Association between the triglyceride-glucose-waist circumference index and obstructive sleep apnoea: a hospital-based case-control study in China and the development of machine learning models.

OBJECTIVES

We aimed to examine the association between the triglyceride-glucose-waist circumference (TyG-WC) index and obstructive sleep apnoea (OSA) and to develop machine learning (ML) models to identify OSA.

DESIGN

Case–control study and development of six ML models.

SETTING

A sleep medicine centre at a tertiary hospital in China.

PARTICIPANTS

534 patients with OSA diagnosed by polysomnography between 2017 and 2025, matched with 534 OSA-free individuals.

PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was the association between TyG-WC and OSA, assessed using multivariable logistic regression adjusted for age, sex, education, marital status, hypertension, diabetes, smoking and alcohol use. Secondary outcomes were the dose-response and threshold effects, subgroup interactions, external consistency testing in the National Health and Nutrition Examination Survey (NHANES), sensitivity analyses and development of ML models (LightGBM, XGBoost, Random Forest, Support Vector Machine, K-Nearest Neighbours, Naive Bayes) incorporating TyG-WC and the same clinical variables, evaluated for discrimination, calibration and clinical utility, interpreted using SHapley Additive exPlanations (SHAP).

RESULTS

TyG-WC was linearly associated with OSA (adjusted OR=1.067, 95% CI 1.055 to 1.079; p<0.001; p for non-linearity=0.827), and this association remained robust in NHANES and sensitivity analyses. Significant interactions were observed by age and hypertension status. In the test set, six ML models demonstrated good predictive performance (area under the receiver operating characteristic curve (AUC)=0.732–0.803). XGBoost achieved the highest discrimination (AUC=0.803, 95% CI 0.755 to 0.851), with no evidence of miscalibration (Brier score=0.183; Hosmer-Lemeshow p>0.05) and positive net benefit across threshold probabilities. SHAP identified TyG-WC as the most influential predictor across all ML models.

CONCLUSIONS

TyG-WC was associated with OSA. ML models incorporating TyG-WC and clinical variables achieved good performance, supporting TyG-WC’s potential as an accessible risk marker.

TRIAL REGISTRATION: Not applicable.