Summary The publication of “The Sleep Apnea Syndromes” by Guilleminault et al. in the 1970s hallmarked the discovery of a new disease entity involving serious health consequences. Obstructive sleep apnea was shown to be the most important disorder among the sleep apnea syndromes (SAS). In the course of time, it was found that the prevalence of obstructive sleep apnea reached the proportions of a global epidemic, with a major impact on public health, safety and the economy. Early on, a metric was introduced to gauge the seriousness of obstructive sleep apnea, based on the objective measurement of respiratory events during nocturnal sleep. The apnea index and later on the apnea−hypopnea index, being the total count of overnight respiratory events divided by the total sleep time in hours, were embraced as principle measures to establish the diagnosis of obstructive sleep apnea and to rate its severity. The current review summarises the historical evolution of the apnea−hypopnea index, which has been subject to many changes, and has been criticised for not capturing relevant clinical features of obstructive sleep apnea. In fact, the application of the apnea−hypopnea index as a continuous exposure variable is based on assumptions that it represents a disease state of obstructive sleep apnea and that evocative clinical manifestations are invariably caused by obstructive sleep apnea if the apnea−hypopnea index is above diagnostic threshold. A critical appraisal of the extensive literature shows that both assumptions are invalid. This conclusion prompts a reconsideration of the role of the apnea−hypopnea index as the prime diagnostic metric of clinically relevant obstructive sleep apnea.
Summary In obstructive sleep apnea, patients’ sleep is fragmented leading to excessive daytime sleepiness and co‐morbidities like arterial hypertension. However, traditional metrics are not always directly correlated with daytime sleepiness, and the association between traditional sleep quality metrics like sleep duration and arterial hypertension is still ambiguous. In a development cohort, we analysed hypnograms from mild ( n = 209), moderate ( n = 222) and severe ( n = 272) obstructive sleep apnea patients as well as healthy controls ( n = 105) from the European Sleep Apnea Database. We assessed sleep by the analysis of two‐step transitions depending on obstructive sleep apnea severity and anthropometric factors. Two‐step transition patterns were examined for an association to arterial hypertension or daytime sleepiness. We also tested cumulative distributions of wake as well as sleep‐states for power‐laws (exponent α) and exponential distributions (decay time τ) in dependency on obstructive sleep apnea severity and potential confounders. Independent of obstructive sleep apnea severity and potential confounders, wake‐state durations followed a power‐law distribution, while sleep‐state durations were characterized by an exponential distribution. Sleep‐stage transitions are influenced by obstructive sleep apnea severity, age and gender. N2 → N3 → wake transitions were associated with high diastolic blood pressure. We observed higher frequencies of alternating (symmetric) patterns (e.g. N2 → N1 → N2, N2 → wake → N2) in sleepy patients both in the development cohort and in a validation cohort ( n = 425). In conclusion, effects of obstructive sleep apnea severity and potential confounders on sleep architecture are small, but transition patterns still link sleep fragmentation directly to obstructive sleep apnea‐related clinical outcomes like arterial hypertension and daytime sleepiness.
While continuous positive airway pressure (CPAP) therapy has a strong evidence base for the treatment of obstructive sleep apnoea (OSA), its impact on cardiovascular comorbidity remains unclear. This journal club reviews three recent randomised controlled trials aimed to evaluate the impact of CPAP therapy in secondary prevention of cerebrovascular and coronary heart disease (SAVE trial), comorbid coronary heart disease (RICCADSA trial) and in patients admitted with acute coronary syndrome (ISAACC trial). All three trials included patients with moderate-to-severe OSA and excluded patients with severe daytime sleepiness. When CPAP was compared with usual care, they all reported no difference in a similar primary composite end-point including death from cardiovascular disease, cardiac events, and strokes. These trials faced the same methodological challenges, including a low primary end-point incidence, the exclusion of sleepy patients, and a low CPAP adherence. Therefore, caution must be taken when broadening their results to the wider OSA population. Although randomised controlled trials provide a high level of evidence, they may not be sufficient to capture the diversity of OSA. Large-scale, real-world data may be able to provide a more rounded and generalisable picture of the effects of routine clinical use of CPAP on cardiovascular morbimortality.
