Impact of sleep disordered breathing on carotid body size.
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ABSTRACT:
SUBMITTER: Welch BT
PROVIDER: S-EPMC5214089 | biostudies-literature | 2017 Feb
REPOSITORIES: biostudies-literature
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Impact of sleep disordered breathing on carotid body size.
Welch Brian T BT Petersen-Jones Humphrey G HG Eugene Andy R AR Brinjikji Waleed W Kallmes David F DF Curry Timothy B TB Joyner Michael J MJ Limberg Jacqueline K JK
Respiratory physiology & neurobiology 20161029
We tested the hypotheses that: (1) carotid body size can be measured by computed tomographic angiography (CTA) with high inter-observer agreement, and (2) patients with sleep apnea exhibit larger carotid bodies than those without sleep apnea. A chart review was conducted from patients who underwent neck CTA and polysomnography at the Mayo Clinic between January 2000 and February 2015. Widest axial measurements of the carotid bodies, performed independently by two radiologists, were possible in 8 ...[more]
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Project description:Academic success involves the ability to use cognitive skills in a school environment. Poor academic performance has been linked to disrupted sleep associated with sleep-disordered breathing (SDB). In parallel, poor sleep is associated with increased risk for obesity, and weight management problems have been linked to executive dysfunction, suggesting that interactions may be operational between SDB and obesity to adversely affect neurocognitive outcomes.To test whether mediator relationships exist between body weight, SDB, and cognition.Structural equation modeling was conducted on data from 351 children in a community-based cohort assessed with the core subtests of the Differential Abilities Scales after an overnight polysomnogram. Body mass index, apnea-hypopnea index, and cognitive abilities were modeled as latent constructs.In a sample of predominantly white children 6 to 10 years of age, SDB amplified the adverse cognitive and weight outcomes by 0.55- to 0.46-fold, respectively. Weight amplified the risk by 0.39- to 0.40-fold for SDB and cognitive outcomes, respectively. Poor ability to perform complex mental processing functions increased the risk of adverse weight and SDB outcomes by 2.9- and 7.9-fold, respectively.Cognitive functioning in children is adversely affected by frequent health-related problems, such as obesity and SDB. Furthermore, poorer integrative mental processing may place a child at a bigger risk for adverse health outcomes.
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Project description:RationaleObesity increases the risk and severity of sleep-disordered breathing. The degree to which excess body weight contributes to blood oxygen desaturation during hypopneic and apneic events has not been comprehensively characterized.ObjectivesTo quantify the association between excess body weight and oxygen desaturation during sleep-disordered breathing.MethodsA total of 750 adult participants in the Wisconsin Sleep Cohort Study were assessed for body mass index (BMI) (kg/m(2)) and sleep-disordered breathing. The amount of Sa(O(2)), duration, and other characteristics of 37,473 observed breathing events were measured during polysomnography studies. A mixed-effects linear regression model estimated the association of blood oxygen desaturation with participant-level characteristics, including BMI, gender, and age, and event-level characteristics, including baseline Sa(O(2)), change in Vt, event duration, sleep state, and body position.Measurements and main resultsBMI was positively associated with oxygen desaturation severity independent of age, gender, sleeping position, baseline Sa(O(2)), and event duration. BMI interacted with sleep state such that BMI predicted greater desaturation in rapid eye movement (REM) sleep than in non-REM sleep. Each increment of 10 kg/m(2) BMI predicted a 1.0% (SE, 0.2%) greater mean blood oxygen desaturation for persons in REM sleep experiencing hypopnea events associated with 80% Vt reductions.ConclusionsExcess body weight is an important predictor of the severity of blood oxygen desaturation during apnea and hypopnea events, potentially exacerbating the impact of sleep-disordered breathing in obese patients.
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Project description:IntroductionCystic fibrosis (CF) is a life-shortening, genetic disease that affects approximately 30,000 Americans. Although patients frequently report snoring, mouth breathing, and insomnia, the extent to which sleep-disordered breathing (SDB) may underlie these complaints remains unknown.MethodsSingle-center retrospective review of polysomnography results from referred patients with and without CF individually-matched (1:2) for age, gender, race, and body mass index (BMI).ResultsMean ages were 8.0 ± 5.2 (sd) and 35.9 ± 12.9 years, among 29 children and 23 adults with CF respectively. The CF and non-CF groups were well-matched in age and BMI. Subjects with vs. without CF had three times greater odds of moderate-severe SDB (apnea-hypopnea index (AHI) ≥ 5 in children, ≥ 15 in adults) (p = 0.01). Nocturnal oxygen saturation nadir (Minimum SpO2) was lower among CF vs. non-CF groups (p = 0.002). For every 1-unit increase in AHI, the decline in Minimum SpO2 was larger for subjects with vs. without CF (p = 0.05). In subjects with CF, forced expiratory volume in 1 s percent predicted (FEV1 PPD) was associated with Minimum SpO2 (Pearson r = 0.68, p < 0.0001) but not AHI (r = -0.19, p = 0.27). For every 1-unit increase in AHI, magnitude of decline in Minimum SpO2 was larger for those with low vs. normal FEV1 PPD (p = 0.01).ConclusionSeverity of SDB may be worse among referred patients with vs. without CF. The SDB may modify the relationship between CF lung disease and nocturnal hypoxemia. Markers of lung disease severity including lung function do not predict SDB severity, suggesting the need for routine polysomnography to screen for this sleep disorder.
