Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders.

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in ß and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes.

Rationale: Symptom subtypes have been described in clinical and population samples of patients with obstructive sleep apnea (OSA). It is unclear whether these subtypes have different cardiovascular consequences.Objectives: To characterize OSA symptom subtypes and assess their association with prevalent and incident cardiovascular disease in the Sleep Heart Health Study.Methods: Data from 1,207 patients with OSA (apnea-hypopnea index ≥ 15 events/h) were used to evaluate the existence of symptom subtypes using latent class analysis. Associations between subtypes and prevalence of overall cardiovascular disease and its components (coronary heart disease, heart failure, and stroke) were assessed using logistic regression. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate whether subtypes were associated with incident events, including cardiovascular mortality.Measurements and Main Results: Four symptom subtypes were identified (disturbed sleep [12.2%], minimally symptomatic [32.6%], excessively sleepy [16.7%], and moderately sleepy [38.5%]), similar to prior studies. In adjusted models, although no significant associations with prevalent cardiovascular disease were found, the excessively sleepy subtype was associated with more than threefold increased risk of prevalent heart failure compared with each of the other subtypes. Symptom subtype was also associated with incident cardiovascular disease (P < 0.001), coronary heart disease (P = 0.015), and heart failure (P = 0.018), with the excessively sleepy again demonstrating increased risk (hazard ratios, 1.7-2.4) compared with other subtypes. When compared with individuals without OSA (apnea-hypopnea index < 5), significantly increased risk for prevalent and incident cardiovascular events was observed mostly for patients in the excessively sleepy subtype.Conclusions: OSA symptom subtypes are reproducible and associated with cardiovascular risk, providing important evidence of their clinical relevance.

Obstructive Sleep Apnea: Update and Future.

Obstructive sleep apnea (OSA) is a worldwide disease whose prevalence is increasing as obesity rates increase. The link between obesity and OSA is likely to be the deposition of fat in the tongue, compromising upper airway size. The role of obesity varies in different ethnic groups, with Chinese being particularly sensitive to increases in weight. OSA lends itself to a personalized approach to diagnosis and therapy. For example, different clinical OSA subtypes likely benefit from therapy in different ways. Hypoglossal nerve stimulation is a useful second-line therapy in patients who cannot tolerate continous positive airway pressure (CPAP) machines or intraoral devices. Technological advances allow patients to participate in their own care, and doing so improves CPAP compliance. We are entering a future where we can focus efforts to predict and prevent OSA on an individual level.

Home sleep apnea testing: comparison of manual and automated scoring across international sleep centers.

PURPOSE: To determine the agreement between the manual scoring of home sleep apnea tests (HSATs) by international sleep technologists and automated scoring systems. METHODS: Fifteen HSATs, previously recorded using a type 3 monitor, were saved in European Data Format. The studies were scored by nine experienced technologists from the sleep centers of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC) using the locally available software. Each study was scored separately by human scorers using the nasal pressure (NP), flow derived from the NP signal (transformed NP), or respiratory inductive plethysmography (RIP) flow. The same procedure was followed using two automated scoring systems: Remlogic (RLG) and Noxturnal (NOX). RESULTS: The intra-class correlation coefficients (ICCs) of the apnea-hypopnea index (AHI) scoring using the NP, transformed NP, and RIP flow were 0.96 [95% CI 0.93-0.99], 0.98 [0.96-0.99], and 0.97 [0.95-0.99], respectively. Using the NP signal, the mean differences in AHI between the average of the manual scoring and the automated systems were - 0.9 ± 3.1/h (AHIRLG vs AHIMANUAL) and - 1.3 ± 2.6/h (AHINOX vs AHIMANUAL). Using the transformed NP, the mean differences in AHI were - 1.9 ± 3.3/h (AHIRLG vs AHIMANUAL) and 1.6 ± 3.0/h (AHINOX vs AHIMANUAL). Using the RIP flow, the mean differences in AHI were - 2.7 ± 4.5/h (AHIRLG vs AHIMANUAL) and 2.3 ± 3.4/h (AHINOX vs AHIMANUAL). CONCLUSIONS: There is very strong agreement in the scoring of the AHI for HSATs between the automated systems and experienced international technologists. Automated scoring of HSATs using commercially available software may be useful to standardize scoring in future endeavors involving international sleep centers.

P4 medicine approach to obstructive sleep apnoea.

