Clinical Phenotypes and Comorbidity in European Sleep Apnoea Patients

Clinical Characteristics and Treatment Efficacy for Co-Morbid Insomnia and Sleep Apnea (COMISA): Evidence from Qualitative and Quantitative Analysis

OBJECTIVES

A multitude of physical and mental challenges are being faced in the population with Co-morbid Insomnia and Sleep Apnea (COMISA). Unfortunately, research about clinical characteristics and management of COMISA based on quantitative evidence is lacking.

METHOD

Standard procedures for literature retrieval, selection and quality assessment, data extraction, analysis, and interpretation were conducted step by step. For studying the sleep characteristics, common complications and widely recognized treatment options for COMISA, Weighted Mean Difference (WMD) and Odds Ratio (OR) were applied to assess the mean and risk differences between compared groups. Outcomes included sleep health parameters and secondary impairments in physical and mental well-being.

RESULTS

COMISA showed worse sleep quality than OSA only by PSQI (WMD = 3.38 point) and heavier sleep fragmentation (WMD = 11.46 min) than insomnia only. Besides, COMISA patients showed a higher risk for depression (OR [95%CI] = 5.03[2.31, 10.93]) and PTSD (OR [95%CI] = 3.96[1.85, 8.46]) in comparison with OSA alone. Compared to insomnia alone, COMISA patients suffered from more than two times higher risk of cardiovascular diseases, hypertension, and diabetes. In treating COMISA patients, combining CBTI with PAP treatment can enhance the improvement of insomnia severity (ISI, WMD [95%CI] =-3.26[-4.51, -2.00] point) and sleep efficiency (WMD [95%CI] = 6.39[1.97, 10.81] %) compared to PAP alone.

CONCLUSIONS

Impaired sleep domains in COMISA cover sleep quality and sleep structure. Also, COMISA has a higher risk for cardiometabolic diseases and mental disorders. Combining CBTI with PAP can be a recommended treatment to relieve sleep impairments for COMISA.

Sex differences in sleep and sleep-disordered breathing

PURPOSE OF REVIEW

There is increasing evidence for relevant sex differences in pathophysiology, symptom presentation and outcomes in obstructive sleep apnoea (OSA). However, research on sex differences and sex-specific phenotypes in sleep-disordered breathing (SDB) is still in its infancy and data on sex differences in other SDB is still very scarce.

RECENT FINDINGS

While OSA is more common in men than in premenopausal women, the prevalence of OSA doubles postmenopausally and becomes comparable to that of men. Women have a lower collapsibility of the upper airway and a lower arousal threshold. In addition, the rapid eye movement (REM)-apnoea-hypopnoea index (AHI) is typically higher in women than in men, but the non-REM-AHI and thus the total AHI is often lower. Women are often symptomatic at lower AHI and present more frequently with symptoms of sleep fragmentation and poor sleep quality. Both certain forms of OSA (e.g. REM-OSA) and certain phenotypes (e.g. COMISA) are more common in women. Men have a higher risk of high loop gain central sleep apnoea.

SUMMARY

For a better understanding of sex-typical phenotypes with the aim of a more targeted treatment approach of SDB, adequately powered studies on sex differences in SDB should be conducted.

A new perspective on OSAS cases with the Baveno classification

OBJECTIVE/BACKGROUND

Since the apnea-hypopnea index (AHI), which is used in the diagnosis and grading of OSAS, does not adequately reflect the clinical perspective of the disease, the Baveno classification of OSA was developed, which allows multicomponent evaluation of OSAS patients. The aim of our study was to evaluate the application of the Baveno classification in clinical practice.

PATIENTS/METHODS

A prospective study was performed on patients diagnosed with OSAS between January 2021 and June 2022. Patients were divided into 4 groups according to Baveno classification (Groups A-D) and three groups as mild, moderate, and severe OSAS according to AHI.

RESULTS

A total of 378 patients (70% male, mean age 48.68 ± 11.81 years) were included in the study. The patients had mild (n: 75; 20%), moderate (n: 88; 23%), and severe (n: 215; 57%) OSAS. According to Baveno classification, patients were included in Groups A (n: 90; 24%), B (n: 105 (28%), C (n: 65; 17%), and D (n: 118; 31%). The mean AHIs of the Baveno groups were similar (p = 0.116). Oxygen desaturation index (ODI) was higher in Groups B and D compared to Group A. The duration of T90 desaturation was longer in Groups C and D compared to Groups A and B (p < 0.05).

CONCLUSIONS

The Baveno classification divided our OSAS cases into equivalent groups. One out of every four patients with mild OSAS was in Group D. This data was noteworthy in that the Baveno classification allows for the identification of symptomatic and comorbid patients with mild OSAS according to AHI and for the application of more effective treatments to these patients. Patients with comorbidities experienced oxygen desaturation for a longer period of time at night, and oxygenation deteriorated in patients with prominent symptoms. Baveno classification was found to be a more reasonable and easily applicable approach in clinical practice.

Association of comorbid obstructive sleep apnea and insomnia with risk of major adverse cardiovascular events in sleep medicine center patients

OBJECTIVES

To investigate the association between comorbid obstructive sleep apnea and insomnia and major adverse cardiovascular events, including myocardial infarction, unstable angina, congestive heart failure, and stroke, in adults with suspected sleep disorders who underwent sleep apnea testing.

METHODS

We conducted a retrospective analysis of electronic medical records data from patients with clinical encounters at sleep medicine centers to identify patients with comorbid obstructive sleep apnea and insomnia, obstructive sleep apnea only, insomnia only, and patients without a diagnosis of obstructive sleep apnea or insomnia (i.e., controls). Obstructive sleep apnea, insomnia, comorbidities, and new-onset major adverse cardiovascular events were ascertained by ICD-9-CM and ICD-10-CM codes. Multivariable adjusted Cox proportional regression models evaluated the risk of major adverse cardiovascular events over a 10-year follow-up period.

RESULTS

A total of 3951 patients, 226 controls, 2107 with obstructive sleep apnea only, 276 with insomnia only, and 1342 with comorbid obstructive sleep apnea and insomnia, were included in the analysis. Compared to controls, comorbid obstructive sleep apnea and insomnia were associated with a significantly higher risk of developing major adverse cardiovascular events (hazard ratio 3.60, 95 CI%: 2.33-5.91) in unadjusted analyses. The relationship between comorbid obstructive sleep apnea and insomnia and major adverse cardiovascular events remained after adjustment for demographic and behavioral factors, but not after further adjustment for comorbidities. The greatest risk of major adverse cardiovascular events was found among younger adults with comorbid obstructive sleep apnea and insomnia. Obstructive sleep apnea only was associated with greater risk of major adverse cardiovascular events in unadjusted analyses only (hazard ratio 2.77, 95% CI: 1.80-4.54). Insomnia only was not significantly associated with increased risk of major adverse cardiovascular events.

CONCLUSIONS

Comorbid obstructive sleep apnea and insomnia may be a high-risk group for major adverse cardiovascular events, particularly younger adults. Further research is needed to better understand the association between comorbid obstructive sleep apnea and insomnia and major adverse cardiovascular events risk.