Abstract Whole blood carbonic anhydrase activity (CAa) is increased in patients with obstructive sleep apnea (OSA). Our study investigated the influence of positive airway pressure (PAP) or CA inhibitor acetazolamide (ACT) therapy on CAa, OSA and blood pressure. Thirty‐three OSA patients (21 hypertensive, body mass index (BMI) 37 ± 7 kg/m 2 and apnea–hypopnea index (AHI) of 47 ± 31 events/hr) were followed‐up after PAP treatment (compliance, 4.7 ± 1.5 hr/day; duration, median 6 [IQR 6,6] months) (Cohort A). A second OSA Cohort (B) contained nine hypertensive patients (BMI, 29 ± 4 kg/m 2 ; AHI, 39 ± 20 events/hr) with 2‐week treatment of ACT, PAP or ACT + PAP in an open crossover study. CAa was assessed at baseline and at the end of each treatment period. In Cohort A, baseline CAa was higher in hypertensive, compared with normotensive, patients (1,033 ± 204 versus 861 ± 201 units, p = .028). PAP treatment reduced systolic/diastolic blood pressure but not CAa (−9 ± 11/−5 ± 7 mmHg and −20 ± 289 units, p < .001, <.001 and .70). In Cohort B, blood pressure was reduced in both ACT‐treated groups (−10 ± 10/−5 ± 7 mmHg, p = .043 and .019; and −5 ± 5/−13 ± 13 mmHg, p < .001 and .009). AHI was reduced in both groups: ACT only, −17 ± 9 events/hr p = .001; and ACT + PAP, −39 ± 19 events/hr, p < .001. PAP did not change CAa ( p = .98) but activity tended to decrease after ACT with or without PAP ( p = .081 and .056). CAa is elevated in hypertensive OSA patients. Long‐term PAP reduced blood pressure without affecting CAa. ACT reduced blood pressure and CAa. Increased CAa may constitute a physiological characteristic in OSA, contributing to comorbid hypertension.
Background and objectives: Obstructive sleep apnea (OSA) patients with mild upper airway collapsibility represents a target group for potential non-positive airway pressure (PAP) treatment. We studied the clinical characteristics of this endotypic group in a large Pan-European sleep apnea registry. Methods: A retrospective analysis was performed in 2541 OSA patients with fixed PAP treatment (male 74%, age 53±11 years, body mass index [BMI] 33±6 kg/m2, apnea-hypopnea index [AHI] 41±25 events/h at baseline). Therapeutic PAP level ≤8 cm H2O was used as the cut-off to classify patients with mild upper airway collapsibility (Landry SA et al. Sleep 2017; 40:6). Results: Mean pressure of the PAP therapy was 9±2 cm H2O and treatment compliance was 5.1±2.1 h/day. A total of 39% of patients had a PAP level ≤8 cm H2O. In a multivariate logistic regression model, compared to patients with PAP >8 cm H2O, hyperlipidemia was more prevalent in mild airway collapsibility patients (OR [95%CI] 1.23 [1.00-1.51], p=0.047). BMI, waist-to-hip ratio, neck circumference, AHI and diagnosis of hypertension were negatively associated with the mild collapsibility group (0.97 [0.95-0.99], 0.26 [0.10-0.64], 0.96 [0.93-1.00], 0.99 [0.98-0.99], 0.75 [0.61-0.92], p=0.006, 0.003, 0.034, <0.001, and 0.007, respectively). PAP compliance did not differ between the mild and more severe airway collapsibility groups (p=0.89). Conclusion: Body composition, comorbidities and OSA severity predicted mild upper airway collapsibility defined by therapeutic PAP level in the ESADA cohort. PAP-based phenotyping approach may provide a simple tool for personalized medicine in OSA.
Background: COPD and OSA have been associated with low vitamin D (Vit D) serum levels but there are limited data regarding Vit D status in patients with coexistent COPD and OSA (overlap syndrome-OVS). Additionally, low vit D levels may be linked with increased cardiovascular risk and COPD exacerbations. Aim: The aim of the study was to compare Vit D serum levels between patients with OVS, patients with OSA and controls and to explore associations with anthropometric, respiratory and sleep parameters. Methods: Vit D serum levels [assessed by 25(OH)D levels] were measured in 3 prospectively recruited age-, gender-, and BMI-matched groups of patients with OVS, patients with OSA, and controls as verified by overnight polysomnography and PFTs. COPD diagnosis was based on symptoms and a post-bronchodilator FEV1/FVC<0.7; while OSA diagnosis was based on apnea-hypopnea index (AHI)≥5/h along with symptoms. Subjects with AHI<5/h and normal PFTs were considered as controls. Each group included 30 individuals. Results: Patients with OVS exhibited decreased serum 25(OH)D levels compared with OSA patients [14.5 (12.3–17.9) vs 18.6 (13.2–25.2) ng/ml respectively, p<0.05] and controls [21.6 (17.8–33.6) ng/ml, p<0.001]. Multiple linear regression analysis identified an association between AHI (β=-0.758, p=0.041) and FEV1 (β=0.698, p=0.038) with serum Vit D levels, in the OVS group. Conclusions: Patients with OVS exhibit significant lower Vit D levels; stronger predictors were AHI and FEV1. Given potential associations between Vit D deficiency and cardiovascular disease and COPD exacerbations, it may be useful its measurement and treatment; however more studies are needed.
