Search

Patients with obstructive sleep apnea (OSA) are at increased risk of cardiovascular and cerebrovascular disease, but predicting those at greatest risk is challenging. Using latent class analysis, patients with OSA can be placed into discrete symptom subtypes. The aim of this study was to determine whether symptom subtypes are associated with future cerebrovascular disease in patients with OSA in a clinic-based cohort.Patients with suspected OSA referred for a polysomnogram at an academic sleep center completed a comprehensive symptom survey. Patients with OSA (apnea-hypopnea index ≥ 5 events/h) were then placed into symptom subtypes based on responses to survey questions using latent class analysis. Cardiovascular events (stroke, myocardial infarction, unstable angina, bypass grafting, percutaneous coronary intervention, cardiac resynchronization therapy, defibrillation) occurring within 8 years of polysomnogram were identified by linkage to provincial health databases.1,607 patients were studied, of whom 1,292 had OSA. One hundred forty first events occurred within 8 years of polysomnogram. Patients in the excessively sleepy with disturbed sleep subtype had a significantly increased rate of events compared to the minimally symptomatic subtype (hazard ratio = 2.25, 95% confidence interval: 1.02-4.94;Patients with OSA in the clinic who are in the excessively sleepy with disturbed sleep subtype are significantly more likely to have a future cardiovascular event. This underscores the importance of understanding clinical heterogeneity and incorporating symptom subtype definitions into routine clinical care.Allen AJH, Jen R, Mazzotti DR, et al. Symptom subtypes and risk of incident cardiovascular and cerebrovascular disease in a clinic-based obstructive sleep apnea cohort.

To examine the relationship between craniofacial skeletal anatomy and objective measures of pharyngeal collapse obtained during drug‐induced sleep endoscopy. We hypothesized that transverse maxillary deficiency and an increased pharyngeal length will be associated with higher levels of pharyngeal collapsibility. Cross‐sectional analysis in a prospective cohort. University Hospital. A cross‐sectional analysis was conducted in a cohort of consecutive patients from the positive airway pressure (PAP) alternatives clinic who underwent computed tomography (CT) analysis and drug‐induced sleep endoscopy for characterization of upper airway collapsibility. PAP titration was used to determine pharyngeal critical pressure (PCRIT) and pharyngeal opening pressure (PhOP). CT metrics included: Transverse maxillary dimensions (interpremolar and intermolar distances) and pharyngeal length (posterior nasal spine to hyoid distance). The cohort (n = 103) of severe obstructive sleep apnea (Apnea and Hipopnea Index 32.1 ± 21.3 events/h) was predominantly male (71.8%), Caucasian (81.6%), middle‐aged (54.4 ± 14.3 years), and obese (body mass index [BMI] = 30.0 ± 4.9 kg/m2). Reduced transverse maxillary dimensions were associated with higher PCRIT (intermolar distance: β [95% confidence interval, CI] = −.25 [−0.14, −0.36] cmH2O/mm; p = .03) and PhOP (Interpremolar distance: β = −.25 [−0.14, −0.36] cmH2O/mm; p = .02). Longer pharyngeal length was also associated with higher PCRIT (β = .11 [0.08, 0.14] cmH2O/mm, p = .04) and PhOP (β [95% CI] = .06 [0.03, 0.09] cmH2O/mm, p = .04). These associations persisted after adjustments for sex, age, height, and BMI. Our results further the concept that skeletal restriction in the transverse dimension and hyoid descent are associated with elevations in pharyngeal collapsibility during sleep, suggesting a role of transverse deficiency in the pathogenesis of airway obstruction.