Identification of quality-of-life clusters by the Quebec sleep questionnaire in sleep apnea patients

Patients with obstructive sleep apnea (OSA) may present different symptoms. The clinical importance of symptom clustering is supported by the difference in the incidence of cardiovascular diseases between hypersomnolent and non-hypersomnolent sleep apnea patients. The objective of this study was to determine if quality-of-life clusters could be identified from the Quebec Sleep Questionnaire (QSQ) in OSA patients. Latent class analysis was used to identify clusters in a multivariate analysis of dichotomic variables (presence or absence of symptoms) for each item the QSQ obtained from 147 patients who fulfilled the questionnaire during its validation and subsequent trials (75.5% males, age: 53 ± 11 years, body mass index (BMI): 30.4 ± 4.7 kg/m2, apnea/hypopnea index (AHI): 31.3 ± 14.8/h). Three clusters were identified. Quality of life was preserved in patients of cluster 1 (20.4% of patients). Patients of cluster 2 (32.6% of patients) had a moderately impaired quality of life, mainly due to daytime somnolence and poor sleep quality. Patients with impaired quality of life (cluster 3, 46.9% of patients) had an important impact in every domain of the QSQ with the highest sleepiness and daytime symptom impairments. Gender, BMI, and AHI did not differ between the three clusters. In conclusion, different quality-of-life clusters can be identified from the QSQ in sleep apnea patients. These clusters are similar to those reported previously. Further studies are needed to validate these clusters in larger and independent cohorts, to evaluate how they respond to OSA treatment, and their relationship with incident outcomes.

Differences in Polysomnographic and Craniofacial Characteristics of Catathrenia Phenotypes: A Cluster Analysis

Purpose

Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy.

Patients and Methods

Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed.

Results

Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, p=0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm3, p<0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, p=0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], p=0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm2, p<0.001) and better response to treatment (41.2% and 82.6%, p=0.004).

Conclusion

Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

Objective

To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.

Methods

We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.

Results

Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.

Conclusion

Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

Evaluation of daytime sleepiness and insomnia symptoms in OSA patients with a characterization of symptom-defined phenotypes and their involvement in depression comorbidity-a cross-sectional clinical study

Introduction

Recent research highlights the significance of insomnia and sleepiness, shifting from obstructive sleep apnea (OSA) severity and sleep structure, in defining OSA phenotypes.

Objectives

This study aimed to characterize insomnia and sleepiness associated with OSA phenotypes and assess their involvement in depression symptoms (DS) in OSA.

Materials and methods

This cross-sectional, clinical study included 181 participants who underwent polysomnography (PSG) and filled out questionnaires, including the Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), and Beck Depression Index (BDI). They were categorized into phenotypes: insomnia-sleepiness (I + S; ESS ≥ 11; ISI ≥ 15; n = 20), sleepiness (S; ESS ≥ 11; ISI < 15; n = 22), insomnia (I; ESS < 11; ISI ≥ 15), and asymptomatic (A; ESS < 11; ISI<15; n=55).

Results

A linear regression model for the BDI score (R2 = 0.357, p < 0.001) included ISI score and subjective-to-objective sleep latency ratio. The ISI score was a predictive factor for mild and moderate DS [OR = 1.23 (95% CI: 1.09-1.38), p < 0.001 and OR = 1.39 (95% CI: 1.13-1.72), p = 0.002]. The I and I + S phenotypes are characterized by higher BDI scores (p < 0.001 and p = 0.02), longer subjective sleep latency (p = 0.008 and p = 0.04), and shorter subjective total sleep time (TST; p = 0.049 and p = 0.006) compared to A. Furthermore, the I and I + S groups had shorter subjective TST than S (p = 0.03 and p = 0.047). The I and I + S had higher BDI scores than A (p < 0.001 and p = 0.02, respectively) and S (p < 0.001 and p = 0.02, respectively). The I phenotype was associated with the risk of mild and moderate DS (OR = 5.61 (95% CI: 1.91-16.53), p < 0.001 and OR = 9.55 (95% CI: 1.81-50.48), p = 0.008 respectively). Moreover, the I + S phenotype presented an even greater risk for mild DS (OR = 10.29 (95% CI: 2.95-35.85), p < 0.001).

Conclusion

Using clinical features for OSA phenotyping holds promise for finding OSA individuals with increased risk for DS occurrence.

Sex and Sleep Disruption as Contributing Factors in Alzheimer's Disease

Alzheimer's disease (AD) affects more women than men, with women throughout the menopausal transition potentially being the most under researched and at-risk group. Sleep disruptions, which are an established risk factor for AD, increase in prevalence with normal aging and are exacerbated in women during menopause. Sex differences showing more disrupted sleep patterns and increased AD pathology in women and female animal models have been established in literature, with much emphasis placed on loss of circulating gonadal hormones with age. Interestingly, increases in gonadotropins such as follicle stimulating hormone are emerging to be a major contributor to AD pathogenesis and may also play a role in sleep disruption, perhaps in combination with other lesser studied hormones. Several sleep influencing regions of the brain appear to be affected early in AD progression and some may exhibit sexual dimorphisms that may contribute to increased sleep disruptions in women with age. Additionally, some of the most common sleep disorders, as well as multiple health conditions that impair sleep quality, are more prevalent and more severe in women. These conditions are often comorbid with AD and have bi-directional relationships that contribute synergistically to cognitive decline and neuropathology. The association during aging of increased sleep disruption and sleep disorders, dramatic hormonal changes during and after menopause, and increased AD pathology may be interacting and contributing factors that lead to the increased number of women living with AD.

CPAP adherence after ambulatory and in-hospital management in patients aged 65 years and older

OBJECTIVES

The aims of this study are to evaluate the early adherence to CPAP treatment in patients aged 65 years and older and to compare ambulatory (ACPAP) and in-hospital (HCPAP) management in starting CPAP treatment.

METHODS

Adherence to CPAP therapy at 3 months was retrospectively studied in patients on whom CPAP therapy was initiated between 1 January 2020 and 31 December 2021. Patients in the ACPAP group were selected based on the current Belgian reimbursement criteria (OAHI ≥30/h and few comorbidities).

RESULTS

146 patients were studied (median OAHI 43.35/h [32.02; 57.40]; median age 69 [67.0; 73.0]): 116 (79.5%) patients in the HCPAP and 30 (20.5%) in the ACPAP group. Based on an adherence threshold of average CPAP use of ≥ 4 hours per day, 120 (82%) patients were adherent to the treatment; 94 (81%) patients in the HCPAP and 26 (86.7%) in the ACPAP group. The median CPAP use for the total population was 6.4 h/day [4.89; 7.34], reaching 6.3 h/d [4.79; 7.15] for the HCPAP group and 6.8 h/d [6.21; 8.06] for the ACPAP group (p = 0.019). Insomnia was a significant risk factor for non-adherence (OR 5.16 [1.64; 16.08], p = 0.0043) but the ACPAP method was not (OR 0.66 [0.18; 1.91], p = 0.4748).

CONCLUSION

Early CPAP adherence in patients ≥ 65 years old was good in terms of average use per day and proportion of adherent patients. ACPAP method was not a risk factor for lower CPAP adherence in patients presenting severe OSAS (OAHI ≥30/h) and few comorbidities.

Update on the genetic basis of obstructive sleep apnoea - hype or hope?

PURPOSE OF REVIEW

The obstructive sleep apnoea syndrome (OSAS) is a chronic, common condition in western societies which can lead to adverse cardiometabolic effects if left untreated and is one of the commonest causes of excessive daytime somnolence.

RECENT FINDINGS

The presentation of OSAS is diverse and is thought to comprise of different intermediate phenotypes and endotypes in varying proportions in each individual. Unfortunately, due to its heterogeneity and the changing definitions of the disorder by workers in the field, attempts at revealing the genetic basis of OSAS has been fraught with difficulty.

SUMMARY

This brief review presents a short update on the achievements of the past three decades in this understudied and underfunded area of endeavour in respiratory sleep medicine. The genetic underpinnings of OSAS remain elusive.

