Exhaled Breath Analysis in Obstructive Sleep Apnea Syndrome: A Review of the Literature

Exhaled Nitric Oxide fraction in asthma and obstructive sleep apnea among children at high altitudes. A cross-sectional study

INTRODUCTION

Exhaled nitric oxide fraction (FeNO) is employed for the diagnosis and phenotyping of asthma as an inflammatory biomarker of the airway. Limited evidence exists regarding its behavior in the presence of asthma and obstructive sleep apnea (OSA). Our objective was to determine whether FeNO levels are associated with the severity of OSA or the coexistence of asthma and OSA in residents at high altitudes.

MATERIALS AND METHODS

Observational, analytical, cross-sectional study in children aged 5-16 years residing at 2600 m above sea level treated at a Sleep Study Center between 2019 and 2021. We conducted a medical history, polysomnogram, and measurement of FeNO levels. The children were categorized into four groups: OSA, asthma, asthma with OSA, and controls (without asthma or OSA). FeNO levels among the groups were compared using the Kruskal-Wallis test, and correlations were explored using the Spearman correlation coefficient. Analyses considered statistical significance at a two-tailed p-value <0.05.

RESULTS

Among the 261 included children, 68 (26.1 %) had OSA, 42 (16.1 %) were diagnosed with asthma, 109 (41.8 %) had both asthma and OSA, and 42 (16.1 %) were controls. Their FeNO medians were 10 ppb, 18.5 ppb, 15 ppb, and 14 ppb, respectively, with no significant differences between the evaluated groups (p = 0.263). We found no correlation between FeNO and apnea-hypopnea index and obstructive apnea index even for the groups of patients with FeNO >20 ppb and FeNO >35 ppb (>75th percentile). In the adjusted model, a significant association was observed between asthma and FeNO levels.

CONCLUSIONS

Our findings suggest that FeNO measurements in children would not allow establishing this biomarker as part of the diagnosis of OSA. However, these findings may be related to high altitude.

Fractional exhaled nitric oxide-a possible biomarker for risk of obstructive sleep apnea in snorers

STUDY OBJECTIVES

Airway inflammation in patients with obstructive sleep apnea (OSA) has been described and can be assessed by measuring the biomarker fractional exhaled nitric oxide (FeNO). In this pilot study, we investigated FeNO measurements in identification of OSA among persons with snoring.

METHODS

In this study we aimed to investigate (1) if FeNO could be used in screening for OSA, (2) if daytime sleepiness correlated to FeNO levels, and (3) whether asthma affected FeNO levels. Persons with snoring were prospectively included in three primary care ear, nose, and throat clinics. Patients underwent spirometry, FeNO tests, and partial polygraphy. They filled out questionnaires on sinonasal and asthma symptoms, daytime sleepiness, and quality of life. Current smokers, patients with upper airway inflammatory conditions, and patients treated with steroids were excluded.

RESULTS

Forty-nine individuals were included. Median apnea-hypopnea index was 11.4, mean age was 50.9 years, and 29% were females. OSA was diagnosed in 73% of the patients of whom 53% had moderate-severe disease. Patients with moderate-severe OSA had significantly higher FeNO counts than patients with no or mild OSA (P = .024). Patients younger than 50 years with a FeNO below 15 had the lowest prevalence of moderate-severe OSA. No correlation was found between FeNO measurements and daytime sleepiness, and asthma did not affect FeNO levels.

CONCLUSIONS

We found a low prevalence of moderate-severe OSA in persons with snoring when FeNO and age were low. This might be considered in a future screening model, though further studies testing the FeNO cutoff level and the diagnostic accuracy are warranted.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: NO Measurements in Screening for Asthma and OSA, in Patients With Severe Snoring; URL: https://clinicaltrials.gov/study/NCT03964324; Identifier: NCT03964324.

CITATION

Kiaer E, Ravn A, Jennum P, et al. Fractional exhaled nitric oxide-a possible biomarker for risk of obstructive sleep apnea in snorers. J Clin Sleep Med. 2024;20(1):85-92.

Efficacy analysis of non-invasive positive pressure ventilation in elderly patients with heart failure complicated with obstructive sleep apnea syndrome

BACKGROUND

It is recommended to use non-invasive positive pressure ventilation in elderly patients with heart failure combined with obstructive sleep apnea syndrome (OSAS).

