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BackgroundEvaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA). MethodsUsing previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence-based review (EBR), or evidence-based review with recommendations (EBR-R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus. ResultsThe ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA-associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated. ConclusionThis review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence-based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.

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.

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep electroencephalogram (EEG) based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index (BAI) 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 β 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.

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.

As part one of the three chapters on sleep-disordered breathing, this chapter reviews obstructive sleep apnea (OSA) epidemiology, causes, and consequences. When comparing OSA prevalence between 1988 to 1994 and 2007 to 2010, we observe that OSA is rapidly on the rise, paralleling increasing rates in obesity. Global epidemiologic studies indicate that there are differences specific to ethnicity with Asians presenting with OSA at a lower body mass index than Caucasians. We have learned that structural and physiologic factors increase the risk of OSA and both can be influenced by genetics. Structural risk factors include craniofacial bony restriction, changes in fat distribution, and the size of the upper airway muscles. Physiologic risk factors include airway collapsibility, loop gain, pharyngeal muscle responsiveness, and arousal threshold. The consequences of OSA include daytime sleepiness and exacerbation of many underlying diseases. OSA has been associated with cardiovascular diseases including hypertension, coronary heart disease, stroke, atrial fibrillation, and other cardiac arrhythmias; pulmonary hypertension; metabolic disorders such as type 2 diabetes, hypothyroidism, acromegaly, Cushing syndrome, and polycystic ovarian syndrome; mild cognitive impairment or dementia; and cancer. This review contains 4 figures, 1 table and 48 references. Key Words: cardiac consequences, craniofacial bony restriction, epidemiology, fat distribution, metabolic disease, neurodegeneration, obesity, obstructive sleep apnea

As part 2 of three chapters on sleep disordered breathing, this chapter reviews obstructive sleep apnea (OSA) diagnosis and management. OSA should be considered in all patients who have loud habitual snoring, excessive daytime sleepiness, and witnessed apneas. On physical examination, craniofacial abnormalities that can lead to sleep apnea include retrognathia, micrognathia, a narrow hard palate, nasal obstruction, an overjet, and an overbite. Enlargement of the upper airway soft tissue structures (the tongue, soft palate, lateral walls, and parapharyngeal fat pads) also increases the risk of OSA. The gold standard for making the diagnosis of OSA is overnight polysomnography, but home sleep apnea tests (HSAT) are rapidly gaining acceptance, especially in patients with a high probability of OSA. The first line of therapy for OSA remains positive airway pressure (PAP), with the second line of therapy being oral appliances. Another alternative to PAP therapy is hypoglossal nerve stimulation, which has been shown to decrease the Apnea-Hypopnea index by 67.4%. This review contains 6 figures, 3 tables, and 52 references. Key Words: craniofacial abnormalities, Epworth Sleepiness Scale, home sleep apnea test, hypoglossal nerve stimulation, obstructive sleep apnea, oral appliances, oral pharyngeal crowding, polysomnography, positive airway pressure, STOP-BANG

As part 3 of the three chapters on sleep-disordered breathing, this chapter reviews central sleep apnea (CSA). CSA is defined as recurrent apneic events during sleep in the absence of respiratory muscle effort and loss of neuronal output to respiratory muscles. The most common cause of CSA is compromised cardiac function. In patients with CSA whose ejection fraction is less than or equal to 45%, adaptive servo-ventilation (ASV) has been shown to increase mortality. With the abuse of opioids reaching epidemic proportions, it has been estimated that 30 to 75% of patients on chronic opioid therapy have a significant increased incidence of CSA. Opioid-induced sleep-disordered breathing can be treated with ASV. This review contains 1 figure, and 37 references. Key Words: adaptive servo-ventilation, central sleep apnea, Cheyne-Stokes respiration, congenital central hypoventilation syndrome, impaired central ventilatory drive, opioid abuse, PHOX2B gene, supplemental oxygen

Study ObjectivesThis 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.MethodsWe 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.ResultsPhenotypes 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.ConclusionsA 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.

Recently, the Icelandic Sleep Apnea Cohort (ISAC) identified three subgroups in patients with obstructive sleep apnea (OSA) recruited from the sleep clinic based on clinical symptoms and comorbidities: excessively sleepy, minimally symptomatic, and disturbed sleep. This study sought to determine whether the three OSA subgroups are applicable to a population-based cohort in Korea.Study subjects are participants of an ongoing cohort study in Korea. Of the 2,918 participants, 422 new moderate to severe OSA cases (apneahypopnea index [AHI] ≥ 15 events/h) were diagnosed by home sleep studies. All participants completed a detailed questionnaire on sleep-related symptoms, comorbidities, medication, and other information. A latent class analysis was performed.When examining solutions for up to 10 clusters, theCombined with the ISAC data, we suggest that the three-symptom cluster solution for patients with OSA may be more widely applicable, irrespective of ethnicity and study population.

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.

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.

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

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/m(2)), and predominantly female (71.1%). Extreme controls were older (53.8 ± 14.1 years), obese (34.0 ± 8.1 kg/m(2)), 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.

Recent studies have shown an association between obstructive sleep apnea (OSA) and cognitive impairment. This study was done to investigate whether varied levels of cyclical intermittent hypoxia (CIH) differentially affect the microvasculature in the hippocampus, operating as a mechanistic link between OSA and cognitive impairment. We exposed C57BL/6 mice to sham [continuous air, arterial O2 saturation (SaO2 ) 97%], severe CIH to inspired O2 fraction (FiO2 ) = 0.10 (CIH10; SaO2 nadir of 61%), or very severe CIH to FiO2 = 0.05 (CIH5; SaO2 nadir of 37%) for 12 h/day for 2 wk. We quantified capillary length using neurostereology techniques in the dorsal hippocampus and utilized quantitative PCR methods to measure changes in sets of genes related to angiogenesis and to metabolism. Next, we employed immunohistochemistry semiquantification algorithms to quantitate GLUT1 protein on endothelial cells within hippocampal capillaries. Capillary length differed among CIH severity groups (P = 0.013) and demonstrated a linear relationship with CIH severity (P = 0.002). There was a strong association between CIH severity and changes in mRNA for VEGFA (P < 0.0001). Less strong, but nominally significant associations with CIH severity were also observed for ANGPT2 (PANOVA = 0.065, PTREND = 0.040), VEGFR2 (PANOVA = 0.032, PTREND = 0.429), and TIE-2 (PANOVA = 0.006, PTREND = 0.010). We found that the CIH5 group had increased GLUT1 protein relative to sham (P = 0.006) and CIH10 (P = 0.001). There was variation in GLUT1 protein along the microvasculature in different hippocampal subregions. An effect of CIH5 on GLUT1 mRNA was seen (PANOVA = 0.042, PTREND = 0.012). Thus CIH affects the microvasculature in the hippocampus, but consequences depend on CIH severity.

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.

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 (AHI(RLG) vs AHI(MANUAL)) and – 1.3 ± 2.6/h (AHI(NOX) vs AHI(MANUAL)). Using the transformed NP, the mean differences in AHI were – 1.9 ± 3.3/h (AHI(RLG) vs AHI(MANUAL)) and 1.6 ± 3.0/h (AHI(NOX) vs AHI(MANUAL)). Using the RIP flow, the mean differences in AHI were – 2.7 ± 4.5/h (AHI(RLG) vs AHI(MANUAL)) and 2.3 ± 3.4/h (AHI(NOX) VS AHI(MANUAL)). 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.

Background Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals. Objective In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA. Significance Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

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.

Sleep is associated with a variety of chronic diseases as well as most psychiatric, addiction and mood disorders. To analyze sleep patterns in rodents, researchers analyze polysomnogram data containing electroencephalographs (EEG) and electromyographs (EMG). However, the analysis is performed manually by a expert human scorer, which is a slow, time consuming, and expensive process that is also subject to known human error and inter-scorer inconsistency [1]. To address this, researchers have developed a variety of techniques to automatically classify rodent sleep states using features extracted from EEG and EMG signals [2]. In many approaches, researchers extract a variety of heuristic features from explicitly chosen spectral bands of the EEG and EMG signals [3]. However, human designed, heuristic features often do not capture complete salient sleep-state information, which leads to inferior classification performance.

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.

In this paper, we present an objective method for localization of proteins in blood brain barrier (BBB) vasculature using standard immunohistochemistry (IHC) techniques and bright-field microscopy. Images from the hippocampal region at the BBB are acquired using bright-field microscopy and subjected to our segmentation pipeline which is designed to automatically identify and segment microvessels containing the protein glucose transporter 1 (GLUT1). Gabor filtering and k-means clustering are employed to isolate potential vascular structures within cryosectioned slabs of the hippocampus, which are subsequently subjected to feature extraction followed by classification via decision forest. The false positive rate (FPR) of microvessel classification is characterized using synthetic and non-synthetic IHC image data for image entropies ranging between 3 and 8 bits. The average FPR for synthetic and non-synthetic IHC image data was found to be 5.48% and 5.04%, respectively.

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.

