Key result
Random Forest model combining BMI, Barthel Index, grip strength, and calf circumference predicts sarcopenic obesity with ~0.84 AUC.
Why the study?
Sarcopenic obesity in older adults is tied to higher disability and mortality, prompting the development and validation of machine learning models to identify affected patients.
Can a machine learning model using simple clinical predictors accurately identify sarcopenic obesity in older adults?
Cross-Sectional (n=386)
No
Can a machine learning model using simple clinical predictors accurately identify sarcopenic obesity in older adults?
A Random Forest machine learning model utilizing four simple clinical predictors (BMI, Barthel Index score, grip strength, and calf circumference) can reliably identify sarcopenic obesity in older adults.
May aid sarcopenic obesity screening with routine measures; hypothesis-generating and requires prospective validation before clinical use.
Background: In the context of the rapidly aging global population, sarcopenic obesity (SO) in older adults is associated with significantly higher rates of disability and mortality. SO has become a serious and critical public health concern. This study aimed to develop and validate predictive models using machine learning (ML) to identify SO in patients. Methods: Data from 386 participants collected at the Affiliated Hospital of Qingdao University were divided into an 8:2 ratio, with 80% used for training and 20% for testing. Univariate analysis was performed to identify the factors correlated with SO, and multivariate logistic regression analysis was performed to determine the independent factors influencing SO. The Shapley Additive exPlanations (SHAP) diagram was used to illustrate the importance of variables in the model. To develop a predictive model for SO, we used five models and applied internal five-fold cross-validation to determine the most suitable hyperparameters for the model. Results: Among 386 participants, 61 were diagnosed with sarcopenic obesity (15.8%). We identified four independent predictive factors, namely BMI, Barthel Index score, grip strength, and calf circumference. Notably, calf circumference plays an important role in assessing the risk of SO in older adults. The area under the curve (AUC) values of the test set for the Random forest (RF), naive Bayes (NB), Light Gradient Boosting Machine (LightGBM), k-nearest neighbor algorithm (KNN), and eXtreme Gradient Boosting (XGBoost) models were recorded as 0.839, 0.815, 0.808, 0.794, and 0.798, respectively. Among these models, the RF model exhibited the best average performance in the training set, with an AUC value of 0.839. Conclusion: We constructed a predictive model based on the results of the RF model, combining four clinical predictors-BMI, Barthel Index score, grip strength, and calf circumference-to reliably predict SO.
No takes yet. Share an insight, caveat, or question.
Xu et al. (2025) conducted a cross-sectional in Sarcopenic obesity (n=386). Random Forest (RF) machine learning model vs. Other machine learning models (NB, LightGBM, KNN, XGBoost) was evaluated on Area under the curve (AUC) for predicting sarcopenic obesity. A Random Forest machine learning model combining BMI, Barthel Index score, grip strength, and calf circumference reliably predicted sarcopenic obesity with an AUC of 0.839.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: