Predictive role of the muscle quality index for testosterone deficiency in adult males based on interpretable machine learning methods
Abstract
BACKGROUND: Testosterone deficiency (TD) is a clinically significant condition strongly associated with aging and metabolic syndrome. While previous studies have established links between muscle mass and TD, evidence regarding the relationship between muscle quality index (MQI) and TD remains limited. This study aimed to investigate the association between MQI and TD in adult males in the United States and to develop an interpretable machine learning (ML) model based on SHapley Additive exPlanation (SHAP) for predicting TD risk.
METHODS: We conducted a cross-sectional study using weighted multivariate logistic regression and subgroup analysis to assess the association between MQI and TD. Six ML models incorporating MQI were developed to predict TD risk. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), confusion matrix, F1 score, Brier score, and precision-recall curve. The optimal model was selected based on these metrics and further interpreted using SHAP to elucidate feature importance and decision-making processes.
RESULTS: The study included 2,628 eligible male participants, with a TD prevalence of 25.76%. After adjusting for confounders, each unit increase in MQI was associated with a 52% reduction in TD risk (OR = 0.480, 95% CI: 0.362-0.636, P < 0.001), demonstrating a dose-response relationship. Among the six ML models, the Light Gradient Boosting Machine (LGBM) exhibited the best predictive performance, achieving…
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