A nationwide study on the prevalence and contributing factors of obstructive sleep apnea in Iran

Reliable obstructive sleep apnea (OSA) prevalence information in Iran is lacking due to inconsistent local study results. To estimate OSA prevalence and identify clinical phenotypes, we conducted a nationally representative study using multi-stage random cluster sampling. We recruited 3198 individuals and extrapolated the results to the entire Iranian population using complex sample survey analyses. We identified 3 clinical phenotypes as "sleepy," "insomnia," and "restless legs syndrome (RLS)." The prevalence of OSA was 28.7% (95%CI: 26.8-30.6). The prevalence of "sleepy," "insomnia," and "RLS" phenotypes were 82.3%, 77.8%, and 36.5% in women, and 64.8%, 67.5%, and 17.9% in men, respectively. "Sleepy" and "insomnia" phenotypes overlapped the most. Age (OR: 1.9), male sex (OR: 3.8), BMI (OR: 1.13), neck circumference (OR: 1.3), RLS (OR: 2.0), and insomnia (OR: 2.3) were significant OSA predictors (p-values: 0.001). In men, "sleepy" phenotype was associated with youth and unmarried status but not in women. The "insomnia" phenotype was associated with shorter sleep duration in women; cardiovascular diseases (CVD), urban residency, and shorter sleep duration in men. "RLS" phenotype was associated with shorter sleep duration and CVD in women and older age, lower educational level, CVD, and hypertension in men. The findings point to the need for funding of OSA screening in Iran, for a different assessment of men and women, and for future sleep research to consider overlapping phenotypes.

Translation of obstructive sleep apnea pathophysiology and phenotypes to personalized treatment: a narrative review

Obstructive Sleep Apnea (OSA) arises due to periodic blockage of the upper airway (UA) during sleep, as negative pressure generated during inspiration overcomes the force exerted by the UA dilator muscles to maintain patency. This imbalance is primarily seen in individuals with a narrowed UA, attributable to factors such as inherent craniofacial anatomy, neck fat accumulation, and rostral fluid shifts in the supine posture. Sleep-induced attenuation of UA dilating muscle responsiveness, respiratory instability, and high loop gain further exacerbate UA obstruction. The widespread comorbidity profile of OSA, encompassing cardiovascular, metabolic, and neuropsychiatric domains, suggests complex bidirectional relationships with conditions like heart failure, stroke, and metabolic syndrome. Recent advances have delineated distinct OSA phenotypes beyond mere obstruction frequency, showing links with specific symptomatic manifestations. It is vital to bridge the gap between measurable patient characteristics, phenotypes, and underlying pathophysiological traits to enhance our understanding of OSA and its interplay with related outcomes. This knowledge could stimulate the development of tailored therapies targeting specific phenotypic and pathophysiological endotypes. This review aims to elucidate the multifaceted pathophysiology of OSA, focusing on the relationships between UA anatomy, functional traits, clinical manifestations, and comorbidities. The ultimate objective is to pave the way for a more personalized treatment paradigm in OSA, offering alternatives to continuous positive airway pressure therapy for selected patients and thereby optimizing treatment efficacy and adherence. There is an urgent need for personalized treatment strategies in the ever-evolving field of sleep medicine, as we progress from a 'one-size-fits-all' to a 'tailored-therapy' approach.

Is insomnia severity a moderator of the associations between obstructive sleep apnea severity with mood and diabetes-related distress?

PURPOSE

This study examined insomnia severity as a moderator of the associations between obstructive sleep apnea (OSA) severity with impaired mood and diabetes-related distress in adults with OSA and type 2 diabetes (T2D).

METHODS

This secondary analysis used pooled baseline data from two randomized controlled trials that evaluated the efficacy of treatment of OSA or insomnia in adults with T2D. Participants for this analysis had OSA (Apnea Hypopnea Index [AHI] ≥ five events/hour obtained from an in-home sleep apnea testing device) and completed questionnaires on insomnia, mood, and diabetes-related distress. Hierarchical multiple linear regression and multivariate linear regression analyses were used controlling for demographic characteristics and restless leg syndrome.

RESULTS

Of 240 participants, mean age was 57.8 ± 10.17, 50% were female, and 35% were non-White. Participants had poorly controlled diabetes (Mean HbA1C = 7.93 ± 1.62), and moderate OSA (Mean AHI = 19.3 ± 16.2). Insomnia severity significantly moderated the association between OSA severity and mood (b = -0.048, p = .017). Although insomnia severity did not moderate the relationship between OSA severity and diabetes-related distress (b = -0.009, p = .458), insomnia severity was independently associated with greater diabetes-related distress (b = 1.133, p < .001).

CONCLUSIONS

In adults with T2D and OSA, as insomnia severity increased, increasing OSA severity was associated with lower level of mood disturbances. Insomnia independently increased the level of diabetes-related distress. These findings suggest that comorbid insomnia may be more impactful than OSA on increasing mood disturbances and diabetes-related distress in adults with T2D.

Relationship between nonrestorative sleep with short sleep duration and diabetes mellitus comorbid among a Japanese occupational population

Objective

Nonrestorative sleep (NRS) is one of the symptoms of insomnia and is clearly more associated with objective indices, such as sleep stability, sleep fragmentation, and inflammatory responses, than other insomnia symptoms. However, the link between NRS and diabetes mellitus is poorly understood. Therefore, the prevalence of NRS in workers and the relationships between NRS and diabetes were investigated.

Methods

The results of a single year's medical examinations were investigated for 26,144 Japanese active office workers who were 30 to 59 years old. NRS was investigated using a personal computer in a medical interview. Furthermore, the relationships between NRS and diabetes comorbidity in addition to sleep duration were also analyzed.

Results

The mean age of the subjects was 47.9 ± 7.3 years old, and the proportion of subjects with NRS was 26%. The presence of NRS together with a sleep duration ≤ 5 h or a sleep duration of 6 h was an independent comorbid factor for diabetes compared with the absence of NRS together with a sleep duration of 7 h (odds ratio [OR] 1.34; 95% confidence interval [CI] 1.14-1.58; P < 0.001; and OR 1.25; 95% CI 1.04-1.48; P = 0.015).

Conclusion

NRS in active workers may contribute to the development of diabetes even if the workers have the same short sleep durations as other workers without NRS.

Hypertension treatment in patients with sleep apnea from the European Sleep Apnea Database (ESADA) cohort - towards precision medicine

We recruited 5,970 hypertensive patients with obstructive sleep apnea (OSA) on current antihypertensive treatment from the European Sleep Apnea Database (ESADA) cohort. The group was subdivided into those receiving monotherapy (n = 3,594) and those receiving dual combined therapy (n = 2,376). We studied how major OSA confounders like age, gender, and body mass index as well as the degree of sleep apnea modified office systolic and diastolic blood pressure. Beta-blockers alone or in combination with a diuretic were compared with other antihypertensive drug classes. Monotherapy with beta-blocker was associated with lower systolic blood pressure, particularly in non-obese middle-aged males with hypertension. Conversely, the combination of a beta-blocker and a diuretic was associated with lower systolic and diastolic blood pressure in hypertensive patients with moderate-severe OSA. Systolic blood pressure was better controlled in female patients using this combined treatment. Our cross-sectional data suggest that specific clinical characteristics and type of antihypertensive medication influence the degree of blood pressure control in hypertensive OSA patients. Controlled trials are warranted.

