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Background <p>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.</p> Methods <p>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.</p> Results <p>Higher KDM-Age and PhenoAge accelerations were independently associated with increased symptom-based OSA risk (both <i>P</i>&#xa0;&lt;&#xa0;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.</p> Conclusions <p>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.</p>

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Association of accelerated biological aging with obstructive sleep apnea symptoms and identification of a candidate biomarker gene signature

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Yixuan Wang,
  • Yuhan Wang,
  • Qingfeng Zhang,
  • Jiali Xiong,
  • Beini Zhou,
  • Mengcan Wang,
  • Shujuan Wu,
  • Ke Hu

摘要

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.