Predictive role of the muscle quality index for testosterone deficiency in adult males based on interpretable machine learning methods.

backgroundTestosterone 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.

methodsWe 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.

resultsThe 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 an AUC of 0.746 (95% CI: 0.707-0.790). SHAP analysis revealed that body mass index (BMI) was the most influential feature in the LGBM model, followed by high-density lipoprotein and MQI. Notably, lower MQI values were consistently associated with a higher risk of TD.

conclusionsOur findings indicate that MQI is an independent and reliable predictor of TD in males. The interpretable LGBM model provides a cost-effective and clinically applicable tool for early TD risk assessment. These results underscore the importance of muscle quality in testosterone regulation and may inform preventive strategies to mitigate TD risk in adult males.