Distinct phenotypic clusters of sleep‐disordered breathing and their association with medical care‐seeking behaviour and sleep habits: the Guangdong Sleep Health Study

Sleep-disordered breathing (SDB) is highly prevalent, but its rates of diagnosis and treatment are low. As a heterogeneous condition, it is associated with delayed diagnosis and/or suboptimal treatment. We aimed to determine distinct SDB clusters and examine their association with medical care-seeking behaviour and sleep habits in a community-dwelling population in South China. Participants were enrolled in the 'Guangdong Sleep Health Study'. Five distinct community sites were investigated. Participant information was collected, and overnight sleep monitoring was performed. Latent class analysis was performed to classify patients with SDB based on patterns of SDB-related symptoms and signs. Overall, 1,524 patients with SDB were evaluated. Four distinct clusters were identified: 'minimally symptomatic' (Cluster 2), which was the most dominant subtype (41.6%), followed by 'pure insomnia/fewer daytime symptoms' (Cluster 4; 24.7%), 'insomnia/multiple daytime symptoms' (Cluster 3; 17.8%), and 'upper airway symptoms with sleepiness' (Cluster 1; 15.9%). The overall medical care-seeking rate attributable to sleep and breathing disorders was only 3.3%: 10.3% in Cluster 3, 2.5% in Cluster 1, 2.1% in Cluster 4, and 1.3% in Cluster 2, in which Cluster 3 was the highest and Cluster 2 was the lowest (adjusted p < 0.05). Regarding the sleep habit of going to bed and waking up at a consistent time, Cluster 3 exhibited the worst performance and Cluster 2 the best. In conclusion, distinct phenotypic subtypes were identified in community-dwelling patients with SDB. Tailored strategies to encourage medical care-seeking, early identification, and optimisation of treatment are necessary considering the different subtypes.

Cancer sleep symptom-related phenotypic clustering differs across three cancer specific patient cohorts

Specific sleep disorders have been linked to disease progression in different cancers. We hypothesised sleep symptom clusters would differ between cancer types. The aim of this study was to compare sleep symptom clusters in post-treatment melanoma, breast and endometrial cancer patients. Data were collected from 124 breast cancer patients (1 male, 60 ± 15 years, 28.1 ± 6.6 kg/m2 ), 82 endometrial cancer patients (64.0 ± 12.5 years, 33.5 ± 10.4 kg/m2 ) and 112 melanoma patients (59 male, 65.0 ± 18.0 years, 29.1 ± 6.6 kg/m2 ). All patients completed validated questionnaires to assess sleep symptoms, including the Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Functional Outcomes of Sleep Questionnaire-10 (FOSQ-10). Snoring, tiredness, observed apneas, age, BMI, and gender data were also collected. Binary values (PSQI, ISI, FOSQ), or continuous variables for sleepiness (ESS) and perceived sleep quality (PSQI), were created and sleep symptom clusters were identified and compared across cancer cohorts. Four distinct sleep symptom clusters were identified: minimally symptomatic (n = 152, 47.7%); insomnia-predominant (n = 87, 24.9%); very sleepy with upper airway symptoms (n = 51, 16.3%), and severely symptomatic with severe dysfunction (n = 34, 11.1%). Breast cancer patients were significantly more likely to be in the insomnia predominant or severely symptomatic with severe dysfunction clusters, whereas melanoma patients were more likely to be minimally symptomatic or sleepy with upper airway symptoms (p <0.0001). Endometrial cancer patients were equally distributed across symptom clusters. Sleep symptom clusters vary across cancer patients. A more personalised approach to the management of sleep-related symptoms in these patients may improve the long term quality of life and survival.

Epidemiology, Physiology and Clinical Approach to Sleepiness at the Wheel in OSA Patients: A Narrative Review

Sleepiness at the wheel (SW) is recognized as an important factor contributing to road traffic accidents, since up to 30 percent of fatal accidents have been attributed to SW. Sleepiness-related motor vehicle accidents may occur both from falling asleep while driving and from behavior impairment attributable to sleepiness. SW can be caused by various sleep disorders but also by behavioral factors such as sleep deprivation, shift work and non-restorative sleep, as well as chronic disease or the treatment with drugs that negatively affect the level of vigilance. An association between obstructive sleep apnea (OSA) and motor vehicle accidents has been found, with an increasing risk in OSA patients up to sevenfold in comparison to the general population. Regular treatment with continuous positive airway pressure (CPAP) relieves excessive daytime sleepiness and reduces the crash risk. Open questions still remain about the physiological and clinical determinants of SW in OSA patients: the severity of OSA in terms of the frequency of respiratory events (apnea hypopnea index, AHI) or hypoxic load, the severity of daytime sleepiness, concomitant chronic sleep deprivation, comorbidities, the presence of depressive symptoms or chronic fatigue. Herein, we provide a review addressing the epidemiological, physiological and clinical aspects of SW, with a particular focus on the methods to recognize those patients at risk of SW.

Sleep Deficiency in Obstructive Sleep Apnea

Sleep deficiency in patients with obstructive sleep apnea includes abnormal quality, timing, and duration of sleep, and the presence of other comorbid conditions. These include insomnia, circadian misalignment disorders, and periodic limb movements of sleep. The co-occurrence of these conditions with obstructive sleep apnea likely plays a role in the pathogenesis, clinical presentation, and management of obstructive sleep apnea. Considering these conditions and their treatment in evaluating sleep deficiency in obstructive sleep apnea may help to improve patient outcomes. However, future research is needed to understand the intersection between obstructive sleep apnea and these disorders.

Obstructive sleep apnea: transition from pathophysiology to an integrative disease model

Obstructive sleep apnea (OSA) is characterised by recurring episodes of upper airway obstruction during sleep and the fundamental abnormality reflects the inability of the upper airway dilating muscles to withstand the negative forces generated within the upper airway during inspiration. Factors that result in narrowing of the oropharynx such as abnormal craniofacial anatomy, soft tissue accumulation in the neck, and rostral fluid shift in the recumbent position increase the collapsing forces within the airway. The counteracting forces of upper airway dilating muscles, especially the genioglossus, are negatively influenced by sleep onset, inadequacy of the genioglossus responsiveness, ventilatory instability, especially post arousal, and loop gain. OSA is frequently associated with comorbidities that include metabolic, cardiovascular, renal, pulmonary, and neuropsychiatric, and there is growing evidence of bidirectional relationships between OSA and comorbidity, especially for heart failure, metabolic syndrome, and stroke. A detailed understanding of the complex pathophysiology of OSA encourages the development of therapies targeted at pathophysiological endotypes and facilitates a move towards precision medicine as a potential alternative to continuous positive airway pressure therapy in selected patients.

Multimorbidity and overall comorbidity of sleep apnoea: a Finnish nationwide study

The prevalence of sleep apnoea is increasing globally; however, population-based studies have reported a wide variation of prevalence estimates, and data on incidence of clinically diagnosed sleep apnoea are scant. Data on the overall burden of comorbidities or multimorbidity in individuals with incident sleep apnoea are scarce, and the pathways to multimorbidity have only marginally been studied. To study the current epidemiology of sleep apnoea in Finland, overall burden of comorbidities, and multimorbidity profiles in individuals with incident sleep apnoea, we conducted a register-based, nationwide, retrospective study of data from January 2016 to December 2019. The prevalence of clinically diagnosed sleep apnoea was 3.7% in the Finnish adult population; 1-year incidence was 0.6%. Multimorbidity was present in 63% of individuals at the time of sleep apnoea diagnosis. Of those with incident sleep apnoea, 34% were heavily multimorbid (presenting with four or more comorbidities). The three most common chronic morbidities before sleep apnoea diagnosis were musculoskeletal disease, hypertension and cardiovascular disease. In multimorbid sleep apnoea patients, hypertension and metabolic diseases including obesity and diabetes, cardiovascular diseases, musculoskeletal diseases and dorsopathies, in different combinations, encompassed the most frequent disease pairs preceding a sleep apnoea diagnosis. Our study adds to the few population-based studies by introducing overall and detailed figures on the burden of comorbidities in sleep apnoea in a nationwide sample and provides up-to-date information on the occurrence of sleep apnoea as well as novel insights into multimorbidity in individuals with incident sleep apnoea.