Mouse models of cyclical intermittent hypoxia (CIH) are used to study the consequences of both hypoxia and oxidative stress in obstructive sleep apnea (OSA). Whether or not a mouse model of CIH that simulates OSA patients' oxygenation characteristics would translate into improved patient care remains unanswered. First we identified oxygenation characteristics using the desaturation and resaturation time in 47 OSA subjects from the Molecular Signatures of Obstructive Sleep Apnea Cohort (MSOSA). We observe that a cycle of intermittent hypoxia is not sinusoidal; specifically, desaturation time increases in an almost linear relationship to the degree of hypoxia (nadir), whereas resaturation time is somewhat constant (∼15 s), irrespective of the nadir. Second, we modified the Hycon mouse model of CIH to accommodate a 15-s resaturation time. Using this modified CIH model, we explored whether a short resaturation schedule (15 s), which includes the characteristics of OSA patients, had a different effect on levels of oxidative stress (i.e., urinary 8,12- iso-iPF2α-VI levels) compared with sham and a long resaturation schedule (90 s), a schedule that is not uncommon in rodent models of CIH. Results suggest that shorter resaturation time may result in a higher level of 8,12- iso-iPF2α-VI compared with long resaturation or sham conditions. Therefore, simulating the rodent model of CIH to reflect this and other OSA patients' oxygenation characteristics may be worthy of consideration to better understand the effects of hypoxia, oxidative stress, and their interactions.

The aim of this study was to evaluate changes in interleukin-6 and soluble interleukin-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, referred for treatment and re-assessed at a 2-year follow-up. Full treatment was defined objectively as use ≥4 hours/day and ≥20 days/month. 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 interleukin-6 level change was found among full, partial and non-users. However, in severely obese patients (body mass index≥35), a significant increase in interleukin-6 levels over 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 interleukin-6 receptor levels between full users and non-users, after adjustment for confounders. In conclusion, among untreated obese sleep apnea patients, interleukin-6 levels increase substantially over 2 years, while adherence to positive airway pressure treatment may prevent further increases in this inflammatory biomarker.

Increasing data support a connection between obstructive sleep apnea (OSA) and cognitive impairment but a causal link has yet to be established. Although neuronal loss has been linked to cognitive impairment, emerging theories propose that changes in synaptic plasticity can cause cognitive impairment. Studies demonstrate that disruption to the blood-brain barrier (BBB), which is uniquely structured to tightly maintain homeostasis inside the brain, leads to changes in the brain's microenvironment and affects synaptic plasticity. Cyclical intermittent hypoxia is a stressor that could disrupt the BBB via molecular responses already known to occur in either OSA patients or animal models of intermittent hypoxia. However, we do not yet know if or how intermittent hypoxia can cause cognitive impairment by mechanisms operating at the BBB. Therefore, we propose that initially, adaptive homeostatic responses at the BBB occur in response to increased oxygen and nutrient demand, specifically through regulation of influx and efflux BBB transporters that alter microvessel permeability. We further hypothesize that although these responses are initially adaptive, these changes in BBB transporters can have long-term consequences that disrupt the brain's microenvironment and alter synaptic plasticity leading to cognitive impairment.

Purpose: To develop a simple and high throughput method for the molecular imaging of proteins at the blood-brain barrier using immunohistochemistry quantification. Methods: C57B6 male mice, age 4 months, were exposed to one of four different conditions for 12 hours/day × 2 weeks (a) Sham--exposure to constant air--e.g. 21% oxygen (b) cyclical intermittent hypoxia (CIH) from 21% to 12% (c) CIH from 21% to 10% and (d) CIH from 21% to 5%. Immunohistochemistry was then optimized for proteins GLUT1, p-glycoprotein and CD31 on blood vessels at the bloodbrain barrier. The image data sets of four different conditions were subjected to our image analysis algorithm. Background noise is eliminated using thresholding techniques and blood vessel candidates are delineated using methods based on connected component analysis. Subsequently, relevant features are extracted from the candidate set and are passed through our classification algorithm for capillary identification. We then run a reinforcement algorithm on a subset of images to improve identity classification. This is accomplished by calculating the false positive/negative rate of the subset and feeding these results back to the classification algorithm. We introduce the idea that the gold standard should be computer + human vision. Results: For GLUT1 we analyzed over 5000 images and identified over 400k blood vessels with a false positive rate of 4.1%. For p-glycoprotein we analyzed over 5000 images and identified over 400k blood vessels with a false positive rate of 5%. Conclusion: Our method provides quantitative measurements of proteins on blood vessels at the blood-brain barrier in an objective, reproducible and comparable way using standard immunohistochemistry techniques and bright-field microscopy.