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Erschienen in:
07.07.2021 | Sleep Breathing Physiology and Disorders • Review
Integrating the precision, sleep, and aerospace medicine fields: a systematic review of the genetic predisposition for obstructive sleep apnea in military aviation
Erschienen in: Sleep and Breathing | Ausgabe 2/2022
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Purpose
Obstructive sleep apnea (OSA) is the most common pathologic sleep disorder with an estimated prevalence in the USA of up to 25% of adult males. With military aviation being heavily comprised of adult men, the impact of OSA on flying operations is concerning as OSA is disqualifying for all flying classes in the US Air Force. In order to minimize the impact of OSA on operations, early identification of at-risk patients is critical in disease management. Individuals could be identified for whom regular polysomnography testing may reveal OSA while mild or sub-clinical, at which point treatment may be initiated in order to promote continued medical qualification for duty and career retention.
Methods
We performed a keyword search of PubMed, EMBASE, and Google Scholar along with searches in the NHGRI/EBI GWAS Catalogue and the Atlas of GWAS Summary Statistics. We included primary research from candidate gene, GWAS, and meta-analyses. We also included other review articles in our search to confirm interpretations and implications of any genetic associations with OSA. Only studies related to OSA susceptibility or risk were included.
Results
We identified 134 publications reporting or reviewing genetic associations with OSA risk. These papers reported 301 variants, of which 195 were unique and 33 were replicated in at least two papers. With respect to the strength of association, 43 variants exhibited odds ratios greater than 2. Finally, there were 84 null results reported, 51 of which were in conflict with reported associations.
Conclusion
There is ample evidence in the literature to confirm that genetics provide an important contribution to OSA development. The high number of strongly associated variants suggests that a polygenic risk model could be created with high predictive value for prognostic screening.