Recent large-scale human genome-wide association studies (GWAS) for insomnia have identified more than 200 significant loci. The functional relevance of these loci to the pathogenesis of insomnia is largely unknown. GWAS signals are typically non-coding variants, which are often arbitrarily annotated to the nearest protein-coding gene; however, due to 3D chromatin structure, variants can interact with more distal genes driving their function. The distal gene may, therefore, represent the true causal gene influencing the phenotype. By integrating our high-resolution chromatin interaction maps from neural progenitor cells with phenotypic data from aDrosophilaRNAi screen, we prioritized candidate genes that we hypothesized would have deep phylogenetic conservation of sleep function. To determine the conservation of these candidate genes in the context of vertebrate sleep and their relevance to insomnia-like behaviors, we performed CRISPR-Cas9 mutagenesis in larval zebrafish for six highly conserved candidate genes and examined sleep-wake behaviors using automated video-tracking. CRISPR mutation of zebrafish orthologs ofMEIS1andSKIV2Lproduced insomnia-like behaviors, while mutation ofARFGAP2impaired activity and development in our larval zebrafish model, demonstrating the importance of performing functional validation of GWAS-implicated effector genes to reveal genes influencing disease-relevant mechanisms.

The ability to remotely monitor positive airway pressure therapy adherence and efficacy provides a unique opportunity for the field of sleep medicine to quickly and efficiently improve patient adherence. Smaller randomized studies and larger-scale retrospective evaluations show that telemedicine interventions leveraging these data can increase average usage and efficiency of care. However, more evidence on the impact of these programs on longer-term adherence and improving patient-reported outcomes is needed. Combining data from remote monitoring with clinical information in electronic health records may prove to be invaluable to the future of clinical sleep medicine practice and research.

OBJECTIVE. To determine if clinically acquired cephalometric measurements, specifically soft palate size, can predict hypoglossal nerve stimulation outcomes. STUDY DESIGN. Combined prospective cohort study and retrospective review. SETTING. US sleep otolaryngology training program. METHODS. Adults with obstructive sleep apnea and apneahypopnea index greater than 15 events/h who underwent hypoglossal nerve stimulation. Eligible subjects had diagnostic preoperative sleep studies and full-night efficacy postoperative studies for analysis. Lateral neck x-rays were obtained as part of routine clinical care and measured for key cephalometric variables by trained head and neck radiologists. Continuous variables were compared using the Student t test, while χ(2) testing was used for categorical variables. RESULTS. Fifty-one patients met all study criteria. On average, patients were white, middle aged, and overweight. Following hypoglossal nerve stimulation, the overall cohort achieved a significant apnea-hypopnea index reduction from 36.7 events/h to 20.6 events/h (P < .01) and a response rate of 47% (defined as apnea-hypopnea index reduction >50% and apnea-hypopnea index

Background Distinct symptom subtypes are found in patients with OSA. The association between these subtypes and neurocognitive function is unclear. Objective: The purposes of this study were to assess whether OSA symptom subtypes are present in a cohort of Canadian patients with suspected OSA and evaluate the relationship between subtypes and neurocognitive function. Methods Patients with suspected OSA who completed a symptom questionnaire and underwent testing for OSA were included. Symptom subtypes were identified using latent class analysis. Associations between subtypes and neurocognitive outcomes (Montreal Cognitive Assessment [MoCA], Rey Auditory Verbal Learning Test [RAVLT], Wechsler Adult Intelligence Scale [WAIS-IV], Digit-Symbol Coding subtest [DSC]) were assessed using analysis of covariance (ANCOVA), controlling for relevant covariates. Results Four symptom subtypes were identified in patients with OSA (oxygen desaturation index ≥5 events/hour). Three were similar to prior studies, including the Excessively Sleepy (N=405), Disturbed Sleep (N=382) and Minimally Symptomatic (N=280), and one was a novel subtype in our sample defined as Excessively Sleepy with Disturbed Sleep (N=247). After covariate adjustment, statistically significant differences among subtypes (p=0.037) and among subtypes and patients without OSA (p=0.044) were observed in DSC scores; the Minimally Symptomatic subtype had evidence of higher DSC scores than all other groups, including non-OSA patients. No differences were seen in MoCA or RAVLT. Conclusions Results support the existence of previously identified OSA symptom subtypes of excessively sleepy, disturbed sleep and minimally symptomatic in a clinical sample from Canada. Subtypes were not consistently associated with neurocognitive function across multiple instruments.