OBJECTIVE

To study the therapeutic effect of non-invasive positive pressure ventilation on elderly patients with heart failure complicated with OSAS.

METHODS

Using the random number table method, 119 elderly patients with heart failure complicated with OSAS who were admitted to our hospital from April 2020 to April 2021 were divided into the observation (60 cases) and control (59 cases) groups. On the basis of conventional drug treatment, patients in the observation group were treated with non-invasive positive pressure ventilation, and patients in the control group were treated with low-flow oxygen inhalation. The sleep quality, hemoglobin, red blood cells, erythropoietin, pro-brain natriureticpeptide (pro-BNP) levels and blood pressure were compared.

RESULTS

After treatment, levels of hemoglobin, erythrocytes, erythropoietin, pro-BNP, blood pressure and sleep apnea-hypopnea index in the observation group were lower before treatment and in the control group in the same period. The oxygen saturation was higher before treatment and the control group in the same period (P< 0.05). The overall satisfaction with sleep quality in the observation group was higher (P< 0.05).

CONCLUSION

Non-invasive positive pressure ventilation can improve blood oxygen saturation and sleep quality in elderly patients with heart failure complicated with OSAS, and reduce pro-BNP level.

The relationship between obstructive sleep apnea and asthma severity and vice versa: a systematic review and meta-analysis

BACKGROUND

There is a great association between the prevalence of obstructive sleep apnea (OSA) and asthma. Nonetheless, whether OSA impacts lung function, symptoms, and control in asthma and whether asthma increases the respiratory events in OSA are unknown. This meta-analysis aimed to examine the relationship between obstructive sleep apnea and asthma severity and vice versa.

METHODS

We carried out a systematic search of PubMed, EMBASE, and Scopus from inception to September 2022. Primary outcomes were lung function, parameters of polysomnography, the risk of OSA in more severe or difficult-to-control asthmatic patients, and the risk of asthma in patients with more severe OSA. Heterogeneity was examined with the Q test and I² statistics. We also performed subgroup analysis, Meta-regression, and Egger's test for bias analysis.

RESULTS

34 studies with 27,912 subjects were totally included. The results showed that the comorbidity of OSA aggravated lung function in asthmatic patients with a consequent decreased forced expiratory volume in one second %predicted (%FEV1) and the effect was particularly evident in children. %FEV1 tended to decrease in adult asthma patients complicated with OSA, but did not reach statistical significance. Interestingly, the risk of asthma seemed to be slightly lower in patients with more severe OSA (OR = 0.87, 95%CI 0.763-0.998). Asthma had no significant effect on polysomnography, but increased daytime sleepiness assessed by the Epworth Sleepiness Scale in OSA patients (WMD = 0.60, 95%CI 0.16-1.04). More severe asthma or difficult-to-control asthma was independently associated with OSA (odds ratio (OR) = 4.36, 95%CI 2.49-7.64).

CONCLUSION

OSA was associated with more severe or difficult-to-control asthma with decreased %FEV₁ in children. The effect of OSA on lung function in adult patients should be further confirmed. Asthma increased daytime sleepiness in OSA patients. More studies are warranted to investigate the effect of asthma on OSA severity and the impact of different OSA severity on the prevalence of asthma. It is strongly recommended that people with moderate-to-severe or difficult-to-control asthma screen for OSA and get the appropriate treatment.

Current and future strategies for diagnostic and management of obstructive sleep apnea

INTRODUCTION

Obstructive sleep apnea (OSA) is a common sleep disorder with multiple comorbidities including hypertension, diabetes, and cardiovascular disorders. Detected based on an overnight sleep study is called polysomnography (PSG); OSA still remains undiagnosed in majority of the population mainly attributed to lack of awareness. To overcome the limitations posed by PSG such as patient discomfort and overnight hospitalization, newer technologies are being explored. In addition, challenges associated with current management of OSA using continuous positive airway pressure (CPAP), etc. presents several pitfalls.

AREAS COVERED

Conventional and modern detection/management techniques including PSG, CPAP, smart wearable/pillows, bio-motion sensors, etc., have both pros and cons. To fulfill the limitations in OSA diagnostics, there is an imperative need for new technology for screening of symptomatic and more importantly asymptomatic OSA patients to reduce the risk of several associated life-threatening comorbidities. In this line, molecular marker-based diagnostics have shown great promises.

