Intermittent Hypoxia-Induced Cognitive Deficits Are Mediated by NADPH Oxidase Activity in a Murine Model of Sleep Apnea

Background: In rodents, exposure to intermittent hypoxia (IH), a hallmark of obstructive sleep apnea (OSA), is associated with neurobehavioral impairments, increased apoptosis in the hippocampus and cortex, as well as increased oxidant stress and inflammation. Excessive NADPH oxidase activity may play a role in IH-induced CNS dysfunction. Methods and Findings: The effect of IH during light period on two forms of spatial learning in the water maze and well as markers of oxidative stress was assessed in mice lacking NADPH oxidase activity (gp91phox _/Y) and wild-type littermates. On a standard place training task, gp91phox _/Y displayed normal learning, and were protected from the spatial learning deficits observed in wild-type littermates exposed to IH. Moreover, anxiety levels were increased in wild-type mice exposed to IH as compared to room air (RA) controls, while no changes emerged in gp91phox _/Y mice. Additionally, wild-type mice, but not gp91phox _/Y mice had significantly elevated levels of NADPH oxidase expression and activity, as well as MDA and 8-OHDG in cortical and hippocampal lysates following IH exposures. Conclusions: The oxidative stress responses and neurobehavioral impairments induced by IH during sleep are mediated, at least in part, by excessive NADPH oxidase activity, and thus pharmacological agents targeting NADPH oxidase may provid

Changes in oxygen partial pressure of brain tissue in an animal model of obstructive apnea

Background: Cognitive impairment is one of the main consequences of obstructive sleep apnea (OSA) and is usually attributed in part to the oxidative stress caused by intermittent hypoxia in cerebral tissues. The presence of oxygen-reactive species in the brain tissue should be produced by the deoxygenation-reoxygenation cycles which occur at tissue level during recurrent apneic events. However, how changes in arterial blood oxygen saturation (SpO2) during repetitive apneas translate into oxygen partial pressure (PtO2) in brain tissue has not been studied. The objective of this study was to assess whether brain tissue is partially protected from intermittently occurring interruption of O2 supply during recurrent swings in arterial SpO2 in an animal model of OSA. Methods: Twenty-four male Sprague-Dawley rats (300-350 g) were used. Sixteen rats were anesthetized and noninvasively subjected to recurrent obstructive apneas: 60 apneas/h, 15 s each, for 1 h. A control group of 8 rats was instrumented but not subjected to obstructive apneas. PtO2 in the cerebral cortex was measured using a fastresponse oxygen microelectrode. SpO2 was measured by pulse oximetry. The time dependence of arterial SpO2 and brain tissue PtO2 was carried out by Friedman repeated measures ANOVA. Results: Arterial SpO2 showed a stable periodic pattern (no significant changes in maximum [95.5 ± 0.5%; m ± SE] and minimum values [83.9 ± 1.3%]). By contrast, brain tissue PtO2 exhibited a different pattern from that of arterial SpO2. The minimum cerebral cortex PtO2 computed during the first apnea (29.6 ± 2.4 mmHg) was significantly lower than baseline PtO2 (39.7 ± 2.9 mmHg; p = 0.011). In contrast to SpO2, the minimum and maximum values of PtO2 gradually increased (p < 0.001) over the course of the 60 min studied. After 60 min, the maximum (51.9 ± 3.9 mmHg) and minimum (43.7 ± 3.8 mmHg) values of PtO2 were significantly greater relative to baseline and the first apnea dip, respectively. Conclusions: These data suggest that the cerebral cortex is partially protected from intermittently occurring interruption of O2 supply induced by obstructive apneas mimicking OSA