Rationale: Obesity is the primary risk factor for obstructive sleep apnea (OSA). Tongue fat is increased in obese persons with OSA, and may explain the relationship between obesity and OSA. Weight loss improves OSA, but the mechanism is unknown. Objectives: To determine the effect of weight loss on upper airway anatomy in subjects with obesity and OSA. We hypothesized that weight loss would decrease soft tissue volumes and tongue fat, and that these changes would correlate with reductions in apnea–hypopnea index (AHI). Methods: A total of 67 individuals with obesity and OSA (AHI ≥ 10 events/h) underwent a sleep study and upper airway and abdominal magnetic resonance imaging before and after a weight loss intervention (intensive lifestyle modification or bariatric surgery). Airway sizes and soft tissue, tongue fat, and abdominal fat volumes were quantified. Associations between weight loss and changes in these structures, and relationships to AHI changes, were examined. Measurements and Main Results: Weight loss was significantly associated with reductions in tongue fat and pterygoid and total lateral wall volumes. Reductions in tongue fat were strongly correlated with reductions in AHI (Pearson’s rho = 0.62, P

We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States.Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ≥ 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated.The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates ≥ 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of ≥ 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases.A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research.

RATIONALE AND OBJECTIVES: Accurate segmentation of the upper airway lumen and surrounding soft tissue anatomy, especially tongue fat, using magnetic resonance images is crucial for evaluating the role of anatomic risk factors in the pathogenesis of obstructive sleep apnea (OSA). We present a convolutional neural network to automatically segment and quantify upper airway structures that are known OSA risk factors from unprocessed magnetic resonance images. MATERIALS AND METHODS: Four datasets (n = [31, 35, 64, 76]) with T1-weighted scans and manually delineated labels of 10 regions of interest were used for model training and validations. We investigated a modified U-Net architecture that uses multiple convolution filter sizes to achieve multi-scale feature extraction. Validations included four-fold cross-validation and leave-study-out validations to measure generalization ability of the trained models. Automatic segmentations were also used to calculate the tongue fat ratio, a biomarker of OSA. Dice coefficient, Pearson’s correlation, agreement analyses, and expert-derived clinical parameters were used to evaluate segmentations and tongue fat ratio values. RESULTS: Cross-validated mean Dice coefficient across all regions of interests and scans was 0.70 ± 0.10 with highest mean Dice coefficient in the tongue (0.89) and mandible (0.81). The accuracy was consistent across all four folds. Also, leave-study-out validations obtained comparable accuracy across uniquely acquired datasets. Segmented volumes and the derived tongue fat ratio values showed high correlation with manual measurements, with differences that were not statistically significant (p < 0.05). CONCLUSION: High accuracy of automated segmentations indicate translational potential of the proposed method to replace time consuming manual segmentation tasks in clinical settings and large-scale research studies.

Study Objectives Tongue fat is associated with obstructive sleep apnea (OSA). Magnetic resonance imaging (MRI) is the standard for quantifying tongue fat. Ultrasound echo intensity has been shown to correlate to the fat content in skeletal muscles but has yet to be studied in the tongue. The objective of this study is to evaluate the relationship between ultrasound echo intensity and tongue fat. Methods Ultrasound coronal cross-sections of ex-vivo cow tongues were recorded at baseline and following three 1 mL serial injections of fat into the tongue. In humans, adults with and without OSA had submental ultrasound coronal cross-sections of their posterior tongue. The average echo intensity of the tongues (cow/human) was calculated in ImageJ software. Head and neck MRIs were obtained on human subjects to quantify tongue fat volume. Echo intensity was compared to injected fat volume or MRI-derived tongue fat percentage. Results Echo intensity in cow tongues showed a positive correlation to injected fat volume (rho = 0.93, p < .001). In human subjects, echo intensity of the tongue base strongly correlated with MRI-calculated fat percentage for both the posterior tongue (rho = 0.95, p < .001) and entire tongue (rho = 0.62, p < .001). Larger tongue fat percentages (rho = 0.38, p = .001) and higher echo intensity (rho = 0.27, p = .024) were associated with more severe apnea-hypopnea index, adjusted for age, body mass index, sex, and race. Conclusions Ultrasound echo intensity is a viable surrogate measure for tongue fat volume and may provide a convenient modality to characterize tongue fat in OSA.