EXPERT OPINION

A detailed overview is presented on the OSA management and diagnostic approaches and recent advances in the molecular screening methods. The potentials of biomarker-based detection and its limitations are also portrayed and a comparison between the standard, current modern approaches, and promising futuristic technologies for OSA diagnostics and management is set forth.ABBREVIATIONS AHI: Apnea hypopnea index; AI: artificial intelligence; CAM: Cell adhesion molecules; CPAP: Continuous Positive Airway Pressure; COVID-19: Coronavirus Disease 2019; CVD: Cardiovascular disease; ELISA: Enzyme linked immunosorbent assay; HSAT: Home sleep apnea testing; IR-UWB: Impulse radio-ultra wideband; MMA: maxillomandibular advancement; PSG: Polysomnography; OSA: Obstructive sleep apnea; SOD: Superoxide dismutase; QD: Quantum dot.

Classification of sleep apnea using EMD-based features and PSO-trained neural networks

In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN's performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN's accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

Characterization of inflammatory profile by breath analysis in chronic coronary syndromes

AIMS

Exhaled breath contains thousands of volatile organic compounds (VOCs) produced during various metabolic processes both in health and disease.Analysis of breath with electronic nose BIONOTE-V allows modifications of exhaled VOCs to be studied, which are clinically recognized to be a marker for several disorders, including heart failure. New noninvasive tests based on VOCs analysis might be a useful tool for early detection of chronic coronary syndromes (CCS).

METHODS

Exhaled air was collected and measured in individuals with an indication to perform invasive coronary angiography (ICA). All patients' samples were obtained before ICA.

RESULTS

Analysis with BIONOTE-V was performed in a total cohort of 42 patients consecutively enrolled, of whom 19 did not require myocardial revascularization and 23 with indication for myocardial revascularization. BIONOTE-V was able to correctly identify 18 out of 23 patients affected by severe coronary artery disease (sensitivity = 78.3% and specificity = 68.4%). Our predicted model had a tight correlation with SYNTAX score (error of the BIONOTE-V = 15).

CONCLUSION

CCS patients have a distinctive fingerprint of exhaled breath, and analysis by BIONOTE-V has the potential for identifying these patients. Moreover, it seems that this technique can correctly identify patients according to anatomical disease severity at ICA. If the preliminary data of this proof of concept study will be confirmed, this rapid and noninvasive diagnostic tool able to identify CCS might have an impact in routine clinical practice.

Risk factors and fraction of exhaled nitric oxide in obstructive sleep apnea in adults

OBJECTIVE

This study aimed to evaluate the relationship between obstructive sleep apnea (OSA) and the fraction of exhaled nitric oxide (F_ENO), and to assess the effect of risk factors of airway inflammation on OSA.

METHODS

Medical records of patients in the Respiratory Sleep Center at Chao-Yang Hospital in Beijing between January 2015 and June 2017 were analyzed. All patients were diagnosed with OSA. Data of the medical history, clinical examinations, F_ENO, and upper airway computed tomographic findings were collected. Logistic regression was used to evaluate risk factors of OSA.

RESULTS

A total of 181 patients were admitted to the Respiratory Sleep Center during the study and 170 had a diagnosis of OSA and were included in the study. Single factor analysis showed that male sex, age, body mass index, smoking index, alcohol consumption, F_ENO, soft palate thickness, soft palate length, the narrowest transverse diameter of the upper airway, tonsil size, and nasal sinusitis were risk factors for sleep-disordered breathing and disease severity.

CONCLUSIONS

Male sex, age, body mass index, F_ENO, the narrowest transverse diameter of the upper airway, and normal tonsil size are associated with OSA and disease severity. The severity of OSA is associated with F_ENO levels.

Sleep-wake stage detection with single channel ECG and hybrid machine learning model in patients with obstructive sleep apnea

Sleep staging is an important step in the diagnosis of obstructive sleep apnea (OSA) and this step is performed by a physician who visually scores the electroencephalography, electrooculography and electromyography signals taken by the polysomnography (PSG) device. The PSG records must be taken by a technician in a hospital environment, this may suggest a drawback. This study aims to develop a new method based on hybrid machine learning with single-channel ECG for sleep-wake detection, which is an alternative to the sleep staging procedure used in hospitals today. For this purpose, the heart rate variability signal was derived using electrocardiography (ECG) signals of 10 OSA patients. Then, QRS components in different frequency bands were obtained from the ECG signal by digital filtering. In this way, nine more signals were obtained in total. 25 features from each of the 9 signals, a total of 225 features have been extracted. Fisher feature selection algorithm and principal component analysis were used to reduce the number of features. Finally, features were classified with decision tree, support vector machines, k-nearest neighborhood algorithm and ensemble classifiers. In addition, the proposed model has been checked with the leave one out method. At the end of the study, it was shown that sleep-wake detection can be performed with 81.35% accuracy with only three features and 87.12% accuracy with 10 features. The sensitivity and specificity values for the 3 features were 0.85 and 0.77, and for 10 features the sensitivity and specificity values were 0.90 and 0.85 respectively. These results suggested that the proposed model could be used to detect sleep-wake stages during the OSA diagnostic process.

