ArticleAging clinical and experimental research2025
A prediction model for the risk of developing mild cognitive impairment in older adults with sarcopenia: evidence from the CHARLS.
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 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and validation of a cardiometabolic multimorbidity prediction model in middle-aged and older adults.Scientific reports · 2026Article
- Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.BMC public health · 2025Article
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Authors and funding
10 authors.
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Abstract
backgroundSarcopenia significantly increases the risk of cognitive impairments in older adults. Early detection of mild cognitive impairment (MCI) in individuals with sarcopenia is essential for timely intervention.
aimsTo develop an accurate prediction model for screening MCI in individuals with sarcopenia.
methodsWe employed machine learning and deep learning techniques to analyze data from 570 patients with sarcopenia from the China Health and Retirement Longitudinal Study (CHARLS). Our model forecasts MCI incidence over the next four years, categorizing patients into low and high-risk groups based on their risk levels.
resultsThe model was constructed using CHARLS data from 2011 to 2015, incorporating eight validated variables. It outperformed logistic regression, achieving an Area Under the Curve (AUC) of 0.708 (95% CI: 0.544-0.844) for the test set and accurately classifying patients' risk in the training set. The deep learning model demonstrated a low false-positive rate of 10.23% for MCI in higher-risk groups. Independent validation using 2015-2018 CHARLS data confirmed the model's efficacy, with an AUC of 0.711 (0.95 CI, 0.588-0.823). An online tool to implement the model is available at http://47.115.214.16:8000/ .
conclusionsThis deep learning model effectively predicts MCI risk in individuals with sarcopenia, facilitating early interventions. Its accuracy aids in identifying high-risk individuals, potentially enhancing patient care.
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