A Model Analysis of Arterial Oxygen Desaturation during Apnea in Preterm Infants

Rapid arterial O2 desaturation during apnea in the preterm infant has obvious clinical implications but to date no adequate explanation for why it exists. Understanding the factors influencing the rate of arterial O2 desaturation during apnea () is complicated by the non-linear O2 dissociation curve, falling pulmonary O2 uptake, and by the fact that O2 desaturation is biphasic, exhibiting a rapid phase (stage 1) followed by a slower phase when severe desaturation develops (stage 2). Using a mathematical model incorporating pulmonary uptake dynamics, we found that elevated metabolic O2 consumption accelerates throughout the entire desaturation process. By contrast, the remaining factors have a restricted temporal influence: low pre-apneic alveolar causes an early onset of desaturation, but thereafter has little impact; reduced lung volume, hemoglobin content or cardiac output, accelerates during stage 1, and finally, total blood O2 capacity (blood volume and hemoglobin content) alone determines during stage 2. Preterm infants with elevated metabolic rate, respiratory depression, low lung volume, impaired cardiac reserve, anemia, or hypovolemia, are at risk for rapid and profound apneic hypoxemia. Our insights provide a basic physiological framework that may guide clinical interpretation and design of interventions for preventing sudden apneic hypoxemia

Avaliação de um modelo de predição para apneia do sono em pacientes submetidos a polissonografia Evaluation of a prediction model for sleep apnea in patients submitted to polysomnography

OBJETIVO: Testar um modelo de predição para apneia do sono a partir de variáveis sociodemográficas e clínicas em uma população com suspeita de distúrbio do sono e submetida à polissonografia. MÉTODOS: Foram incluídos no estudo 323 pacientes consecutivos submetidos à polissonografia por suspeita clínica de distúrbio do sono. Utilizou-se um questionário com questões sociodemográficas e a escala de sonolência de Epworth. Foram medidos pressão arterial, peso, altura e SpO2. A regressão linear múltipla, tendo o índice de apneia-hipopneia (IAH) como variável dependente, foi utilizada para construir um modelo de predição de apneia do sono. A regressão logística multinomial foi realizada para verificar fatores associados de forma independente à gravidade da apneia (leve, moderada ou grave) em comparação à ausência de apneia. RESULTADOS: A prevalência de apneia do sono na população de estudo foi de 71,2%, e foi mais prevalente nos homens que nas mulheres (81,2% vs. 56,8%; p < 0,001). O modelo de regressão linear múltipla, com o log IAH como variável dependente, foi composto pelas seguintes variáveis independentes: circunferência do pescoço, apneia testemunhada, idade, IMC e presença de rinite alérgica. O melhor modelo de regressão linear encontrado conseguiu explicar 39% da variabilidade do IAH. Na regressão logística multinomial, a apneia leve esteve associada com IMC e circunferência do pescoço, e a apneia grave associou-se com idade, IMC, circunferência do pescoço e apneia testemunhada. CONCLUSÕES: Modelos de predição clínica para apneia do sono não substituem a polissonografia como ferramenta para o seu diagnóstico, mas podem otimizar sua indicação e aumentar a chance de positividade do exame.<br>OBJECTIVE: To test a prediction model for sleep apnea based on clinical and sociodemographic variables in a population suspected of having sleep disorders and submitted to polysomnography. METHODS: We included 323 consecutive patients submitted to polysomnography because of the clinical suspicion of having sleep disorders. We used a questionnaire with sociodemographic questions and the Epworth sleepiness scale. Blood pressure, weight, height, and SpO2 were measured. Multiple linear regression was used in order to create a prediction model for sleep apnea, the apnea-hypopnea index (AHI) being the dependent variable. Multinomial logistic regression was used in order to identify factors independently associated with the severity of apnea (mild, moderate, or severe) in comparison with the absence of apnea. RESULTS: The prevalence of sleep apnea in the study population was 71.2%. Sleep apnea was more prevalent in men than in women (81.2% vs. 56.8%; p < 0.001). The multiple linear regression model, using log AHI as the dependent variable, was composed of the following independent variables: neck circumference, witnessed apnea, age, BMI, and allergic rhinitis. The best-fit linear regression model explained 39% of the AHI variation. In the multinomial logistic regression, mild apnea was associated with BMI and neck circumference, whereas severe apnea was associated with age, BMI, neck circumference, and witnessed apnea. CONCLUSIONS: Although the use of clinical prediction models for sleep apnea does not replace polysomnography as a tool for its diagnosis, they can optimize the process of deciding when polysomnography is indicated and increase the chance of obtaining positive polysomnography findings