Investigators found SESI-HRMS measurements could be useful for breath analysis in conjunction with OSA screening questionnaires.
Findings from a recent observational study confirmed a significant difference between patients with obstructive sleep apnea (OSA) and those without, as well as a correlation between biomarker levels and OSA severity, indicating that secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) may be a viable screening method.1
A panel of 33 metabolites/breath biomarkers were validated in the observational study, confirming previous findings of the OSA-specific metabolic breath pattern in a pilot study.2 Future validation studies are necessary to investigate the value of SESI-HRMS in coordination with OSA screening questionnaires, and additionally, studies should include larger cohorts.
“To the best of our knowledge, this is the first report of a validation of breath biomarkers for OSA. Previous studies using e-noses, offline gas chromatography couple to mass spectrometry, or enzyme immunoassays to analyze exhaled breath condensate have achieved promising results regarding the distinction between OSA patients and controls without OSA from exhaled breath. However, sample sizes in all these studies were limited and none of the results has been validated in an independent cohort of patients,” lead author Nora Nowak, analytical chemistry PhD student, ETH Zurich, et al wrote. “In this study, we could confirm in a large and independent cohort that breath intensities of many of our previously discovered potential biomarkers for OSA differ significantly between OSA patients and controls without OSA.”
READ MORE: FDA Approves Xywav for Idiopathic Hypersomnia
A total of 149 participants with possible OSA were included in the study, all of whom were evaluated for the presence of 33 biomarkers previously identified by SESI-HRMS in a pilot study. The mass-to-charge (m/z) ratio tolerance was set to 0.005 Da. Investigators evaluated 78 of the m/z features, which, in the pilot study, showed significant difference between the continuous positive airway pressure group and the withdrawal group or showed oxygen desaturation index (ODI) as a predictor for OSA. The remaining features were then analyzed for correlation with ODI and Epworth Sleepiness Scale (ESS), as well as significant differences between those with OSA and those without OSA.
Patients had a median age of 53.3 years (standard deviation [SD], 13.7) and a median body mass index of 30.1 kg/m2 (SD, 6.6). When evaluating metabolic patterns in exhaled breath in patients with definitive OSA (ODI >30/hr; or ODI >10/h and ESS score >10 points), to control subjects without relevant OSA (ODI <5/hr; or ODI <10/hr and ESS <11 points), 19 features showed significant differences (P ≤.05).
Researchers also found correlation between disease severity and biomarker levels. A total of 21 features had a significant correlation between breath levels and ODI (P ≤.05) and all except for 1 indicated higher intensities for an increased ODI. Breath intensities and ESS score also showed a significant correlation for 9 features (P ≤.05).
“It seems unlikely that there is one single biomarker, which is sufficient for diagnosing a disease like OSA, associated with complex metabolic and cardiovascular consequences. In contrast, a pattern of several biomarkers is more likely to be disease specific. Therefore, classification algorithms based on machine learning are convenient tools for making clinical diagnoses based on biomarker pattern,” Nowak et al wrote. “Here, we achieved a classification of the validation data set with an AUROC of 0.66, 76% sensitivity and 42% specificity, when we trained the model with the data from the independent patient cohort of our previously reported study.”
Two inclusion criteria for the study were clinical suspicion of sleep apnea, based on an ESS score of greater than 10 points, and an appropriate medical history of snoring, witnessed apneas, and/or choking episodes.
The study was limited by the relative newness of SESI-HRMS technology, as well as the gap between the pilot study and validation study, which allowed for improvements. A lack of standardization of SESI-HRMS is another limitation of the study, investigators noted, in combination with the absence of quality control for real-time breath samples.