Two-thirds of individuals with sleep apnoea are multimorbid at diagnosis and one-third are heavily multimorbid. Dorsopathies, musculoskeletal diseases, hypertension and metabolic diseases are the most common comorbidities preceding sleep apnoea diagnosis.https://bit.ly/36WMlX1

Defining obstructive sleep apnoea syndrome: a failure of semantic rules

Obstructive sleep apnoea syndrome (OSAS) is one of the most ubiquitous medical conditions in industrialised society. Since the recognition that symptoms of excessive daytime somnolence, problems with concentration, mood and cognitive impairment, as well as cardiometabolic abnormalities can arise as a consequence of obstructed breathing during sleep, it has been subject to variation in its definition. Over the past five decades, attempts have been made to standardise the definitions and scoring criteria used for apnoeas and hypopnoea, which are the hallmarks of obstructive sleep apnoea (OSA). However, applying these definitions in clinical and research practice has resulted in over- and under-estimation of the severity and prevalence of OSAS. Furthermore, the definitions may eventually become redundant in the context of rapid technological advances in breathing measurement and other signal acquisition. Increased efforts towards precision medicine have led to a focus on the pathophysiology of obstructed breathing during sleep. However, the same degree of effort has not been focused on how and why the latter does or does not result in diurnal symptoms, integral to the definition of OSAS. This review focuses on OSAS in adults and discusses some of the difficulties with current definitions and the possible reasons behind them.

The definition of obstructive sleep apnoea syndrome appears to be in constant flux dependent on the definitions attributed to its diagnostic componentshttps://bit.ly/3zXrWKg

Novel avenues to approach non-CPAP therapy and implement comprehensive OSA care

Recent advances in obstructive sleep apnoea (OSA) pathophysiology and translational research have opened new lines of investigation for OSA treatment and management. Key goals of such investigations are to provide efficacious, alternative treatment and management pathways that are better tailored to individual risk profiles to move beyond the traditional, continuous positive airway pressure (CPAP)-focused, "one size fits all", trial and error approach which is too frequently inadequate for many patients. Identification of different clinical manifestations of OSA (clinical phenotypes) and underlying pathophysiological phenotypes (endotypes), that contribute to OSA have provided novel insights into underlying mechanisms and have underpinned these efforts. Indeed, this new knowledge has provided the framework for precision medicine for OSA to improve treatment success rates with existing non-CPAP therapies such as mandibular advancement devices and upper airway surgery, and newly developed therapies such as hypoglossal nerve stimulation and emerging therapies such as pharmacotherapies and combination therapy. These concepts have also provided insight into potential physiological barriers to CPAP adherence for certain patients. This review summarises the recent advances in OSA pathogenesis, non-CPAP treatment, clinical management approaches and highlights knowledge gaps for future research. OSA endotyping and clinical phenotyping, risk stratification and personalised treatment allocation approaches are rapidly evolving and will further benefit from the support of recent advances in e-health and artificial intelligence.

Characteristics of Patients with Obstructive Sleep Apnea at High Risk for Cardiovascular Disease

Background and Objectives: To evaluate the influence of obstructive sleep apnea (OSA)-related symptoms on prevalent cardiovascular disease (CVD) in a large clinical population of patients. Materials and Methods: A total of 2127 patients (mean age 55 years, 24% women) underwent diagnostic polysomnography and were evaluated using the Epworth sleepiness scale (ESS), the Athens Insomnia Scale (AIS), and the Beck Depression Inventory (BDI). We investigated the predictive value of OSA-associated symptoms for prevalent cardiovascular disease, after adjustment for relevant confounding factors including age, obesity, and co-morbidities. Results: Patients with OSA and CVD were older and had a higher Body Mass Index (BMI); the percentage of obese patients was also higher (83% vs. 70%, p < 0001). They also had greater neck, waist, and hip circumferences and a higher waist-to-hip ratio. Excessive daytime sleepiness (ESS ≥ 10) [odds ratio (95% CI) 1.112 (0.708–1.748), p = 0.64], insomnia symptoms (AIS ≥ 6) [odds ratio (95% CI) 0.748 (0.473–1.184), p = 0.21], frequent awakenings [odds ratio (95% CI) 1.599 (1.019–2.508), p = 0.06], and nocturia [odds ratio (95% CI) 1.359 (0.919–2.009), p = 0.124] were not associated with CVD after adjustment for the previous confounders. On the other hand, depressive symptoms (BDI ≥ 10) independently predicted prevalent CVD [odds ratio (95% CI) 1.476 (1.154–1.887), p = 0.002]. Further analysis in subgroups stratified by age, BMI, and gender demonstrated that depressive symptoms predicted prevalent CVD but only in the subgroup of younger (age group < 60 years), obese (BMI group ≥ 30), and male (OR = 1.959, 95% CI = 1.209–3.175, p = 0.006) OSA patients. Conclusions: OSA patients with CVD were more likely to complain of less typical OSA symptoms and depressive symptoms compared to patients without CVD in this large clinical patient cohort, supportingthecomplexity and heterogeneityof OSA.

The negative health effects of having a combination of snoring and insomnia

STUDY OBJECTIVES

Insomnia and snoring are common sleep disorders. The aim was to investigate the association of having a combination of insomnia symptoms and snoring with comorbidity and daytime sleepiness.

METHODS

The study population comprised 25,901 participants (16-75 years, 54.4% women) from four Swedish cities, who answered a postal questionnaire that contained questions on snoring, insomnia symptoms (difficulties initiating and/or maintaining sleep and/or early morning awakening), smoking, educational level, and respiratory and non-respiratory disorders.

RESULTS

Snoring was reported by 4,221 (16.2%), while 9,872 (38.1%) reported ≥ 1 insomnia symptom. A total of 2,150 (8.3%) participants reported both insomnia symptoms and snoring. The association with hypertension (adj. OR 1.4, 95% CI: 1.2-1.6), chronic obstructive pulmonary disease (COPD) (adj. OR 1.8, 95% CI: 1.3-2.4), asthma (adj. OR 1.9; 95% CI: 1.6-2.3), daytime sleepiness (adj. OR 7.9, 95% CI 7.1-8.8) and the use of hypnotics (adj. OR 7.5, 95% CI: 6.1-9.1) was highest for the group with both insomnia symptoms and snoring.

CONCLUSIONS

Participants with both snoring and insomnia run an increased risk of hypertension, COPD, asthma, daytime sleepiness and the use of hypnotics. It is important to consider snoring in patients seeking medical assistance for insomnia and, vice versa, in patients with snoring inquiring about insomnia.

OSA patients not treated with PAP - Evolution over 5 years according to the Baveno classification and cardiovascular outcomes

INTRODUCTION

The evolution of patients with obstructive sleep apnea (OSA) non-eligible for PAP-therapy at diagnosis is unknown. Currently, the severity of OSA is based on the apnea-hypopnea index (AHI), but its prognostic relevance has raised concerns. The Baveno classification may allow a better stratification of severity and therapeutic guidance in OSA.

METHODS

Patients with AHI≥5/h in 2015, classified into Baveno groups A and B and non-eligible for PAP therapy at diagnosis and over 5 years, were analyzed. Patients were reclassified into Baveno groups (A-D) and changes in groups over 5 years were explored. Patients in Baveno groups C and D, who developed major cardiovascular comorbidities (CVC) or end-organ damage (EOD group), were compared with patients in Baveno groups A and B (non-EOD group). To identify predictors of the development of major CVC or EOD, a logistic regression analysis was performed.

