ArticleAging clinical and experimental research2025
Development and validation of a Four-Year predictive model for sarcopenia in older adults: insights from the CHARLS cohort.
Article in Aging clinical and experimental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSarcopenia is a progressive skeletal muscle disorder characterized by declining muscle mass and function in older adults. 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.
methodsWe 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).
resultsAmong 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.
conclusionThis 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.