Correction: Nonlinear Dynamics Forecasting of Obstructive Sleep Apnea Onsets.
Ontology highlight
ABSTRACT:
SUBMITTER: Le TQ
PROVIDER: S-EPMC5552328 | biostudies-other | 2017
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Recent advances in sensor technologies and predictive analytics are fueling the growth in point-of-care (POC) therapies for obstructive sleep apnea (OSA) and other sleep disorders. The effectiveness of POC therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes. This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. A prognosis method based on a nonparametric statistical Dirichlet-Process Mixture-Gaussian-Process (DPMG) model to estimate the transition from normal states to an anomalous (apnea) state is utilized to estimate the remaining time until the onset of an impending OSA episode. The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of 10 OSA patients from the University of Dublin OSA database and (3) records of eight subjects from previous work. Validation tests suggest that the model can be used to track the time until the onset of an OSA episode with the likelihood of correctly predicting apnea onset in 1 min to 5 mins ahead is 83.6 ± 9.3%, 80 ± 8.1%, 76.2 ± 13.3%, 66.9 ± 15.4%, and 61.1 ± 16.7%, respectively. The present prognosis approach can be integrated with wearable devices, enhancing proactive treatment of OSA and real-time wearable sensor-based of sleep disorders.
| S-EPMC5105938 | biostudies-literature
Project description:IntroductionEnhanced characterization of sleep architecture, compared with routine polysomnographic metrics such as stage percentages and sleep efficiency, may improve the predictive phenotyping of fragmented sleep. One approach involves using stage transition analysis to characterize sleep continuity.Methods and principal findingsWe analyzed hypnograms from Sleep Heart Health Study (SHHS) participants using the following stage designations: wake after sleep onset (WASO), non-rapid eye movement (NREM) sleep, and REM sleep. We show that individual patient hypnograms contain insufficient number of bouts to adequately describe the transition kinetics, necessitating pooling of data. We compared a control group of individuals free of medications, obstructive sleep apnea (OSA), medical co-morbidities, or sleepiness (n = 374) with mild (n = 496) or severe OSA (n = 338). WASO, REM sleep, and NREM sleep bout durations exhibited multi-exponential temporal dynamics. The presence of OSA accelerated the "decay" rate of NREM and REM sleep bouts, resulting in instability manifesting as shorter bouts and increased number of stage transitions. For WASO bouts, previously attributed to a power law process, a multi-exponential decay described the data well. Simulations demonstrated that a multi-exponential process can mimic a power law distribution.Conclusion and significanceOSA alters sleep architecture dynamics by decreasing the temporal stability of NREM and REM sleep bouts. Multi-exponential fitting is superior to routine mono-exponential fitting, and may thus provide improved predictive metrics of sleep continuity. However, because a single night of sleep contains insufficient transitions to characterize these dynamics, extended monitoring of sleep, probably at home, would be necessary for individualized clinical application.
| S-EPMC2893208 | biostudies-literature
Project description:Rationale: Obstructive sleep apnea (OSA) has been associated with metabolic dysregulation and systemic inflammation. This may be due to pathophysiologic effects of OSA on visceral adipose tissue. We sought to assess the transcriptional consequences of OSA on adipocytes by utilizing pathway-focused analyses. Methods: Patients scheduled to undergo ventral hernia repair surgery were recruited to wear a portable home sleep monitor for two nights prior to surgery. Visceral fat biopsies were obtained intra-operatively. RNA was extracted and whole-genome expression profiling was performed. Gene Set Enrichment Analysis (GSEA) was used to identify curated gene sets that were differentially enriched in OSA subjects. Network analysis was applied to a select set of highly enriched pathways. Results: 10 patients with OSA and 8 control subjects were recruited. There were no differences in age, gender, body mass index between the two groups, but the OSA subjects had a significantly higher respiratory disturbance index (19.2 vs. 0.6, P-value 0.05) and worse hypoxemia (minimum oxygen saturation 79.7% vs. 87.8%, P-value < 0.001). GSEA identified a number of gene sets up-regulated in adipose tissue of OSA patients including the pro-inflammatory NF-M-NM-:B pathway and the proteolytic ubiquitin/proteasome module. A critical metabolic pathway, the peroxisome proliferator-activated receptor (PPAR), was down-regulated in subjects with OSA. Network analysis linked members of these modules together and identified regulatory hubs. Conclusions: OSA is associated with alterations in visceral fat gene expression. Pathway-based network analysis highlighted perturbations in several key pathways whose coordinated interactions may contribute to the metabolic dysregulation observed in this complex disorder. Total RNA from visceral fat of 18 subjects (10 OSA, 8 Control) was hybridized to 18 Affymetrix Genechip Human Gene 1.0 ST microarrays.
