Obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) are two chronic diseases that afflict many individuals worldwide with negative effects on health that may overlap in Overlap Syndrome (OS). The aim of our study was to investigate the differences in mortality between OSAS alone and OS and the risk factors involved. The study was conducted on patients with OSAS or OS diagnosis that completed 15-year follow-up between 2005 and 2023. Of these, the clinical, functional, sleep and survival data were registered and analysed. Risk factors were found by regression analysis. 501 patients (428 OSAS and 73 OS) were enrolled. Patients with OS had higher mortality than OSAS (p < 0,001). The morality risk factors for the overall population found were age >65 years (odds ratio (OR) = 10.69 (95%CI 3,85-29,69), p < 0,001) and low forced-expiratory volume in 1-s (FEV₁) (OR = 10.18 (95%CI 2,32-44,68), p = 0,002). In patients with OSAS, age and nocturnal hypoxemia (NH) (OR = 2.41 (95%CI 1,07-5,41), p = 0,03) were risk factors, while adherence to nighttime positive airway pressure (PAP) reduced mortality (OR = 0,36 (95%CI 0,15-0,83), p = 0,017). Multivariate analysis confirmed age and FEV₁ as risk factors in OS. Conversely, the risk factors for the overall population under 65 years were NH, which is confirmed in patients with OSAS alone (OR = 4,72 (95%CI 1,07-20,77), p = 0,04) in whom, on the other hand, PAP compliance reduced the mortality risk. The study suggests that NH is a risk factor for all-cause mortality in sleep disorders by excluding the age; conversely, nighttime PAP improves the survival.

This study investigates volatile organic compound (VOC) profiles in the exhaled breath of normal subjects under different oxygenation conditions—normoxia (FiO2 21%), hypoxia (FiO2 11%), and hyperoxia (FiO2 35%)—using an electronic nose (e-nose). We aim to identify significant differences in VOC profiles among the three conditions utilizing principal component analysis (PCA) and canonical discriminant analysis (CDA). Our results indicate distinct VOC patterns corresponding to each oxygenation state, demonstrating the potential of e-nose technology in detecting physiological changes in breath composition (cross-validated accuracy values: FiO2 21% vs. FiO2 11% = 63%, FiO2 11% vs. FiO2 35% = 65%, FiO2 21% vs. FiO2 35% = 71%, and p < 0.05 for all). This research underscores the viability of breathomics in the non-invasive monitoring and diagnostics of various respiratory and systemic conditions.

Background: Obstructive sleep apnea (OSA) is a heterogeneous disorder associated with substantial cardiometabolic and neurocognitive morbidity. Although the apnea–hypopnea index (AHI) remains the conventional measure of OSA severity, it only partially reflects the underlying pathophysiological complexity. Growing evidence indicates that nocturnal hypoxemia may be a more powerful marker of adverse outcomes than event frequency alone. Therefore, this study aimed to identify distinct OSA phenotypes based on oximetry-derived features and to assess whether these profiles offer additional clinical insight beyond traditional AHI-based classification. Methods: This multicenter retrospective study, part of the Living with OSA and CPAP: The Apulia Region Experience project, included 1386 adults diagnosed with OSA across 15 sleep centers in Southern Italy. Standardized clinical, anthropometric, and polysomnographic (PSG) data were collected. Hierarchical clustering analysis was performed based on PSG oximetry-derived variables. Resulting clusters were compared across demographic, clinical, hypoxemic, and therapeutic features. Results: Three reproducible clusters emerged. Cluster 1 (mild–non-obese) included younger, leaner patients with lower AHI (22.9 ± 10.5 events·h⁻¹), minimal desaturation (T90 5.6 ± 7.6%), and limited comorbidities. Cluster 2 (severe–obese–hypoxemic) represented the most critical phenotype, characterized by marked obesity (BMI 39.2 ± 8.2 kg·m⁻²), severe OSA (AHI 74.9 ± 17.9 events·h⁻¹), profound nocturnal hypoxemia (T90 51.5 ± 28.2%), and a high prevalence of metabolic disorders (76%), requiring higher CPAP pressures and frequent oxygen supplementation. Cluster 3 (older–comorbid) comprised older males (63.7 ± 11.8 years) with moderate-to-severe OSA (AHI 44.8 ± 15.2 events·h⁻¹) and multiple cardiometabolic comorbidities. Conclusions: Oximetry-derived variables identify distinct and clinically meaningful OSA phenotypes that extend beyond traditional AHI-based classification. Recognizing hypoxemia-driven subtypes could improve risk stratification and enable more personalized management strategies in clinical practice.