P4 medicine is an evolving approach to personalized medicine. The four Ps offer a means to: Predict who will develop disease and co-morbidities; Prevent rather than react to disease; Personalize diagnosis and treatment; have patients Participate in their own care. P4 medicine is very applicable to obstructive sleep apnoea (OSA) because each OSA patient has a different pathway to disease and its consequences. OSA has both structural and physiological mechanisms with different clinical subgroups, different molecular profiles and different consequences. This may explain why there are different responses to alternative therapies, such as intraoral devices and hypoglossal nerve stimulation therapy. Currently, technology facilitates patients to participate in their own care from screening for OSA (snoring and apnoea apps) to monitoring response to therapy (sleep monitoring, blood pressure, oxygen saturation and heart rate) as well as monitoring their own continuous positive airway pressure (CPAP) compliance. We present a conceptual framework that provides the basis for a new, P4 medicine approach to OSA and should be considered more in depth: predict and prevent those at high risk for OSA and consequences, personalize the diagnosis and treatment of OSA and build in patient participation to manage OSA.

Effects of obesity on the association between long-term sleep apnea treatment and changes in interleukin-6 levels: the Icelandic Sleep Apnea Cohort.

The aim of this study was to evaluate changes in interleukin (IL)-6 and soluble IL-6 receptor levels in obstructive sleep apnea patients and assess the role of positive airway pressure treatment and obesity on these changes. A total of 309 newly diagnosed subjects with sleep apnea from the Icelandic Sleep Apnea Cohort were referred for treatment and reassessed at a 2-year follow-up. Full treatment was defined objectively as use ≥ 4 h day(-1) and ≥ 20 days month(-1). At the 2-year follow-up, there were 177 full users, 44 partial users and 88 non-users. The mean change in biomarker levels from baseline to the 2-year follow-up was assessed in a primary model that included adjustment for baseline biomarker levels, baseline body mass index and change in body mass index, as well as after adjustment for numerous relevant covariates. No significant overall difference in IL-6 level change was found among full, partial and non-users. However, in severely obese patients (body mass index ≥ 35), a significant increase in IL-6 levels during the 2-year period was found in partial and non-users, compared to no change in full users. Results were attenuated in a smaller propensity score matched subsample, although similar trends were observed. No differences were found in soluble IL-6 receptor levels between full users and non-users, after adjustment for confounders. In conclusion, among untreated obese sleep apnea patients, IL-6 levels increase substantially during 2 years, while adherence to positive airway pressure treatment may prevent further increases in this inflammatory biomarker.

A novel methodology for emotion recognition through 62-lead EEG signals: multilevel heterogeneous recurrence analysis.

Objective: Recognizing emotions from electroencephalography (EEG) signals is a challenging task due to the complex, nonlinear, and nonstationary characteristics of brain activity. Traditional methods often fail to capture these subtle dynamics, while deep learning approaches lack explainability. In this research, we introduce a novel three-phase methodology integrating manifold embedding, multilevel heterogeneous recurrence analysis (MHRA), and ensemble learning to address these limitations in EEG-based emotion recognition. Approach: The proposed methodology was evaluated using the SJTU-SEED IV database. We first applied uniform manifold approximation and projection (UMAP) for manifold embedding of the 62-lead EEG signals into a lower-dimensional space. We then developed MHRA to characterize the complex recurrence dynamics of brain activity across multiple transition levels. Finally, we employed tree-based ensemble learning methods to classify four emotions (neutral, sad, fear, happy) based on the extracted MHRA features. Main results: Our approach achieved high performance, with an accuracy of 0.7885 and an AUC of 0.7552, outperforming existing methods on the same dataset. Additionally, our methodology provided the most consistent recognition performance across different emotions. Sensitivity analysis revealed specific MHRA metrics that were strongly associated with each emotion, offering valuable insights into the underlying neural dynamics. Significance: This study presents a novel framework for EEG-based emotion recognition that effectively captures the complex nonlinear and nonstationary dynamics of brain activity while maintaining explainability. The proposed methodology offers significant potential for advancing our understanding of emotional processing and developing more reliable emotion recognition systems with broad applications in healthcare and beyond.

Orthotopic Models Using New, Murine Lung Adenocarcinoma Cell Lines Simulate Human Non-Small Cell Lung Cancer Treated with Immunotherapy.

