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The study builds and validates a model using China Health and Retirement Longitudinal Study (CHARLS) data to enable earlier recognition and targeted interventions. Predictors drawn from anthropometrics, laboratory biomarkers, and cognitive function are selected via LASSO and modeled with multivariable logistic regression. Performance is assessed with ROC, calibration, and decision curve analyses, and an online nomogram is produced, with external validation recommended to support generalizability.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-and-validation-of-a-four-year-predictive-model-for-sarcopenia-in-older-adults-insights-from-the-charls-cohort/450184/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-and-validation-of-a-four-year-predictive-model-for-sarcopenia-in-older-adults-insights-from-the-charls-cohort/450184.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What is the goal of the four-year predictive model for sarcopenia?","Question",{"text":113,"@type":114},"The study aims to develop and validate a four-year predictive model that supports early identification of older adults at high risk for sarcopenia and enables targeted interventions.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which data sources and participant criteria were used to build the model?",{"text":118,"@type":114},"Data came from 2,173 CHARLS participants enrolled from the 2011 baseline survey after applying exclusion criteria. Predictors included anthropometric measurements, laboratory biomarkers, and cognitive function.",{"name":120,"@type":111,"acceptedAnswer":121},"How were predictors selected and how was model performance evaluated?",{"text":122,"@type":114},"Feature selection used LASSO regression with 10-fold cross-validation, followed by multivariable logistic regression. Performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).",{"name":124,"@type":111,"acceptedAnswer":125},"What predictors were identified as significant and what tool was created for clinical use?",{"text":126,"@type":114},"Ten significant predictors were reported, including age, BMI, cognition, waist circumference, MCV, platelet count, glucose, creatinine, HDL cholesterol, and hematocrit. 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This study aimed to develop and validate a four-year predictive model for sarcopenia using data from the China Health and Retirement Longitudinal Study (CHARLS) to facilitate early identification and targeted interventions.  \nMethods We analyzed data from 2,173 participants enrolled in the 2011 CHARLS baseline survey after applying exclusion criteria. Predictors including anthropometric measurements, laboratory biomarkers, and cognitive function were assessed. Data were split 7:3 for training and testing, with oversampling applied to address class imbalance in the training set. LASSO regression with 10-fold cross-validation was used for feature selection, followed by multivariable logistic regression to identify significant predictors. Model performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA) .  \nResults Among 2,173 participants, 160 (7.36%) developed sarcopenia over four years. Univariate analyses showed area under the curve (AUC) values ranging from 0.461 to 0.818. Ten significant predictors were identified: age, body mass index (BMI), cognition, waist circumference, mean corpuscular volume (MCV), platelet count, glucose, creatinine, high-density lipoprotein cholesterol (HDL), and hematocrit. Advancing age and higher MCV were positively associated with sarcopenia, whereas higher BMI, larger waist circumference, and better cognitive function were protective. The model showed excellent discrimination with AUCs of 0.859 (training) and 0.842 (testing), good calibration, and favorable clinical utility across a range of thresholds. An online nomogram was developed for clinical application.  \nConclusion This study developed and validated a four-year predictive model for sarcopenia in older adults, integrating key risk factors into a user-friendly nomogram. The model enables early identification of high-risk individuals, supporting targeted interventions and enhancing sarcopenia management strategies. External validation is recommended to confirm generalizability.  \nKeywords Sarcopenia · Predictive model · Older adults · CHARLS  \n􀀍 Zherong Xu [xzr1978@zju.edu.cn](xzr1978@zju.edu.cn)  \n1 Department of Medical Records, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China  \n2 Department of Stomatology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China  \n3 Department of Geriatrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China  \n4 Key Laboratory of Traditional Frailty Research in Older People, Zhejiang Provincial Administration of Traditional Chinese Medicine, Hangzhou, China  \nIntroduction  \nSarcopenia is a progressive and age-related skeletal muscle disorder characterized by the loss of muscle mass and function [1, 2] . Globally, the prevalence of sarcopenia varies according to diagnostic criteria and population demographics, yet it commonly affects 10–16% of older individuals and imposes considerable challenges on healthcare systems [3] . International efforts are necessary to combat sarcopenia and avert related health complications among older adults. In China, where the population aged 60 years and above is growing rapidly, sarcopenia has emerged as a critical public health concern [4] . Epidemiological studies indicate that sarcopenia rates in Chinese communities are ","cbCaiuZS2ry40mDk","https://ap.wps.com/l/cbCaiuZS2ry40mDk","pdf",2299703,16,"English","# Abstract\n# Background\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What is the goal of the four-year predictive model for sarcopenia?\",\"answer\":\"The study aims to develop and validate a four-year predictive model that supports early identification of older adults at high risk for sarcopenia and enables targeted interventions.\"},{\"question\":\"Which data sources and participant criteria were used to build the model?\",\"answer\":\"Data came from 2,173 CHARLS participants enrolled from the 2011 baseline survey after applying exclusion criteria. Predictors included anthropometric measurements, laboratory biomarkers, and cognitive function.\"},{\"question\":\"How were predictors selected and how was model performance evaluated?\",\"answer\":\"Feature selection used LASSO regression with 10-fold cross-validation, followed by multivariable logistic regression. Performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA).\"},{\"question\":\"What predictors were identified as significant and what tool was created for clinical use?\",\"answer\":\"Ten significant predictors were reported, including age, BMI, cognition, waist circumference, MCV, platelet count, glucose, creatinine, HDL cholesterol, and hematocrit. The study created an online nomogram for clinical application and recommends external validation.\"}]","Development and validation of a Four-Year predictive model for sarcopenia in older adults - insights from the CHARLS cohort | PDF",1790732366]