Metabolomics in Sleep, Insomnia and Sleep Apnea

Sleep-wake disorders are highly prevalent disorders, which can lead to negative effects on cognitive, emotional and interpersonal functioning, and can cause maladaptive metabolic changes. Recent studies support the notion that metabolic processes correlate with sleep. The study of metabolite biomarkers (metabolomics) in a large-scale manner offers unique opportunities to provide insights into the pathology of diseases by revealing alterations in metabolic pathways. This review aims to summarize the status of metabolomic analyses-based knowledge on sleep disorders and to present knowledge in understanding the metabolic role of sleep in psychiatric disorders. Overall, findings suggest that sleep-wake disorders lead to pronounced alterations in specific metabolic pathways, which might contribute to the association of sleep disorders with other psychiatric disorders and medical conditions. These alterations are mainly related to changes in the metabolism of branched-chain amino acids, as well as glucose and lipid metabolism. In insomnia, alterations in branched-chain amino acid and glucose metabolism were shown among studies. In obstructive sleep apnea, biomarkers related to lipid metabolism seem to be of special importance. Future studies are needed to examine severity, subtypes and treatment of sleep-wake disorders in the context of metabolite levels.

A Randomized, Controlled, Pilot Study of CPAP for Patients with Chronic Cough and Obstructive Sleep Apnea

BACKGROUND

An association between chronic cough and obstructive sleep apnea (OSA) has been reported in prior studies with resolution or improvement in cough after continuous positive airway pressure (CPAP) therapy. Controlled studies of the benefit of CPAP on cough-quality of life measures have not been conducted.

RESEARCH QUESTION

Does CPAP therapy for OSA improve cough in patients with chronic unexplained cough?

STUDY DESIGN AND METHODS

Patients with unexplained chronic cough (> 2 months duration of cough) and OSA were randomized to receive either CPAP or sham CPAP therapy for 6 weeks. The primary end point was the change in health status assessed with the Leicester Cough Questionnaire (LCQ) in patients treated with CPAP vs. sham CPAP. Secondary end points were changes in exhaled breath condensate markers of airway inflammation (interleukin-6, nitrite/nitrates, hydrogen peroxide and 8-isoprostanes).

RESULTS

A total of 22 patients with chronic unexplained cough and OSA were randomized of whom18 completed 6 weeks of treatments with either CPAP or sham CPAP. The CPAP vs. sham CPAP-treated group were comparable in terms of sex distribution, body mass index, and OSA severity. Following CPAP therapy, there was a significantly greater improvement in total LCQ scores as compared to those treated with sham therapy (ANCOVA p value 0.016). No significant differences were noted in the exhaled breath condensate marker changes between CPAP-treated vs. sham CPAP-treated groups.

CONCLUSION

Treatment of comorbid OSA in patients with chronic cough improved cough quality of life measures following treatment of OSA with CPAP in this pilot study. Larger studies to understand this association and unravel mechanisms of CPAP benefit in chronic cough need to be undertaken. Clinical Trial Registration NCT03172130.

Obstructive Sleep Apnea: A View from the Back Door

Obstructive sleep apnea (OSA) is a common disease that may affect up to 50% of the adult population and whose incidence continues to rise, as well as its health and socio-economic burden. OSA is a well-known risk factor for motor vehicles accidents and decline in work performance and it is frequently accompanied by cardiovascular diseases. The aim of this Special Issue is to focus on the characteristics of OSA in special populations which are less frequently investigated. In this regard, seven groups of experts in the field of sleep medicine gave their contribution in the realization of noteworthy manuscripts which will support all physicians in improving their understanding of OSA with the latest knowledge about its epidemiology, pathophysiology and comorbidities in special populations, which will serve as a basis for future research.