Excessive daytime sleepiness includes both an inability to stay awake during the day and a general feeling of sleepiness. We describe different dimensions of daytime sleepiness in adults with moderate-severe obstructive sleep apnea (OSA) before and after 2 years of positive airway pressure (PAP) treatment. Using the Epworth Sleepiness Scale (score >10 defined as "risk of dozing") and Basic Nordic Sleep Questionnaire (feeling sleepy ≥3 times/week defined as "feeling sleepy"), participants were categorised into sleepiness phenotypes labelled non-sleepy, risk of dozing only, feeling sleepy only, or both symptoms. Participants repeated baseline assessments and PAP adherence was evaluated after 2 years. PAP-adherent subjects with sleepiness symptoms at both baseline and follow-up were considered persistently sleepy. Of the 810 participants, 722 (89%) returned for follow-up. At baseline, 17.7% were non-sleepy, 7.7% were at risk of dozing only, 24.7% were feeling sleepy only, and 49.9% had both symptoms. PAP adherence did not differ by baseline sleepiness phenotype. Patients with risk of dozing demonstrated greater PAP benefits for sleepiness symptoms than non-sleepy and feeling sleepy only phenotypes. Using these phenotypes, 42.3% of PAP users had persistent sleepiness; they had less severe OSA (p

It is unclear if the response to positive airway pressure (PAP) treatment is different between African American (AA) and European Americans (EA). We examined whether race modifies the effects of PAP on sleep and daytime function. We assessed Epworth Sleepiness Scale (ESS), Functional Outcomes of Sleep Questionnaire, Psychomotor Vigilance Task and actigraphy in 185 participants with moderate-to-severe obstructive sleep apnea before and 3–4 months after PAP treatment. The participants were middle-aged (mean, 55.1 years), 83.8% men and 60.5% AA. Linear regression models were used to examine the effect of race on outcomes. The AA had smaller reductions in ESS (mean change (95% confidence interval, CI) AA, −2.30 [−3.35, −1.25] vs. EA, −4.16 [−5.48, −2.84] and frequency of awakenings (AA, −0.73 [−4.92, 3.47] vs. EA, −9.35 [−15.20, −3.51]). A race × PAP usage interaction term was added to the model to examine if the change in outcomes per 1 h increase in PAP usage differed by race. AA exhibited greater improvement in wake after sleep onset (β (95% CI) AA, −8.89 [−16.40, −1.37] vs. EA, 2.49 [−4.15, 9.12]) and frequency of awakening (β (95% CI) AA, −2.59 [−4.44, −0.75] vs. EA, 1.71 [−1.08, 4.50]). The results indicate the importance of race in evaluating outcomes following PAP treatment.

Naima Laharnar,1,&ast; Sebastian Herberger,1,&ast; Lisa-Kristin Prochnow,1 Ning-Hung Chen,2 Peter A Cistulli,3,4 Allan I Pack,5 Richard Schwab,5 Brendan T Keenan,5 Diego R Mazzotti,5,6 Ingo Fietze,1,7 Thomas Penzel1,8 1Department of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany; 2Department of Pulmonary and Critical Care Medicine, Sleep Center, Chang Gung Memorial Hospital, Taipei, Taiwan; 3Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia; 4Department of Respiratory Medicine, Royal North Shore Hospital, Sydney, NSW, Australia; 5Department of Medicine/Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 6Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, USA; 7The Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov, First Moscow State Medical University of the Ministry of Health of the Russian Federation, Moscow, Russia; 8Department of Biology, Saratov State University, Saratov, Russia&ast;These authors contributed equally to this workCorrespondence: Naima LaharnarDepartment of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité – Universitätsmedizin Berlin, Campus Charité Mitte, Chariteplatz 1, Berlin, 10117, GermanyTel +49-30 450 513 120Email naima.laharnar@charite.dePurpose: 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).Keywords: obstructive sleep apnea, diagnostic, sensitivity, specificity, screening