Understanding tumor-host immune interactions and the mechanisms of lung cancer response to immunotherapy is crucial. Current preclinical models used to study this often fall short of capturing the complexities of human lung cancer and lead to inconclusive results. To bridge the gap, we introduce two new murine monoclonal lung cancer cell lines for use in immunocompetent orthotopic models. We demonstrate how our cell lines exhibit immunohistochemical protein expression (TTF-1, NapA, PD-L1) and common driver mutations (KRAS, p53, and p110α) seen in human lung adenocarcinoma patients, and how our orthotopic models respond to combination immunotherapy in vivo in a way that closely mirrors current clinical outcomes. These new lung adenocarcinoma cell lines provide an invaluable, clinically relevant platform for investigating the intricate dynamics between tumor and the immune system, and thus potentially contributes to a deeper understanding of immunotherapeutic approaches to lung cancer treatment.

Screening for obstructive sleep apnea in patients with cancer - a machine learning approach.

Background: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with daytime sleepiness, fatigue, and increased all-cause mortality risk in patients with cancer. Existing screening tools for OSA do not account for the interaction of cancer-related features that may increase OSA risk. Study Design and Methods: This is a retrospective study of patients with cancer at a single tertiary cancer institution who underwent a home sleep apnea test (HSAT) to evaluate for OSA. Unsupervised machine learning (ML) was used to reduce the dimensions and extract significant features associated with OSA. ML classifiers were applied to principal components and model hyperparameters were optimized using k-fold cross-validation. Training models for OSA were subsequently tested and compared with the STOP-Bang questionnaire on a prospective unseen test set of patients who underwent an HSAT. Results: From a training dataset of 249 patients, kernel principal component analysis (PCA) extracted eight components through dimension reduction to explain the maximum variance with OSA at 98%. Predictors of OSA were smoking, asthma, chronic kidney disease, STOP-Bang score, race, diabetes, radiation to head/neck/thorax (RT-HNT), type of cancer, and cancer metastases. Of the ML models, PCA + RF had the highest sensitivity (96.8%), specificity (92.3%), negative predictive value (92%), F1 score (0.93), and ROC-AUC score (0.88). The PCA + RF screening algorithm also performed better than the STOP-Bang questionnaire alone when tested on a prospective unseen test set. Conclusions: The PCA + RF ML model had the highest accuracy in screening for OSA in patients with cancer. History of RT-HNT, cancer metastases, and type of cancer were identified as cancer-related risk factors for OSA.

Dual inhibition of IDO1/TDO2 enhances anti-tumor immunity in platinum-resistant non-small cell lung cancer.

BACKGROUND: The impact of non-small cell lung cancer (NSCLC) metabolism on the immune microenvironment is not well understood within platinum resistance. We have identified crucial metabolic differences between cisplatin-resistant (CR) and cisplatin-sensitive (CS) NSCLC cells with elevated indoleamine 2,3-dioxygenase-1 (IDO1) activity in CR, recognized by increased kynurenine (KYN) production. METHODS: Co-culture, syngeneic, and humanize mice models were utilized. C57BL/6 mice were inoculated with either Lewis lung carcinoma mouse cells (LLC) or their platinum-resistant counterpart (LLC-CR) cells. Humanized mice were inoculated with either A (human CS cells) or ALC (human CR cells). Mice were treated with either IDO1 inhibitor or TDO2 (tryptophan 2,3-dioxygenase-2) inhibitor at 200 mg/kg P.O. once a day for 15 days; or with a new-in-class, IDO1/TDO2 dual inhibitor AT-0174 at 170 mg/kg P.O. once a day for 15 days with and without anti-PD1 antibody (10 mg/kg, every 3 days). Immune profiles and KYN and tryptophan (TRP) production were evaluated. RESULTS: CR tumors exhibited a more highly immunosuppressive environment that debilitated robust anti-tumor immune responses. IDO1-mediated KYN production from CR cells suppressed NKG2D on immune effector natural killer (NK) and CD8+ T cells and enhanced immunosuppressive populations of regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs). Importantly, while selective IDO1 inhibition attenuated CR tumor growth, it concomitantly upregulated the TDO2 enzyme. To overcome the compensatory induction of TDO2 activity, we employed the IDO1/TDO2 dual inhibitor, AT-0174. Dual inhibition of IDO1/TDO2 in CR mice suppressed tumor growth to a greater degree than IDO1 inhibition alone. Significant enhancement in NKG2D frequency on NK and CD8+ T cells and a reduction in Tregs and MDSCs were observed following AT-1074 treatment. PD-L1 (programmed death-ligand-1) expression was increased in CR cells; therefore, we assessed dual inhibition + PD1 (programmed cell death protein-1) blocking and report profound anti-tumor growth and improved immunity in CR tumors which in turn extended overall survival in mice. CONCLUSION: Our study reports the presence of platinum-resistant lung tumors that utilize both IDO1/TDO2 enzymes for survival, and to escape immune surveillance as a consequence of KYN metabolites. We also report early in vivo data in support of the potential therapeutic efficacy of the dual IDO1/TDO2 inhibitor AT-0174 as a part of immuno-therapeutic treatment that disrupts tumor metabolism and enhances anti-tumor immunity.

