Fig. 5
Evaluating model performance predicting future lipid-regulating drug usage with five different combinations of 28 associated clinical traits and adipose tissue gene expression. (A) Receiver Operating Characteristic (ROC) curves for five predictive models that incorporate various combinations of clinical phenotypes and gene expression for predicting future use of lipid-regulating drugs. Each curve represents one of the combinations outlined in Table 4, with the x-axis reflecting the false positive rate and the y-axis showing the true positive rate. The area under the curve (AUC) value corresponds to the models’ performance, illustrating the trade-offs between sensitivity and specificity across different thresholds. A curve closer to the top-left corner signifies a better-performing model, as evidenced by the highest AUC value associated with the combination of 28 traits and 1212 genes (AUC = 0.919), suggesting this model exhibited the greatest capacity to discriminate future lipid-regulating drug users. (B) Variable importance plot for the XGBoost model that achieved the highest AUC. The x-axis lists the variables, which includes clinical phenotypes such as apolipoprotein B in Serum and ASCVD risk, and the expression of genes such as SERPINA5 and UBBP1. The x-axis quantifies the importance scores, reflecting the extent and frequency to which each variable improves the model’s overall performance.