Study Objectives Randomized controlled trials (RCTs) have shown no reduction in adverse cardiovascular (CV) events in patients randomized to continuous positive airway pressure (CPAP) therapy for obstructive sleep apnea (OSA). This study examined whether randomized study populations were representative of OSA patients attending a sleep clinic. Methods Sleep clinic patients were 3,965 consecutive adults diagnosed with OSA by in-laboratory polysomnography from 2006 to 2010 at a tertiary hospital sleep clinic. Characteristics of these patients were compared with participants of five recent RCTs examining the effect of CPAP on adverse CV events in OSA. The percentage of patients with severe (apnea-hypopnea index, [AHI] ≥ 30 events/h) or any OSA (AHI ≥ 5 events/h) who met the eligibility criteria of each RCT was determined, and those criteria that excluded the most patients identified. Results Compared to RCT participants, sleep clinic OSA patients were younger, sleepier, more likely to be female and less likely to have established CV disease. The percentage of patients with severe or any OSA who met the RCT eligibility criteria ranged from 1.2% to 20.9% and 0.8% to 21.9%, respectively. The eligibility criteria that excluded most patients were preexisting CV disease, symptoms of excessive sleepiness, nocturnal hypoxemia and co-morbidities. Conclusions A minority of sleep clinic patients diagnosed with OSA meet the eligibility criteria of RCTs of CPAP on adverse CV events in OSA. OSA populations in these RCTs differ considerably from typical sleep clinic OSA patients. This suggests that the findings of such OSA treatment-related RCTs are not generalizable to sleep clinic OSA patients. Randomized Intervention with Continuous Positive Airway Pressure in CAD and OSA (RICCADSA) trial, https://clinicaltrials.gov/ct2/show/NCT00519597, ClinicalTrials.gov number, NCT00519597. Usefulness of Nasal Continuous Positive Airway Pressure (CPAP) Treatment in Patients with a First Ever Stroke and Sleep Apnea Syndrome, https://clinicaltrials.gov/ct2/show/NCT00202501, ClinicalTrials.gov number, NCT00202501. Effect of Continuous Positive Airway Pressure (CPAP) on Hypertension and Cardiovascular Morbidity-Mortality in Patients with Sleep Apnea and no Daytime Sleepiness, https://clinicaltrials.gov/ct2/show/NCT00127348, ClinicalTrials.gov number, NCT00127348. Continuous Positive Airway Pressure (CPAP) in Patients with Acute Coronary Syndrome and Obstructive Sleep Apnea (OSA) (ISAACC), https://clinicaltrials.gov/ct2/show/NCT01335087, ClinicalTrials.gov number, NCT01335087.

BACKGROUND: Prediction tools without patient-reported symptoms could facilitate widespread identification of OSA. RESEARCH QUESTION: What is the diagnostic performance of OSA prediction tools derived from machine learning using readily available data without patient responses to questionnaires? Also, how do they compare with STOP-BANG, an OSA prediction tool, in clinical and community-based samples? STUDY DESIGN AND METHODS: Logistic regression and machine learning techniques, including artificial neural network (ANN), random forests (RF), and kernel support vector machine, were used to determine the ability of age, sex, BMI, and race to predict OSA status. A retrospective cohort of 17,448 subjects from sleep clinics within the international Sleep Apnea Global Interdisciplinary Consortium (SAGIC) were randomly split into training (n = 10,469) and validation (n = 6,979) sets. Model comparisons were performed by using the area under the receiver-operating curve (AUC). Trained models were compared with the STOP-BANG questionnaire in two prospective testing datasets: an independent clinic-based sample from SAGIC (n = 1,613) and a community-based sample from the Sleep Heart Health Study (n = 5,599). RESULTS: The AUCs (95% CI) of the machine learning models were significantly higher than logistic regression (0.61 [0.60-0.62]) in both the training and validation datasets (ANN, 0.68 [0.66-0.69]; RF, 0.68 [0.67-0.70]; and kernel support vector machine, 0.66 [0.65-0.67]). In the SAGIC testing sample, the ANN (0.70 [0.68-0.72]) and RF (0.70 [0.68-0.73]) models had AUCs similar to those of the STOP-BANG (0.71 [0.68-0.72]). In the Sleep Heart Health Study testing sample, the ANN (0.72 [0.71-0.74]) had AUCs similar to those of STOP-BANG (0.72 [0.70-0.73]). INTERPRETATION: OSA prediction tools using machine learning without patient-reported symptoms provide better diagnostic performance than logistic regression. In clinical and community-based samples, the symptomless ANN tool has diagnostic performance similar to that of a widely used prediction tool that includes patient symptoms. Machine learning-derived algorithms may have utility for widespread identification of OSA.

