Depression Linked to Higher Risk of Sleep Apnea, NHANES Study Finds
China: A recent study leveraging data from the National Health and Nutrition Examination Survey (NHANES) has revealed that individuals with depression face a significantly higher risk of developing obstructive sleep apnea (OSA).
Published in BMC Psychiatry, the research was conducted by Xiangyang Cheng from the Department of Anesthesiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China, and colleagues.
The study analyzed data from 14,492 adults across the United States to explore the complex relationship between depression and OSA. Researchers found that depression increased the likelihood of OSA by 31% (odds ratio [OR] = 1.31), with the association being particularly strong among individuals without insomnia (OR = 1.65). This suggests that depressive symptoms independently contribute to the risk of sleep-disordered breathing, even when sleep disturbances such as insomnia are absent.
To delve deeper, the research team employed multiple machine learning (ML) algorithms to predict OSA risk among participants with depression.
Key Findings:
- The neural network model was identified as the most accurate predictive tool for assessing OSA risk in individuals with depression.
- It achieved the highest values for Youden’s Index, area under the curve (AUC), and Kappa scores compared to other models.
- The Shapley Additive Explanations (SHAP) interpretability method was used to identify the most influential predictors of OSA.
- Key predictors included body mass index (BMI), age, hypertension, sex, marital status, caffeine intake, alcohol consumption, and dietary fat intake.
The findings highlight the intricate interplay between mental health, lifestyle habits, and physical conditions. Depression and OSA share overlapping risk pathways, including inflammation, hormonal changes, and altered autonomic nervous system activity, which may explain their frequent co-occurrence. Early identification of high-risk individuals could help clinicians intervene before the onset of more severe complications.
While the results provide valuable insight, the study also acknowledged several limitations. Because the NHANES data are cross-sectional, causal relationships between depression and OSA cannot be definitively established. Both conditions were self-reported by participants, which may lead to diagnostic inaccuracies. Additionally, the modest sample size of depressed participants (n = 1,212) raises the potential for overfitting in predictive models. The researchers also noted that insomnia could confound the depression–OSA association, potentially influencing the accuracy of risk estimates.
Despite these constraints, the use of machine learning offers a promising direction for precision screening and risk stratification. By integrating demographic, dietary, and health-related data, the models provide a more holistic understanding of OSA risk in depressed patients.
Dr. Cheng and colleagues concluded that depression is significantly associated with obstructive sleep apnea, highlighting the importance of screening for OSA symptoms in individuals with depressive disorders. They emphasized the potential of machine learning–based tools to enhance early detection and guide personalized management strategies. However, further validation in larger and more diverse populations is essential before clinical application.
Reference:
Cheng, X., Liu, F., Zhang, X. et al. Investigating the role of depression in obstructive sleep apnea and predicting risk factors for OSA in depressed patients: machine learning-assisted evidence from NHANES. BMC Psychiatry 25, 964 (2025). https://doi.org/10.1186/s12888-025-07414-x
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