Uric acid to high-density lipoprotein cholesterol ratio as a novel biomarker for sarcopenia: a national study with machine learning insights.

Abstract

Background: Sarcopenia is a major age-related health burden. Although the uric acid to high-density lipoprotein cholesterol ratio (UHR) has been linked to sarcopenia, its underlying pathways and clinical utility remain unclear.

Methods: We analyzed 9853 adults from the China Health and Retirement Longitudinal Study. Sarcopenia was defined by Asian Working Group for Sarcopenia 2019 Consensus criteria. We assessed the UHR-sarcopenia relationship using multivariable logistic regression and restricted cubic splines. Mediation analyses examined roles of insulin resistance (TyG index) and renal function (eGFR). Five machine learning algorithms were developed and evaluated in training (70%) and testing (30%) sets. Feature selection was performed using Boruta and LASSO, and model interpretability was assessed using SHapley Additive exPlanations (SHAP).

Results: Higher UHR was independently associated with lower odds of sarcopenia, with an adjusted odds ratio per-standard deviation increase of 0.75 (95% CI: 0.69-0.81; p < .001), and showed a nonlinear dose-response relationship. UHR correlated with better handgrip strength and chair-stand performance. The TyG index and eGFR mediated 16.44% and 2.03% of this association, respectively. In machine learning analyses, ROC-AUC values ranged from 0.73 to 0.77, with XGBoost showing the best performance. SHAP analysis based on the XGBoost model further identified UHR as the second most important predictor of sarcopenia, following age.

Conclusions: Higher UHR was associated with lower sarcopenia risk in Chinese middle-aged and older adults, partly through metabolic and renal pathways. UHR may serve as a simple biomarker for sarcopenia risk stratification.