The need to promote sleep health in public health agendas across the globe.

Healthy sleep is essential for physical and mental health, and social wellbeing; however, across the globe, and particularly in developing countries, national public health agendas rarely consider sleep health. Sleep should be promoted as an essential pillar of health, equivalent to nutrition and physical activity. To improve sleep health across the globe, a focus on education and awareness, research, and targeted public health policies are needed. We recommend developing sleep health educational programmes and awareness campaigns; increasing, standardising, and centralising data on sleep quantity and quality in every country across the globe; and developing and implementing sleep health policies across sectors of society. Efforts are needed to ensure equity and inclusivity for all people, particularly those who are most socially and economically vulnerable, and historically excluded.

Facial and Intraoral Photographic Traits Related to Sleep Apnea in a Clinical Sample with Genetic Ancestry Analysis.

Rationale: Craniofacial and pharyngeal morphology influences risk for obstructive sleep apnea (OSA). Quantitative photography provides phenotypic information about these anatomical factors and is feasible in large samples. However, whether associations between morphology and OSA severity differ among populations is unknown. Objectives: The aim of this study was to examine this question in a large sample encompassing people from different ancestral backgrounds. Methods: Participants in SAGIC (Sleep Apnea Global Interdisciplinary Consortium) with genotyping data were included (N = 2,393). Associations between photography-based measures and OSA severity were assessed using linear regression, controlling for age, sex, body mass index, and genetic ancestry. Subgroups (on the basis of 1000 Genomes reference populations) were identified: European (EUR), East Asian, American, South Asian, and African (AFR). Interaction tests were used to assess if genetically determined ancestry group modified these relationships. Results: Cluster analysis of genetic ancestry proportions identified four ancestrally defined groups: East Asia (48.3%), EUR (33.6%), admixed (11.7%; 46% EUR, 27% Americas, and 22% AFR), and AFR (6.4%). Multiple anatomical traits were associated with more severe OSA independent of ancestry, including larger cervicomental angle (standardized ß [95% confidence interval (CI)] = 0.11 [0.06-0.16]; P < 0.001), mandibular width (standardized ß [95% CI] = 0.15 [0.10-0.20]; P < 0.001), and tongue thickness (standardized ß [95% CI] = 0.06 [0.02-0.10]; P = 0.001) and smaller airway width (standardized ß [95% CI] = -0.08 [-0.15 to -0.002]; P = 0.043). Other traits, including maxillary and mandibular depth angles and lower face height, demonstrated different associations with OSA severity on the basis of ancestrally defined subgroups. Conclusions: We confirm that multiple facial and intraoral photographic measurements are associated with OSA severity independent of ancestral background, whereas others differ in their associations among the ancestrally defined subgroups.

Synaptic dysfunction connects autism spectrum disorder and sleep disturbances: A perspective from studies in model organisms.

Sleep disturbances (SD) accompany many neurodevelopmental disorders, suggesting SD is a transdiagnostic process that can account for behavioral deficits and influence underlying neuropathogenesis. Autism Spectrum Disorder (ASD) comprises a complex set of neurodevelopmental conditions characterized by challenges in social interaction, communication, and restricted, repetitive behaviors. Diagnosis of ASD is based primarily on behavioral criteria, and there are no drugs that target core symptoms. Among the co-occurring conditions associated with ASD, SD are one of the most prevalent. SD often arises before the onset of other ASD symptoms. Sleep interventions improve not only sleep but also daytime behaviors in children with ASD. Here, we examine sleep phenotypes in multiple model systems relevant to ASD, e.g., mice, zebrafish, fruit flies and worms. Given the functions of sleep in promoting brain connectivity, neural plasticity, emotional regulation and social behavior, all of which are of critical importance in ASD pathogenesis, we propose that synaptic dysfunction is a major mechanism that connects ASD and SD. Common molecular targets in this interplay that are involved in synaptic function might be a novel avenue for therapy of individuals with ASD experiencing SD. Such therapy would be expected to improve not only sleep but also other ASD symptoms.