Obstructive sleep apnea (OSA) and idiopathic pulmonary fibrosis (IPF) frequently coexist. Elevated levels of Krebs von den Lungen‑6 (KL‑6), endothelin‑1 (ET‑1) and S100 calcium‑binding protein A9 (S100A9) have been observed in patients with IPF, suggesting their potential role as biomarkers for lung fibrosis. The aim of the present study was to measure the levels of KL‑6, ET‑1 and S100A9 in patients with IPF‑OSA and to test the potential of these biomarkers as a characteristic OSA signature with diagnostic and prognostic potential for IPF. A total of 55 subjects with newly‑diagnosed IPF participated in the present cross‑sectional study. In addition to performing overnight attended polysomnography and pulmonary function tests, serum and bronchoalveolar lavage (BAL) levels of KL‑6, along with serum levels of ET‑1 and S100A9, were also assessed. A total of 15 patients with IPF and 40 patients with IPF‑OSA were included. Age, sex, comorbidities and pulmonary function tests did not differ between the groups. Although there was no significant difference between groups in the levels of KL‑6, ET‑1 and S100A9 (P>0.05), the serum ET‑1 levels tended to be elevated in patients with OSA‑IPF compared with patients with IPF (1.78 vs. 1.07 pg/ml; P=0.06). Additionally, a significant association was observed between serum KL‑6 levels and the severity of IPF, and also between BAL KL‑6 levels and nocturnal mean SaO2 levels, even after taking into account factors such as obesity and smoking. S100A9 levels were associated with the oxygen desaturation index, even after adjustments for obesity, smoking and the gender‑age‑physiology index, only in the IPF‑OSA group. Conclusively, the present findings suggested significant associations between serum ET‑1, S100A9 and BAL KL‑6 levels and specific OSA severity parameters in the IPF‑OSA group. This evidence suggested that these molecules could serve as biomarkers for the identification of patients with IPF‑OSA, offering a distinct OSA signature that has diagnostic and potential treatment value. Larger studies are crucial to substantiate the present findings and reinforce this hypothesis.
Introduction: To personalize OSA management, several studies aimed at better capturing disease heterogeneity by clustering methods. The objective of this study was to perform a pan European OSA clustering analysis in the European Sleep Apnea Database (ESADA) using the same variables. Material and Methods: The data from 30 centres contributing to the ESADA were used for this analysis. Subjects with an apnea-hypopnea index (AHI) or an oxygen desaturation index (ODI) ≥ 5 events per hour were included. Clusters of phenotypes were identified by using a latent class analysis (LCA). Results: From 18,686 patients included for the LCA, eight clusters were finally identified (Figure 1). Two clusters (2 (only females)) and 4 (only males)) were constituted by non-obese, young subjects without sleepiness or comorbidities. Three clusters (3, 7 and 8) were constituted by the oldest patients with a high burden of comorbidities and obesity. Finally, three clusters (1, 5 and 6) were constituted by obese patients free of comorbidities despite various levels of AHI. The comparison of clusters showed significant differences in the prevalence of comorbidities, ESS, medications and OSA treatment indications. Conclusion: Eight distinct clinical phenotypes were identified in a large pan European database. The impact of these clusters on treatment adherence or prognosis remains to be studied in the ESADA follow-up data.
'/%3e%3c/g%3e%3cuse xlink:href='%23d' x='45.714'/%3e%3cuse xlink:href='%23e' transform='matrix(-1 0 0 1 479.792 0)'/%3e%3cg id='f' fill='%23fff'%3e%3cpath fill='%23039' d='M232.636 202.406v.005c0 2.212.85 4.227 2.212 5.69 1.365 1.466 3.245 2.377 5.302 2.377 2.067 0 3.944-.905 5.303-2.365 1.358-1.459 2.202-3.472 2.202-5.693v-10.768l-14.992-.013-.028 10.766'/%3e%3ccircle cx='236.074' cy='195.735' r='1.486'/%3e%3ccircle cx='244.392' cy='195.742' r='1.486'/%3e%3ccircle cx='240.225' cy='199.735' r='1.486'/%3e%3ccircle cx='236.074' cy='203.916' r='1.486'/%3e%3ccircle cx='244.383' cy='203.905' r='1.486'/%3e%3c/g%3e%3cuse xlink:href='%23f' y='-26.016'/%3e%3cuse xlink:href='%23f' x='-20.799'/%3e%3cuse xlink:href='%23f' x='20.745'/%3e%3cuse xlink:href='%23f' y='25.784'/%3e%3c/svg%3e)