RESULTS

There were 76 patients, 58% male, mean age 51.9 ± 10.1 years, mean body mass index (BMI) of 30.3 ± 5.0 kg/m2 and median AHI of 8.9 (5.9-12.0) events/h. At diagnosis, 46% and 54% of patients were classified into Baveno group A and group B, respectively. In total, 21% of patients developed major CVC or EOD (Baveno group C or D); higher age (p = 0.011) and BMI (p = 0.004) and a higher percentage of central apneas (p = 0.012) at diagnosis significantly predicted it, while sex, sleepiness, insomnia, AHI, ODI and T90 were not.

CONCLUSIONS

A significant percentage of patients non-eligible for PAP-therapy at diagnosis of OSA developed CVC or EOD; higher age and BMI and a higher percentage of central apneas were significant predictors.

Comorbid Insomnia and Obstructive Sleep Apnea (COMISA): Current Concepts of Patient Management

Obstructive sleep apnea (OSA) and insomnia are the two most common sleep disorders among the general population, and they may often coexist in patients with sleep-disordered breathing (SDB). The higher prevalence of insomnia symptoms in patients with OSA (40–60%) compared to that observed in the general population has thus led researchers to identify a new disorder named comorbid insomnia and OSA (COMISA), whose true burden has been so far largely underestimated. The combined treatment of COMISA patients with positive-airway pressure ventilation (PAP) with cognitive behavioral therapy for insomnia (CBTi) has shown a better patient outcome compared to that obtained with a single treatment. Furthermore, recent evidence has shown that an innovative patient-centered approach taking into consideration patient characteristics, treatment preferences and accessibility to treatment is recommended to optimize clinical management of COMISA patients. However, in this complex mosaic, many other sleep disorders may overlap with COMISA, so there is an urgent need for further research to fully understand the impact of these therapies on outcomes for OSA patients with comorbidity. In light of this need, this review focuses on the major sleep disorders comorbid with OSA and the recent advances in the management of these insomniac patients.

Metrics of sleep apnea severity: beyond the apnea-hypopnea index

Obstructive sleep apnea (OSA) is thought to affect almost 1 billion people worldwide. OSA has well established cardiovascular and neurocognitive sequelae, although the optimal metric to assess its severity and/or potential response to therapy remains unclear. The apnea-hypopnea index (AHI) is well established; thus, we review its history and predictive value in various different clinical contexts. Although the AHI is often criticized for its limitations, it remains the best studied metric of OSA severity, albeit imperfect. We further review the potential value of alternative metrics including hypoxic burden, arousal intensity, odds ratio product, and cardiopulmonary coupling. We conclude with possible future directions to capture clinically meaningful OSA endophenotypes including the use of genetics, blood biomarkers, machine/deep learning and wearable technologies. Further research in OSA should be directed towards providing diagnostic and prognostic information to make the OSA diagnosis more accessible and to improving prognostic information regarding OSA consequences, in order to guide patient care and to help in the design of future clinical trials.

Future Treatment of Sleep Disorders: Syndromic Approach Versus Management of Treatable Traits?

Sleep disorders are categorized in line with traditional taxonomy. This conventional approach allows adequate management of many patients. Failure of treatment, however, may be due to nonspecificity of symptoms, coincidental association between symptoms and pathophysiological endotype, as well as co-occurrence of different pathologic mechanisms affecting sleep. Complex phenotypes often do not respond well to standard therapeutic interventions. In these cases, the clinical workup should aim at identifying treatable traits that will likely improve under targeted therapy. The challenge for sleep medicine is to further develop this innovative approach that is driven by the principles of systems medicine.

Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

BACKGROUND

The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.

OBJECTIVE

This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.

METHODS

A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).

RESULTS

A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).

CONCLUSIONS

Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Sleep Apnea and Insomnia: Emerging Evidence for Effective Clinical Management

Comorbid insomnia and sleep apnea (COMISA) are the most common co-occurring sleep disorders and present many challenges to clinicians. This review provides an overview of the clinical challenges in the management of patients with COMISA, with a focus on recent evidence regarding the evaluation and treatment of COMISA. Innovations in the assessment of COMISA have used profile analyses or dimensional approaches to examine symptom clusters or symptom severity that could be particularly useful in the assessment of COMISA. Recent randomized controlled trials have provided important evidence about the safety and effectiveness of a concomitant treatment approach to COMISA using cognitive-behavioral therapy for insomnia (CBT-I) with positive airway pressure (PAP). Furthermore, patient-centered considerations that integrate patient characteristics, treatment preferences, and accessibility to treatment in the context of COMISA are discussed as opportunities to improve patient care. Based on these recent advances and clinical perspectives, a model for using multidisciplinary, patient-centered care is recommended to optimize the clinical management of patients with COMISA.

Determinants of Sleepiness at Wheel and Missing Accidents in Patients With Obstructive Sleep Apnea

STUDY OBJECTIVES

Motor-vehicle crashes are frequent in untreated OSA patients but there is still uncertainty on prevalence as well as physiological or clinical determinants of sleepiness at the wheel (SW) in OSA patients. We assessed determinants of SW or sleepiness related near-miss car accident (NMA) in a group of non-professional drivers with OSA.

METHODS

A 237 consecutive, treatment-naïve PSG-diagnosed OSA patients (161 males, 53.1 ± 12.6 years) were enrolled. Self-reported SW was assessed by positive answer to the question, "Have you had episodes of falling asleep while driving or episodes of drowsiness at wheel that could interfere with your driving skill in the last year?" Occurrence of NMA in the last 3 years was also individually recorded. Habitual self-reported average sleep time was collected.

RESULTS

SW was found in 41.3% of patients but one-quarter of patients with SW did not report excessive daytime sleepiness. Predictors of SW were the following subjective factors: Epworth sleepiness scale score (ESS-OR 1.26; IC 1.1-1.4; p < 0.0001), depressive symptoms (BDI-OR 1.2; IC 1.06-1.18; p < 0.0001) and level of risk exposure (annual mileage-OR 1.9; IC 1.15-3.1; p = 0.007). NMAs were reported by 9.7% of patients, but more frequently by SW+ than SW- (22.4% vs. 0.7%; χ2 31, p < 0.0001). The occurrence of NMAs was significantly associated to ESS, BDI, habitual sleep duration and ODI (R 2 = 0.41).

CONCLUSION

SW is not predicted by severity of OSA. Evaluation of risk exposure, assessment of depressive symptoms, and reported NMA should be included in the clinical evaluation, particularly in patients with reduced habitual sleep time and severe nocturnal hypoxia.

Insomnia disorder: clinical and research challenges for the 21st century

BACKGROUND AND PURPOSE

Insomnia is a common and debilitating disorder that is frequently associated with important consequences for physical health and well-being.

METHODS

An international expert group considered the current state of knowledge based on the most relevant publications in the previous 5 years, discussed the current challenges in the field of insomnia and identified future priorities.

RESULTS

The association of trajectories of insomnia with subsequent quality of life, health and mortality should be investigated in large populations. Prospective health economics studies by separating the costs driven specifically by insomnia and costs attributable to its long-term effects are needed. Ignoring the heterogeneity of insomnia patients leads to inadequate diagnosis and inefficient treatment. Individualized interventions should be promoted. More data are needed on both the impact of sleep on overnight effects, such as emotion regulation, and the potential compensatory effort to counteract diurnal impairments. Another gap is the definition of neurocognitive deficits in insomnia patients compared to normal subjects after chronic sleep loss. There are also a number of key gaps related to insomnia treatment. Expert guidelines indicate cognitive-behavioural therapy for insomnia as first-line treatment. They neglect, however, the reality of major healthcare providers. The role of combined therapy, cognitive-behavioural therapy for insomnia plus pharmacological treatment, should be evaluated more extensively.

