# The Predictive Value of TyG-BMI and TG/HDL-C for Metabolic Dysfunction-Associated Steatotic Liver Disease in Obstructive Sleep Apnea: A Single-Center Retrospective Cohort Analysis

## TL;DR

This study shows that TyG-BMI and TG/HDL-C are useful for predicting liver disease in people with sleep apnea, especially when combined with ALT levels.

## Contribution

The study introduces combined models of TyG-BMI and TG/HDL-C with ALT for improved prediction of MASLD in OSA patients.

## Key findings

- TyG-BMI and TG/HDL-C are significant predictors of MASLD in OSA patients.
- Combining TyG-BMI with ALT improves predictive accuracy for MASLD.
- The combination of TG/HDL-C with ALT also enhances predictive performance.

## Abstract

Background/Objectives: This study aimed to evaluate the predictive value of the triglyceride-glucose index with body mass index (TyG-BMI) and the triglyceride-to-high-density lipoprotein-cholesterol (TG/HDL-C) ratio for predicting the occurrence of metabolic dysfunction-associated steatotic liver disease (MASLD) in obstructive sleep apnea (OSA). Methods: Data from patients diagnosed with OSA were analyzed in this retrospective cohort study. The participants were stratified into two groups: OSA alone and OSA with MASLD. The clinical characteristics and polysomnography data were collected. TyG-BMI and TG/HDL-C ratios were categorized into tertiles. Logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to identify risk factors and assess their predictive performance for MASLD in OSA. Results: Among the 133 patients with OSA, 104 (78.2%) were diagnosed with MASLD. Multivariate analysis identified alanine aminotransferase (ALT), alkaline phosphatase, and TyG-BMI as independent risk factors for MASLD development in patients with OSA. Both TyG-BMI and TG/HDL-C ratio were significant predictors of MASLD in this patient population. The optimal cut-off values for TyG-BMI and TG/HDL-C ratio were 0.546 (sensitivity, 79.6%; specificity, 75.0%) and 0.539 (sensitivity, 93.2%; specificity, 60.7%), respectively. Combining TyG-BMI with ALT improved the predictive accuracy, yielding a cutoff of 0.696 (sensitivity, 76.7%; specificity, 92.9%). Similarly, the combination of TG/HDL-C ratio with ALT resulted in a cutoff value of 0.728 (sensitivity, 83.5%; specificity, 89.3%). Conclusions: TyG-BMI and the TG/HDL-C ratio are effective predictors of MASLD in patients with OSA. A combined model incorporating these indices with ALT levels demonstrated enhanced predictive accuracy for MASLD in this population. These indices are well-suited for risk stratification in resource-constrained settings facing a rising dual burden of OSA and MASLD.