Sleep-Disordered Breathing and Free Fatty Acid Metabolism.
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ABSTRACT: Background
Sleep-disordered breathing (SDB) is independently associated with insulin resistance, glucose intolerance, and type 2 diabetes mellitus. However, data on whether SDB alters the metabolism of free fatty acids (FFAs) are lacking.Research question
The primary objective of the current study was to characterize alterations in FFA metabolism across the spectrum of SDB severity.Study design and methods
The study sample included 118 participants with and without SDB who underwent full-montage polysomnography, the frequently sampled IV glucose tolerance test (FSIGTT), and body composition measurements including determination of percent body fat. Parameters of lipolysis suppression, time to FFA nadir, and FFA rebound after an IV glucose challenge were derived using a mathematical model. Multivariable regression analyses were used to characterize the independent associations between SDB severity and parameters of FFA metabolism.Results
SDB severity, as assessed by the apnea-hypopnea index, was associated with adipocyte insulin resistance, a decrease in the glucose- and insulin-mediated suppression of lipolysis, a longer duration to reach a nadir in FFA levels during the FSIGTT, and a sluggish rebound in FFA levels after suppression. Severity of SDB-related hypoxemia was independently associated with adipocyte insulin resistance and the time to reach the FFA nadir during the FSIGTT. Finally, a higher percentage of stage N3 sleep was positively associated with greater suppression of lipolysis and a faster rebound in the FFA levels during the FSIGTT.Interpretation
Independent of adiposity, SDB is associated with impairments in FFA metabolism, which may contribute to the development of glucose intolerance and type 2 diabetes in SDB.
SUBMITTER: Stefanovski D
PROVIDER: S-EPMC7674991 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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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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Project description:BACKGROUND:Sleep-disordered breathing (SDB) is common in pregnancy, but there are limited data on predictors. OBJECTIVES:The objective of this study was to develop predictive models of sleep-disordered breathing during pregnancy. STUDY DESIGN:Nulliparous women completed validated questionnaires to assess for symptoms related to snoring, fatigue, excessive daytime sleepiness, insomnia, and restless leg syndrome. The questionnaires included questions regarding the timing of sleep and sleep duration, work schedules (eg, shift work, night work), sleep positions, and previously diagnosed sleep disorders. Frequent snoring was defined as self-reported snoring ≥3 days per week. Participants underwent in-home portable sleep studies for sleep-disordered breathing assessment in early (6-15 weeks gestation) and mid pregnancy (22-31 weeks gestation). Sleep-disordered breathing was characterized by an apnea hypopnea index that included all apneas, plus hypopneas with ≥3% oxygen desaturation. For primary analyses, an apnea hypopnea index ≥5 events per hour was used to define sleep-disordered breathing. Odds ratios and 95% confidence intervals were calculated for predictor variables. Predictive ability of the logistic models was estimated with area under the receiver-operating-characteristic curves, along with sensitivities, specificities, and positive and negative predictive values and likelihood ratios. RESULTS:Among 3705 women who were enrolled, data were available for 3264 and 2512 women in early and mid pregnancy, respectively. The corresponding prevalence of sleep-disordered breathing was 3.6% and 8.3%, respectively. At each time point in gestation, frequent snoring, chronic hypertension, greater maternal age, body mass index, neck circumference, and systolic blood pressure were associated most strongly with an increased risk of sleep-disordered breathing. Logistic regression models that included current age, body mass index, and frequent snoring predicted sleep-disordered breathing in early pregnancy, sleep-disordered breathing in mid pregnancy, and new onset sleep-disordered breathing in mid pregnancy with 10-fold cross-validated area under the receiver-operating-characteristic curves of 0.870, 0.838, and 0.809. We provide a supplement with expanded tables, integrated predictiveness, classification curves, and an predicted probability calculator. CONCLUSION:Among nulliparous pregnant women, logistic regression models with just 3 variables (ie, age, body mass index, and frequent snoring) achieved good prediction of prevalent and incident sleep-disordered breathing. These results can help with screening for sleep-disordered breathing in the clinical setting and for future clinical treatment trials.
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