Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that is linked to cardiovascular, metabolic, and neurocognitive complications. However, its diagnosis relies on polysomnography, which is complex and resource-intensive, leading to frequent underdiagnosis. Emerging evidence suggests that accelerated biological aging may contribute to OSA pathophysiology, but systematic assessments using biological age metrics are limited. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) were analyzed to evaluate associations between biological age acceleration and symptom-based OSA risk. Weighted multivariable logistic regression was used to assess the relationships of KDM-Age and PhenoAge accelerations with symptom-based OSA risk. Bioinformatics analyses of the GSE135917 dataset identified aging-related differentially expressed genes (DEGs). Machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were used to screen hub genes, which were validated in both external cohorts and a chronic intermittent hypoxia (CIH) mouse model. RESULTS: Higher KDM-Age and PhenoAge accelerations were independently associated with increased symptom-based OSA risk (both P < 0.001). Thirty aging-related DEGs were identified, which were mainly enriched in senescence, inflammatory, and immune pathways. Three hub genes-RBBP4, UCHL1, and ERRFI1-were selected by machine learning and exhibited favorable discriminative potential across validation datasets and the CIH model. In addition, an integrated three-gene predictive model demonstrated promising discriminative ability in the training set and acceptable predictive performance in independent validation datasets. A nomogram integrating these genes showed good calibration and demonstrated value as an exploratory analytical tool at this stage. CONCLUSIONS: Accelerated biological aging is significantly associated with symptom-based OSA risk. The identified three-gene candidate biomarker signature links aging-related alterations to OSA and warrants further validation.