Proteomic biomarkers of the apnea hypopnea index and obstructive sleep apnea: Insights into the pathophysiology of presence, severity, and treatment response

Journal article

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.

Authors: Cederberg, KLJ; Hanif, U; Sempere, VP; Petousi, N; Turnbull, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: MDPI
• Journal: International Journal of Molecular Sciences
• Volume: 23
• Issue: 14
• Article number: 7983
• Publication date: 2022-07-20
• Acceptance date: 2022-07-17
• DOI: 10.3390/ijms23147983
• EISSN: 1422-0067
• ISSN: 1661-6596
• Language: English
• Keywords: machine learning; apnea-hypopnea index; FFR; obstructive sleep apnea; biomarkers; treatment; proteomics