BackgroundThe impact of positive airway pressure (PAP) therapy for obstructive sleep apnea (OSA) on healthcare costs is uncertain.Research QuestionAre three-year healthcare costs associated with PAP adherence in participants from the Tele-OSA clinical trial?Study Design and MethodsParticipants with OSA and prescribed PAP in the Tele-OSA study were stratified into three PAP adherence groups based on usage patterns over three years: (a) high (consistently ≥ 4 hours/night); (b) moderate (2-3.9 hours/night or inconsistently ≥ 4 hours/night); (c) low (ResultsOf 543 participants, 25% were categorized as having high adherence, 22% moderate adherence, and 52% low adherence to PAP therapy. Average (standard deviation) PAP use was 6.5 (1.0) hours, 3.7 (1.2) hours, and 0.5 (0.5) hours for the high, moderate, and low adherence groups, respectively. The high adherence group had the lowest average [standard error] covariate-adjusted six-month healthcare costs ($3,162 [$240]) compared with the moderate ($3,658 [$369]) and low ($4,016 [$315]) adherence groups. Significant cost savings were observed between the high and low adherence groups ($854 [95% CI $158, $1,551]); savings between moderate and low adherence were non-significant ($359 [95% CI -$459, $1,176]).InterpretationIn participants with OSA, better PAP adherence was associated with significantly lower healthcare costs over three years. Findings support the importance of strategies to enhance long-term PAP adherence.

Like other animals, the nematode C. elegans exhibits reduced movement and sleep in response to sickness, which can be induced by exposure to high temperatures (Hill et al. 2014; Nelson et al. 2014) ultraviolet light (DeBardeleben et al. 2017), and other stressful exposures (Hill et al. 2014; Goetting et al. 2020). This response has been termed Stress/Sickness-Induced Sleep (SIS) (Hill et al. 2014; Trojanowski and Raizen 2016). Exposure to the stressor leads to quiescence in part via release of the cytokine Epidermal Growth Factor (EGF) (Hill et al. 2014; Konietzka et al. 2020), which is encoded by the gene lin-3 (Hill and Sternberg 1992). EGF activates the ALA and RIS neurons, which then release their respective neuropeptides to effect reduced movement and behavioral quiescence (Konietzka et al. 2020).

Rationale: Obstructive sleep apnea (OSA) occurring in pregnancy has been associated with maternal and fetal complications. Early detection of OSA may have important implications for maternal-fetal ...