Predicting brain age based on sleep EEG and DenseNet.

We proposed a sleep EEG-based brain age prediction model which showed higher accuracy than previous models. Six-channel EEG data were acquired for 6 hours sleep. We then converted the EEG data into 2D scalograms, which were subsequently inputted to DenseNet used to predict brain age. We then evaluated the association between brain aging acceleration and sleep disorders such as insomnia and OSA.The correlation between chronological age and expected brain age through the proposed brain age prediction model was 80% and the mean absolute error was 5.4 years. The proposed model revealed brain age increases in relation to the severity of sleep disorders.In this study, we demonstrate that the brain age estimated using the proposed model can be a biomarker that reflects changes in sleep and brain health due to various sleep disorders.Clinical Relevance-Proposed brain age index can be a single index that reflects the association of various sleep disorders and serve as a tool to diagnose individuals with sleep disorders.

Neural injury in sleep apnea.

Sleepiness has long been recognized as a presenting symptom in obstructive sleep apnea syndrome, but persistent neurocognitive injury from sleep apnea has been appreciated only recently. Although therapy for sleep apnea markedly improves daytime symptoms, cognitive impairments may persist despite long-term therapy with continuous positive airway pressure. We know now that certain groups of neurons, typically those that are more metabolically active, are more vulnerable to injury than others. Animal models of sleep apnea oxygenation patterns have been instrumental in elucidating mechanisms of injury. The hypoxia/reoxygenation events result in oxidative, inflammatory, and endoplasmic reticulum stress responses in susceptible neural groups. With molecular pathways being fleshed out in animal models, it is time to carefully and systematically examine neural injury in humans and test the applicability of findings from animal models. To succeed, however, we cannot view sleep apnea as an isolated process. Rather, injury in sleep apnea is more likely the consequence of overlapping injuries from comorbid conditions. The progress in elucidating mechanisms of neural injury is palpable, and it now seems we indeed are closer to developing therapies to prevent and treat neural injury in obstructive sleep apnea.

Blood pressure monitoring in sleep: time to wake up.

Hypertension is a highly common condition with well-established adverse consequences. Ambulatory blood pressure monitoring has repeatedly been shown to better predict cardiovascular outcomes and mortality, compared to single office visit blood pressure. Non-dipping of sleep-time blood pressure is an independent marker for increased cardiovascular risk. We review blood pressure variability and the challenges of blood pressure monitoring during sleep. Although pathological sleep such as obstructive sleep apnea has been associated with non-dipping of sleep-time blood pressure, blood pressure is not routinely measured during sleep due to lack of unobtrusive blood pressure monitoring technology. Second, we review existing noninvasive continuous blood pressure monitoring technologies. Lastly, we propose including sleep-time blood pressure monitoring during sleep studies and including sleep studies in patients undergoing ambulatory blood pressure monitoring.

Reinventing polysomnography in the age of precision medicine.

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

High-throughput sleep phenotyping produces robust and heritable traits in Diversity Outbred mice and their founder strains.

