Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence, Severity, and Treatment Response

Katie L.J. Cederberg, Umaer Hanif, Vicente Peris Sempere, Julien Hédou, Eileen B. Leary, Logan D. Schneider, Ling Lin, Jing Zhang, Anne M. Morse, Adam Blackman, Paula K. Schweitzer, Suresh Kotagal, Richard Bogan, Clete A. Kushida, Yo El S. Ju, Nayia Petousi, Chris D. Turnbull, Emmanuel Mignot*, The STAGES Cohort Investigator Group

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

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.

Original languageEnglish
Article number7983
JournalInternational Journal of Molecular Sciences
Volume23
Issue number14
Number of pages19
ISSN1661-6596
DOIs
Publication statusPublished - 2022

Keywords

  • Apnea–hypopnea index
  • Biomarkers
  • Machine learning
  • Obstructive sleep apnea
  • Proteomics
  • Treatment

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