength with other physiological indicators improved the female model's screening capability, achieving an AUC of 90%.

conclusionThis study demonstrates that the auxiliary screening model for sarcopenia, developed using machine learning methods, highlights the significant value of lower limb muscle strength indicators in identifying sarcopenia in MHD patients. The gender-specific screening models show good discriminatory ability across different genders, providing effective tools for the early screening and management of sarcopenia in MHD patients.

trial registrationChinese Clinical Trial Registry (ChiCTR2100051111), registered on 2021-09-13.

Indexed as

Kidney Failure, ChronicLower ExtremityMuscle StrengthRenal DialysisSarcopeniaAgedChinaCross-Sectional StudiesFemaleHumansMachine LearningMaleMass ScreeningMiddle AgedSex FactorsIdentificationLower limb muscle strengthMachine learningMaintenance hemodialysisSarcopenia

Identifiers

PMID40389905
PMCPMC12090688

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.