PMID: 40186129 https://pubmed.ncbi.nlm.nih.gov/40186129/

Abstract

BACKGROUND/AIMS: The incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) among individuals with hyperuricemia is significantly high. The aim of this study was to identify effective biomarkers for the detection of MASLD among patients with hyperuricemia.

METHOD: We conducted an analysis involving 3424 participants with hyperuricemia from the National Health and Nutrition Examination Survey (1999-2020). To identify potential significant variables, we employed Boruta's algorithm, SHapley Additive exPlanations (SHAP) and random forests. Multivariable logistic regression models were utilized to assess the odds ratio (OR) of developing MASLD. To evaluate the accuracy and clinical value of our prediction model, we employed receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves.

RESULTS: Among the study population of 3424 participants (mean [SD] age, 54 [20] years, 1788 [52.22%] males) with hyperuricemia, 1670 participants had MASLD. Using Boruta's algorithm, SHAP and random forests, our analysis suggested that Triglyceride Glucose-Waist Circumference (TyG_WC) was one of the most significant variables in predicting MASLD risk, with an area under the receiver operating characteristic (AUROC) of 0.865. The restricted curve spline (RCS) revealed a positive association between the odds ratio of TyG_WC and MASLD, when compared with lowest quantile of TyG_WC, the risk of MASLD for highest quantile was 137.96 times higher. The predictive strategy incorporating TyG_WC notably enhanced the clinical model, with threshold probabilities spanning from approximately 0% to 100%, resulting in a significant improvement of the net benefit.

CONCLUSIONS: Our analysis found that TyG_WC was one of the most significant variables in predicting MASLD risk among individuals with hyperuricemia.