Construct prediction models for low muscle mass with metabolic syndrome using machine learning Article Swipe
Yuanxin Wu
,
Li Fu
,
Hao Chen
,
Liang Shi
,
Meng Yin
,
Fan Hu
,
Gongchang Yu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0331925
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0331925
Background Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posing a major public health challenge. Numerous studies have regarded reduced muscle mass as a reliable indicator for identifying pre-sarcopenia. Nevertheless, there are currently no well-developed methods for identifying low muscle mass in individuals with MetS. Methods A total of 2,467 MetS patients (aged 18–59 years) with low muscle mass assessed by dual-energy X-ray absorptiometry (DXA) were included using data from the 2011–2018 National Health and Nutrition Examination Survey (NHANES). Least Absolute Shrinkage and Selection Operator (LASSO) regression was then used to screen for important features. A total of nine Machine learning (ML) models were constructed in this study. Area under the curve (AUC), F1 Score, Recall, Precision, Accuracy, Specificity, PPV, and NPV were used to evaluate the model’s performance and explain important predictors using the Shapley Additive Explain (SHAP) values. Results The Logistic Regression (LR) model performed the best overall, with an AUC of 0.925 (95% CI: 0.9043, 0.9443), alongside strong F1-score (0.87) and specificity (0.89). Five important predictors are displayed in the summary plot of SHAP values: height, gender, waist circumference, thigh length, and alkaline phosphatase (ALP). Conclusion This study developed an interpretable ML model based on SHAP methodology to identify risk factors for low muscle mass in a young population of MetS patients. Additionally, a web-based tool was implemented to facilitate sarcopenia screening.
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0331925
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331925&type=printable
- OA Status
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- References
- 61
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414084933Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0331925Digital Object Identifier
- Title
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Construct prediction models for low muscle mass with metabolic syndrome using machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-09Full publication date if available
- Authors
-
Yuanxin Wu, Li Fu, Hao Chen, Liang Shi, Meng Yin, Fan Hu, Gongchang YuList of authors in order
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https://doi.org/10.1371/journal.pone.0331925Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331925&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331925&type=printableDirect OA link when available
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0Total citation count in OpenAlex
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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