CONCLUSION

Whilst insomnia disorder might affect large proportions of the population, there are a number of significant gaps in the epidemiological/clinical/research studies carried out to date. In particular, the identification of different insomnia phenotypes could allow more cost-effective and efficient therapies.

Defining the Heterogeneity of Sleep Apnea Syndrome: A Cluster Analysis With Implications for Patient Management

INTRODUCTION

Obstructive sleep apnea (OSA) is a complex pathology with heterogeneity that has not been fully characterized to date. Our objective is to identify groups of patients with common clinical characteristics through cluster analysis that could predict patient prognosis, the impact of comorbidities and/or the response to a common treatment.

METHODS

Cluster analysis was performed using the hierarchical cluster method in 2025 patients in the apnea-HUGU cohort. The variables used for building the clusters included general data, comorbidity, sleep symptoms, anthropometric data, physical exam and sleep study results.

RESULTS

Four clusters were identified: (1) young male without comorbidity with moderate apnea and otorhinolaryngological malformations; (2) middle-aged male with very severe OSA with comorbidity without cardiovascular disease; (3) female with mood disorder; and (4) symptomatic male with established cardiovascular disease and severe OSA.

CONCLUSIONS

The characterization of these four clusters in OSA can be decisive when identifying groups of patients who share a special risk or common therapeutic strategies, orienting us toward personalized medicine and facilitating the design of future clinical trials.

Simple and Unbiased OSA Prescreening: Introduction of a New Morphologic OSA Prediction Score

PURPOSE

An early prescreening in suspected obstructive sleep apnea (OSA) patients is desirable to expedite diagnosis and treatment. However, the accuracy and applicability of current prescreening tools is insufficient. We developed and tested an unbiased scoring system based solely on objective variables, which focuses on the diagnosis of severe OSA and exclusion of OSA.

PATIENTS AND METHODS

The OSA prediction score was developed (n = 150) and validated (n = 50) within German sleep center patients that were recruited as part of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Six objective variables that were easy to assess and highly correlated with the apnea-hypopnea index were chosen for the score, including some known OSA risk factors: body-mass index, neck circumference, waist circumference, tongue position, male gender, and age (for women only). To test the predictive ability of the score and identify score thresholds, the receiver-operating characteristics (ROC) and curve were calculated.

RESULTS

A score ≥8 for predicting severe OSA resulted in an area under the ROC curve (ROC-AUC) of 90% (95% confidence interval: 84%, 95%), test accuracy of 82% (75%, 88%), sensitivity of 82% (65%, 93%), specificity of 82% (74%, 88%), and positive likelihood ratio of 4.55 (3.00, 6.90). A score ≤5 for predicting the absence of OSA resulted in a ROC-AUC of 89% (83%, 94%), test accuracy of 80% (73%, 86%), sensitivity of 72% (55%, 85%), specificity of 83% (75%, 89%), and positive likelihood ratio of 4.20 (2.66, 6.61). Performance characteristics were comparable in the small validation sample.

CONCLUSION

We introduced a novel prescreening tool combining easily obtainable objective measures with predictive power and high general applicability. The proposed tool successfully predicted severe OSA (important due to its high risk of cardiovascular disease) and the exclusion of OSA (rarely a feature of previous screening instruments, but important for better differential diagnosis and treatment).

The Complex Interaction Between the Major Sleep Symptoms, the Severity of Obstructive Sleep Apnea, and Sleep Quality

Introduction: Little information exists in the general population whether clinical presentation phenotypes of obstructive sleep apnea (OSA) differ in terms of sleep quality and comorbidities. Aim: The purpose of our study was to assess possible differences between symptomatic and asymptomatic OSA patients concerning syndrome's severity, patients' sleep quality, and comorbidities. Subjects and methods: First, in a nationwide, stratified, epidemiological survey, 4,118 Cypriot adult participants were interviewed about sleep habits and complaints. In the second stage of the survey, 264 randomly selected adults underwent a type III sleep study for possible OSA. Additionally, they completed the Greek version of Pittsburgh Sleep Quality Index (Gr-PSQI), Epworth Sleepiness Scale (ESS), Athens Insomnia Scale (AIS), and Hospital Anxiety and Depression Scale (HADS). Results: From 264 enrolled participants, 155 individuals (40 females and 115 males) were first diagnosed with OSA. Among these 155 patients, 34% had ESS ≥ 10 and 49% AIS ≥ 6. One or both symptoms present categorized the individual as symptomatic (60%) and neither major symptom as asymptomatic (40%). There were no significant statistical differences (SSDs) between the two groups (symptomatic-asymptomatic) with regard to anthropometrics [age or gender; neck, abdomen, and hip circumferences; and body mass index (BMI)]. The two groups had no differences in OSA severity-as expressed by apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and mean oxyhemoglobin saturation (SaO₂)-and in cardiometabolic comorbidities. Symptomatic patients expressed anxiety and depression more often than asymptomatics (p < 0.001) and had poorer subjective sleep quality (Gr-PSQI, p < 0.001). According to PSQI questionnaire, there were no SSDs regarding hours in bed and the use of sleep medications, but there were significant differences in the subjective perception of sleep quality (p < 0.001), sleep efficiency (p < 0.001), duration of sleep (p = 0.001), sleep latency (p = 0.007), daytime dysfunction (p < 0.001), and finally sleep disturbances (p < 0.001). Conclusion: According to our data, OSA patients reporting insomnia-like symptoms and/or sleepiness do not represent a more severe phenotype, by the classic definition of OSA, but their subjective sleep quality is compromised, causing a vicious cycle of anxiety or depression.

Evaluation of a multicomponent grading system for obstructive sleep apnoea: the Baveno classification

New findings on pathophysiology, epidemiology, and outcome have raised concerns on the relevance of the apnoea-hypopnoea index (AHI) in the classification of obstructive sleep apnoea (OSA) severity. Recently, a multicomponent grading system decision integrating symptomatology and comorbidities (Baveno classification), was proposed to characterise OSA and to guide therapeutic decisions. We evaluated whether this system reflects the OSA population, whether it translates into differences in outcomes, and whether the addition of AHI improves the scheme. A total of 14 499 OSA patients from the European Sleep Apnoea Database cohort were analysed. The groups were homogeneously distributed and were found to clearly stratify the population with respect to baseline parameters. Differences in sleepiness and blood pressure between the groups were analysed in a subgroup of patients after 24-36 months of treatment. Group A (minor symptoms and comorbidities) did not demonstrate any effect of treatment on outcome. However, groups B (severe symptoms, minor comorbidities), C (minor symptoms, severe comorbidities) and D (severe symptoms and comorbidities) were associated with improvement in either or both parameters with treatment. The AHI is an essential prerequisite of the diagnosis; however, adding the AHI did not improve the classification. Rather, it was inferior with respect to guiding the treatment decision. Thus, the Baveno classification allows a better stratification of the OSA population and may provide a better guidance for therapeutic decisions in OSA.