STUDY OBJECTIVES: This study describes high-throughput phenotyping strategies for sleep and circadian behavior in mice, including examinations of robustness, reliability, and heritability among Diversity Outbred (DO) mice and their eight founder strains. METHODS: We performed high-throughput sleep and circadian phenotyping in male mice from the DO population (n = 338) and their eight founder strains: A/J (n = 6), C57BL/6J (n = 14), 129S1/SvlmJ (n = 6), NOD/LtJ (n = 6), NZO/H1LtJ (n = 6), CAST/EiJ (n = 8), PWK/PhJ (n = 8), and WSB/EiJ (n = 6). Using infrared beam break systems, we defined sleep as at least 40 s of continuous inactivity and quantified sleep-wake amounts and bout characteristics. We developed assays to measure sleep latency in a new environment and during a modified Murine Multiple Sleep Latency Test, and estimated circadian period from wheel-running experiments. For each trait, broad-sense heritability (proportion of variability explained by all genetic factors) was derived in founder strains, while narrow-sense heritability (proportion of variability explained by additive genetic effects) was calculated in DO mice. RESULTS: Phenotypes were robust to different inactivity durations to define sleep. Differences across founder strains and moderate/high broad-sense heritability were observed for most traits. There was large phenotypic variability among DO mice, and phenotypes were reliable, although estimates of heritability were lower than in founder mice. This likely reflects important nonadditive genetic effects. CONCLUSIONS: A high-throughput phenotyping strategy in mice, based primarily on monitoring of activity patterns, provides reliable and heritable estimates of sleep and circadian traits. This approach is suitable for discovery analyses in DO mice, where genetic factors explain some proportion of phenotypic variation.

Defining Extreme Phenotypes of OSA Across International Sleep Centers.

BACKGROUND: Extreme phenotypes of OSA have not been systematically defined. RESEARCH QUESTION: This study developed objective definitions of extreme phenotypes of OSA by using a multivariate approach. The utility of these definitions for identifying characteristics that confer predisposition toward or protection against OSA is shown in a new prospective sample. STUDY DESIGN AND METHODS: In a large international sample, race-specific liability scores were calculated from a weighted logistic regression that included age, sex, and BMI. Extreme cases were defined as individuals with an apnea-hypopnea index (AHI) ≥ 30 events/hour but low likelihood of OSA based on age, sex, and BMI (liability scores > 90th percentile). Similarly, extreme controls were individuals with an AHI < 5 events/hour but high likelihood of OSA (liability scores < 10th percentile). Definitions were applied to a prospective sample from the Sleep Apnea Global Interdisciplinary Consortium, and differences in photography-based craniofacial and intraoral phenotypes were evaluated. RESULTS: This study included retrospective data from 81,338 individuals. A total of 4,168 extreme cases and 1,432 extreme controls were identified by using liability scores. Extreme cases were younger (43.1 ± 14.7 years), overweight (28.6 ± 6.8 kg/m2), and predominantly female (71.1%). Extreme controls were older (53.8 ± 14.1 years), obese (34.0 ± 8.1 kg/m2), and predominantly male (65.8%). These objective definitions identified 29 extreme cases and 87 extreme controls among 1,424 Sleep Apnea Global Interdisciplinary Consortium participants with photography-based phenotyping. Comparisons suggest that a greater cervicomental angle increases risk for OSA in the absence of clinical risk factors, and smaller facial widths are protective in the presence of clinical risk factors. INTERPRETATION: This objective definition can be applied in sleep centers throughout the world to consistently define OSA extreme phenotypes for future studies on genetic, anatomic, and physiologic pathways to OSA.

Age attenuates the transcriptional changes that occur with sleep in the medial prefrontal cortex.

Sleep abnormalities are common with aging. Studies show that sleep plays important roles in brain functions, and loss of sleep is associated with increased risks for neurological diseases. Here, we used RNA sequencing to explore effects of age on transcriptome changes between sleep and sleep deprivation (SD) in medial prefrontal cortex and found that transcriptional changes with sleep are attenuated in old. In particular, old mice showed a 30% reduction in the number of genes significantly altered between sleep/wake and, in general, had smaller magnitudes of changes in differentially expressed genes compared to young mice. Gene ontology analysis revealed differential age effects on certain pathways. Compared to young mice, many of the wake-active functions were similarly induced by SD in old mice, whereas many of the sleep-active pathways were attenuated in old mice. We found similar magnitude of changes in synaptic homeostasis genes (Fos, Arc, and Bdnf) induced by SD, suggesting intact synaptic upscaling on the transcript level during extended wakefulness with aging. However, sleep-activated processes, such as DNA repair, synaptogenesis, and axon guidance, were sensitive to the effect of aging. Old mice expressed elevated levels of immune response genes when compared to young mice, and enrichment analysis using cell-type-specific markers indicated upregulation of microglia and oligodendrocyte genes in old mice. Moreover, gene sets of the two cell types showed age-specific sleep/wake regulation. Ultimately, this study enhances understanding of the transcriptional changes with sleep and aging, providing potential molecular targets for future studies of age-related sleep abnormalities and neurological disorders.