Many patients with obstructive sleep apnea (OSA), but not all, have a reduction in blood pressure (BP) with positive airway pressure (PAP) treatment. Our objective was to determine whether the BP response following PAP treatment is related to obesity. A total of 188 adults with OSA underwent 24-hour BP monitoring and 24-hour urinary norepinephrine collection at baseline. Obesity was assessed by waist circumference, body mass index, and abdominal visceral fat volume. Participants adherent to PAP treatment were reassessed after 4 months. Primary outcomes were 24-hour mean arterial pressure (MAP) and 24-hour urinary norepinephrine level. Obstructive sleep apnea participants had a significant reduction in 24-hour MAP following PAP treatment (-1.22 [95% CI: -2.38, -0.06] mm Hg; P = .039). No significant correlations were present with any of the 3 obesity measures for BP or urinary norepinephrine measures at baseline in all OSA participants or for changes in BP measures in participants adherent to PAP treatment. Changes in BP measures following treatment were not correlated with baseline or change in urinary norepinephrine. Similar results were obtained when BP or urinary norepinephrine measures were compared between participants dichotomized using the sex-specific median of each obesity measure. Greater reductions in urinary norepinephrine were correlated with higher waist circumference (rho = -0.21, P = .037), with a greater decrease from baseline in obese compared to non-obese participants (-6.26 [-8.82, -3.69] vs -2.14 [-4.63, 0.35] ng/mg creatinine; P = .027). The results indicate that the BP response to PAP treatment in adults with OSA is not related to obesity or urinary norepinephrine levels.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is a global health issue and is associated with obesity and oropharyngeal crowding. Global data are limited on the effect of ethnicity and sex on these relationships. We compare associations between the apnea-hypopnea index (AHI) and these risk factors across ethnicities and sexes within sleep clinics. METHODS: This is a cross-sectional, multicenter study of patients with OSA from eight sleep centers representing the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Four distinct ethnic groups were analyzed, using a structured questionnaire: Caucasians (Australia, Iceland, Germany, United States), African Americans (United States), Asians (Taiwan), and South Americans (Brazil). Regression analyses and interaction tests were used to assess ethnic and sex differences in relationships between AHI and anthropometric measures (body mass index [BMI], neck circumference, waist circumference) or Mallampati score. RESULTS: Analyses included 1,585 individuals from four ethnic groups: Caucasian (60.6%), African American (17.5%), Asian (13.1%), and South American (8.9%). BMI was most strongly associated with AHI in South Americans (7.8% increase in AHI per 1 kg/m(2) increase in BMI; P < .0001) and most weakly in African Americans (1.9% increase in AHI per 1 kg/m(2) increase in BMI; P = .002). In Caucasians and South Americans, associations were stronger in males than females. Mallampati score differed between ethnicities but did not influence AHI differently across groups. CONCLUSIONS: We demonstrate ethnic and sex variations in associations between obesity and OSA. For similar BMI increases, South American patients show greatest AHI increases compared to African Americans. Findings highlight the importance of considering ethnicity and sex in clinical assessments of OSA risk. CITATION: Sutherland K, Keenan BT, Bittencourt L, Chen NH, Gislason T, Leinwand S, Magalang UJ, Maislin G, Mazzotti DR, McArdle N, Mindel J, Pack AI, Penzel T, Singh B, Tufik S, Schwab RJ, Cistulli PA; for the SAGIC Investigators. A global comparison of anatomic risk factors and their relationship to obstructive sleep apnea severity in clinical samples. J Clin Sleep Med. 2019;15(4):629–639.

Summary Many aspects of sleep are heritable, but only a few sleep-regulating genes have been reported. Here, we leverage mouse models to identify and confirm a previously unreported gene affecting sleep duration—dihydropyrimidine dehydrogenase (Dpyd). Using activity patterns to quantify sleep in 325 Diversity Outbred (DO) mice—a population with high genetic and phenotypic heterogeneity—a linkage peak for total sleep in the active lights off period was identified on chromosome 3 (LOD score = 7.14). Mice with the PWK/PhJ ancestral haplotype at this location demonstrated markedly reduced sleep. Among the genes within the linkage region, available RNA sequencing data in an independent sample of DO mice supported a highly significant expression quantitative trait locus for Dpyd, wherein reduced expression was associated with the PWK/PhJ allele. Validation studies were performed using activity monitoring and EEG/EMG recording in Collaborative Cross mouse strains with and without the PWK/PhJ haplotype at this location, as well as EEG and EMG recording of sleep and wake in Dpyd knockout mice and wild-type littermate controls. Mice lacking Dpyd had 78.4 min less sleep during the lights-off period than wild-type mice (p = 0.007; Cohen’s d = −0.94). There was no difference in other measured behaviors in knockout mice, including assays evaluating cognitive-, social-, and affective-disorder-related behaviors. Dpyd encodes the rate-limiting enzyme in the metabolic pathway that catabolizes uracil and thymidine to β-alanine, an inhibitory neurotransmitter. Thus, data support β-alanine as a neurotransmitter that promotes sleep in mice.