Gender Phenotyping of Patients with Obstructive Sleep Apnea Syndrome Using a Network Science Approach

We defined gender-specific phenotypes for men and women diagnosed with obstructive sleep apnea syndrome (OSAS) based on easy-to-measure anthropometric parameters, using a network science approach. We collected data from 2796 consecutive patients since 2005, from 4 sleep laboratories in Western Romania, recording sleep, breathing, and anthropometric measurements. For both genders, we created specific apnea patient networks defined by patient compatibility relationships in terms of age, body mass index (BMI), neck circumference (NC), blood pressure (BP), and Epworth sleepiness score (ESS). We classified the patients with clustering algorithms, then statistically analyzed the groups/clusters. Our study uncovered eight phenotypes for each gender. We found that all males with OSAS have a large NC, followed by daytime sleepiness and high BP or obesity. Furthermore, all unique female phenotypes have high BP, followed by obesity and sleepiness. We uncovered gender-related differences in terms of associated OSAS parameters. In males, we defined the pattern large NC–sleepiness–high BP as an OSAS predictor, while in women, we found the pattern of high BP–obesity–sleepiness. These insights are useful for increasing awareness, improving diagnosis, and treatment response.

Obstructive sleep apnoea phenotypes: The many faces of a public health monolith

See related article

Obstructive sleep apnoea in adult patients post-tonsillectomy

BACKGROUND

The impact of removing the upper airway lymphoid tissue and in particular, tonsillectomy, in adults with OSA has not been demonstrated in large populations.

AIMS

To compare the severity of OSA and the prevalence of cardiovascular, metabolic and respiratory co-morbidities between patients with OSA who had undergone previous tonsillectomy and those who had not.

METHODS

The 19,711 participants in this study came from the European sleep apnea database (ESADA) which comprises data from unselected adult patients aged 18-80 years with a history of symptoms suggestive of OSA referred to sleep centers throughout Europe.

RESULTS

There were no differences between the two groups in terms of sex ratio and age (146 patients with previous tonsillectomy vs. 19565 patients without). Patients who had undergone tonsillectomy had a lower body mass index (29.3 ± 5.2 kg/m² vs 32.2 ± 6.6 kg/m², p < 0.001), lower subjective sleep latency (17.1 ± 17.8 min vs 25.5 ± 30.4 min, p = 0.001), lower ODI (15.7 ± 18.3 events/hour vs 30.7 ± 26.1 events/hour, p < 0.001), and SpO₂<90% time during sleep (21.8 ± 47.5 min vs 52.6 ± 80.8 min, p < 0.001). OSA patients with tonsillectomy had a lower prevalence of Type II diabetes mellitus (p = 0.001), hypertension (p < 0.001) and a higher prevalence of hyperlipidemia (p < 0.001) and were less likely to be commenced on CPAP (p < 0.001).

CONCLUSION

In a large population of almost 20,000 OSA patients from across Europe, patients who had undergone tonsillectomy presented with less severe OSA at time of diagnosis, and had a lower prevalence of Type II diabetes mellitus and cardiovascular co-morbidities.

Clusters of sleep apnoea phenotypes: A large pan-European study from the European Sleep Apnoea Database (ESADA)

BACKGROUND AND OBJECTIVE

To personalize OSA management, several studies have attempted to better capture disease heterogeneity by clustering methods. The aim of this study was to conduct a cluster analysis of 23 000 OSA patients at diagnosis using the multinational ESADA.

METHODS

Data from 34 centres contributing to ESADA were used. An LCA was applied to identify OSA phenotypes in this European population representing broad geographical variations. Many variables, including symptoms, comorbidities and polysomnographic data, were included. Prescribed medications were classified according to the ATC classification and this information was used for comorbidity confirmation.

RESULTS

Eight clusters were identified. Four clusters were gender-based corresponding to 54% of patients, with two clusters consisting only of men and two clusters only of women. The remaining four clusters were mainly men with various combinations of age range, BMI, AHI and comorbidities. The preferred type of OSA treatment (PAP or mandibular advancement) varied between clusters.

CONCLUSION

Eight distinct clinical OSA phenotypes were identified in a large pan-European database highlighting the importance of gender-based phenotypes and the impact of these subtypes on treatment prescription. The impact of cluster on long-term treatment adherence and prognosis remains to be studied using the ESADA follow-up data set.

Prevalence of chronic insomnia in patients with obstructive sleep apnea

Objectives:

To investigate the prevalence of and factors associated with chronic insomnia and obstructive sleep apnea (CIOSA) comorbidity in obstructive sleep apnea (OSA) patients.

Methods:

Between March 2014 and March 2015, we conducted a prospective, cross-sectional study of 238 adults diagnosed with OSA according to polysomnography and International Classification of Sleep Disorders-Third Edition criteria.

Results:

The prevalence of CIOSA was 29%. There was a trend towards older age in the CIOSA group. Sex was not associated with CIOSA. Sleep-maintenance and sleep-onset insomnia predominated in the sample. Beck’s depression and anxiety inventory scores were higher in the CIOSA group. Both depression and anxiety symptoms were associated with CIOSA. The arousal and apnea-hypopnea indices were lower in the CIOSA group. CIOSA was also associated with age ≥ 60 years and current tobacco use. Severe OSA and alcohol use were negatively associated with CIOSA.

Conclusions:

Chronic insomnia is prevalent among OSA patients. Our study highlights the need for detailed evaluation of patients with sleep breathing disorders to diagnose other important sleep and mood disorders (such as depression and anxiety), given their frequent association.

[Respiratory Disease in the Era of Big Data]

One of the key elements of medicine in the second decade of the 21st century is the exponential growth of patient-produced information, due not only to the transition to the digitization of medical records, but also to the emergence of new sources of information and the capacity for analysis and interpretation of existing ones. The amount of medical information is expected to double every 2 years, which means that there will be 50 times more information available in 2020 than in 2011. In this setting, these large amounts of data or «big data» must be properly managed to implement new initiatives that improve the diagnosis, treatment, and prognosis of patients on the path to personalized medicine.The concept of personalization or precision medicine is of special interest in chronic respiratory disease. In recent years, research in entities such as asthma, COPD, cancer, or SAHS has focused on the identification of genomic, molecular, metabolic, and protein changes (biomarkers). Big data analysis tools can be used to move on from models based on the mean response to treatment, which are suboptimal for most patients, to focus on the individualized response. Part of this journey involves systems medicine, which also integrates clinical and population data to provide a multidimensional view of the disease and help identify causal associations that are usually only evident on big data analysis.

Cognitive profiles in obstructive sleep apnea: a cluster analysis in sleep clinic and community samples

STUDY OBJECTIVES

Although cognitive dysfunction is a recognized consequence of untreated obstructive sleep apnea (OSA), the deficit pattern is heterogeneous. Understanding this heterogeneity may identify those at risk of cognitive deficits and guide intervention strategies. To facilitate understanding, we examined whether distinct profiles of neuropsychological performance were present in OSA and, if so, how they are related to other OSA features.

METHODS

We studied sleep clinic (n = 121) and community (n = 398) samples with moderate-severe OSA (apnea-hypopnea index ≥ 15 events/h). Attention and memory were assessed using the Cognitive Drug Research system. Sleep was assessed using polysomnography in the clinic sample and dual channel (flow, oximetry) portable monitoring in the community sample. Latent profile analysis was used to determine structure of cognitive clusters. Discriminant function analysis was used to examine associations between nocturnal and diurnal features of OSA and profile membership.

RESULTS

Both samples were best characterized by a 3-profile solution: (1) strong thinkers (performed well across most domains and showed greater cognitive reserve); (2) inattentive fast thinkers (strong processing speed but poor ability to maintain attention); and (3) accurate slow thinkers (strengths in maintaining attention but poor processing speed). Profile membership was associated with mean overnight oxygen saturation and cognitive reserve in the clinic sample and the presence of cardiovascular disease and/or diabetes in the community sample.

CONCLUSIONS

These findings help explain the diversity of outcomes in previous studies of cognitive dysfunction in OSA by demonstrating that individual differences in cognitive reserve, nocturnal oxygen saturation, and comorbidities affect how cognition is impacted by OSA.