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Project description:ObjectiveTo examine the association between sleep-disordered breathing and stroke outcomes, and determine the contribution of sleep-disordered breathing to outcome disparities in Mexican Americans.MethodsIschemic stroke patients (n = 995), identified from the population-based Brain Attack Surveillance in Corpus Christi Project (2010-2015), were offered participation in a sleep-disordered breathing study including a home sleep apnea test (ApneaLink Plus). Sleep-disordered breathing (respiratory event index ≥10) was determined soon after stroke. Neurologic, functional, cognitive, and quality of life outcomes were assessed at 90 days poststroke. Regression models were used to assess associations between sleep-disordered breathing and outcomes, adjusted for sociodemographics, prestroke function and cognition, health-risk behaviors, stroke severity, and vascular risk factors.ResultsMedian age was 67 years (interquartile range [IQR] = 59-78); 62.1% were Mexican American. Median respiratory event index was 14 (IQR = 6-25); 62.8% had sleep-disordered breathing. Sleep-disordered breathing was associated with worse functional outcome (mean difference in activities of daily living/instrumental activities of daily living score = 0.15, 95% confidence interval [CI] = 0.01-0.28) and cognitive outcome (mean difference in modified Mini-Mental State Examination = -2.66, 95% CI = -4.85 to -0.47) but not neurologic or quality of life outcomes. Sleep-disordered breathing accounted for 9 to 10% of ethnic differences in functional and cognitive outcome and was associated with cognitive outcome more strongly for Mexican Americans (β = -3.97, 95% CI = -6.63 to -1.31) than non-Hispanic whites (β = -0.40, 95% CI = -4.18 to 3.39, p-interaction = 0.15).InterpretationSleep-disordered breathing is associated with worse functional and cognitive function at 90 days poststroke. These outcomes are reasonable endpoints for future trials of sleep-disordered breathing treatment in stroke. If effective, sleep-disordered breathing treatment may somewhat lessen ethnic stroke outcome disparities. ANN NEUROL 2019;86:241-250.
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Project description:Sleep-disordered breathing (SDB) comprises a diverse set of disorders marked by abnormal respiration during sleep. Clinicians should realize that SDB may present as acute cardiopulmonary failure in susceptible patients. In this review, we discuss three clinical phenotypes of acute cardiopulmonary failure from SDB: acute ventilatory failure, acute congestive heart failure, and sudden death. We review the pathophysiologic mechanisms and recommend general principles for management. Timely recognition of, and therapy for, SDB in the setting of acute cardiopulmonary failure may improve short- and long-term outcomes.
| S-EPMC3296458 | biostudies-other
Project description:Objective/backgroundSleep-disordered breathing (SDB) is highly prevalent after stroke and is associated with poor outcomes. Currently, after stroke, objective testing must be used to differentiate patients with and without SDB. Within a large, population-based study, we evaluated the usefulness of a flexible statistical model based on baseline characteristics to predict post-stroke SDB.Patients/methodsWithin a population-based study, participants (2010-2018) underwent SDB screening, shortly after ischemic stroke, with a home sleep apnea test. The respiratory event index (REI) was calculated as the number of apneas and hypopneas per hour of recording; values ≥10 defined SDB. The distributed random forest classifier (a machine learning technique) was applied to predict SDB with the following as predictors: demographics, stroke risk factors, stroke severity (NIHSS), neck and waist circumference, palate position, and pre-stroke symptoms of snoring, apneas, and sleepiness.ResultsWithin the total sample (n = 1330), median age was 65 years; 47% were women; 32% non-Hispanic white, 62% Mexican American, and 6% African American. SDB was found in 891 (67%). The area under the receiver operating characteristic curve, a measure of predictive ability, applied to the validation sample was 0.75 for the random forest model. Random forest correctly classified 72.5% of validation samples.ConclusionsIn this large, ethnically diverse, population-based sample of ischemic stroke patients, prediction models based on baseline characteristics and clinical measures showed fair rather than clinically reliable performance, even with use of advanced machine learning techniques. Results suggest that objective tests are still needed to differentiate ischemic stroke patients with and without SDB.
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