2012-09-01 | E-GEOD-38792 | biostudies-arrayexpress
Project description:Rationale: Obstructive sleep apnea (OSA) has been associated with metabolic dysregulation and systemic inflammation. This may be due to pathophysiologic effects of OSA on visceral adipose tissue. We sought to assess the transcriptional consequences of OSA on adipocytes by utilizing pathway-focused analyses. Methods: Patients scheduled to undergo ventral hernia repair surgery were recruited to wear a portable home sleep monitor for two nights prior to surgery. Visceral fat biopsies were obtained intra-operatively. RNA was extracted and whole-genome expression profiling was performed. Gene Set Enrichment Analysis (GSEA) was used to identify curated gene sets that were differentially enriched in OSA subjects. Network analysis was applied to a select set of highly enriched pathways. Results: 10 patients with OSA and 8 control subjects were recruited. There were no differences in age, gender, body mass index between the two groups, but the OSA subjects had a significantly higher respiratory disturbance index (19.2 vs. 0.6, P-value 0.05) and worse hypoxemia (minimum oxygen saturation 79.7% vs. 87.8%, P-value < 0.001). GSEA identified a number of gene sets up-regulated in adipose tissue of OSA patients including the pro-inflammatory NF-κB pathway and the proteolytic ubiquitin/proteasome module. A critical metabolic pathway, the peroxisome proliferator-activated receptor (PPAR), was down-regulated in subjects with OSA. Network analysis linked members of these modules together and identified regulatory hubs. Conclusions: OSA is associated with alterations in visceral fat gene expression. Pathway-based network analysis highlighted perturbations in several key pathways whose coordinated interactions may contribute to the metabolic dysregulation observed in this complex disorder.
2012-09-01 | GSE38792 | GEO
Project description:Obstructive sleep apnea (OSA) severity might be correlated to the flow characteristics of the upper airways. We aimed to investigate the severity of OSA based on 3D models constructed from CT scans coupled with computational fluid dynamics (CFD) simulations. The CT scans of seven adult patients diagnosed with OSA were used to reconstruct the 3D models of the upper airways and CFD modeling and analyses were performed. Results from the fluid simulations were compared with the apnea-hypopnea index. Here we show a correlation between a CFD-based parameter, the adjusted pressure coefficient (Cp*), and the respective apnea-hypopnea index (Pearson's r = 0.91, p = 0.004), which suggests that the anatomical-based model coupled with CFD could provide functional and localized information for different regions of the upper airways.
| S-EPMC6872714 | biostudies-literature
Project description:In obese subjects with obstructive sleep apnea (OSA), chronic intermittent hypoxia (CIH) may be linked to systemic and adipose tissue inflammation.We obtained abdominal subcutaneous adipose tissue biopsies from OSA and non-OSA obese (BMI > 35) subjects at baseline and after 24 weeks (T1) of weight-loss intervention plus continuous positive airway pressure (c-PAP) or weight-loss intervention alone, respectively. OSA subjects were grouped according to good (therapeutic) or poor (subtherapeutic) adherence to c-PAP.At baseline, anthropometric and metabolic parameters, serum cytokines, and adipose tissue mRNA levels of obesity-associated chemokines and inflammatory markers were not different in OSA and non-OSA subjects. At T1, body weight was significantly reduced in all groups. Serum concentrations of IL-2, IL-4, IL-6, MCP-1, PDGF?, and VEGF? were reduced by therapeutic c-PAP in OSA subjects and remained unaltered in non-OSA and subtherapeutic c-PAP groups. Similarly, adipose tissue mRNA levels of macrophage-specific (CD68, CD36) and ER stress (ATF4, CHOP, ERO-1) gene markers, as well as of IL-6, PDGF?, and VEGF?, were decreased only in the therapeutic c-PAP group.CIH does not represent an additional factor increasing systemic and adipose tissue inflammation in morbid obesity. However, in subjects with OSA, an effective c-PAP therapy improves systemic and obesity-associated inflammatory markers.Ministero dell'Università e della Ricerca and Progetti di Rilevante Interesse Nazionale.
| S-EPMC5621882 | biostudies-other
Project description:Illumina MiSeq next generation sequencing chip was used to identify differentially expressed miRs by comparing peripheral blood mononuclear cell samples between OSA patients and healthy non-snorers.
2021-12-01 | GSE174245 | GEO
Project description:INTRODUCTION:Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. MATERIALS AND METHODS:Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. RESULTS:A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. DISCUSSION:We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. CONCLUSIONS:Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. TRIAL REGISTRATION:ClinicalTrials.gov NCT01161381.
| S-EPMC4777493 | biostudies-literature
Project description:Study objectivesThe aim of the study was to investigate the effect of ambient temperature on sleep, sleep apnea, and morning alertness in patients with obstructive sleep apnea.DesignRandomized controlled trial.SettingIn-hospital investigations.ParticipantsForty patients with obstructive sleep apnea naïve to treatment, with an apnea-hypopnea index of 10-30.InterventionsThree different nights in room temperatures of 16°C, 20°C, and 24°C.MeasurementsOvernight polysomnography and Karolinska Sleepiness Scale.ResultsThe obstructive apnea-hypopnea index was 30 ± 17 at 16°C room temperature, 28 ± 17 at 20°C, and 24 ± 18 at 24°C. The obstructive apnea-hypopnea index was higher at 16°C room temperature versus 24°C (P = 0.001) and at 20°C room temperature versus 24°C (P = 0.033). Total sleep time was a mean of 30 min longer (P = 0.009), mean sleep efficiency was higher (77 ± 11% versus 71 ± 13% respectively, P = 0.012), and the patients were significantly more alert according to the Karolinska Sleepiness Scale (P < 0.028) in the morning at 16°C room temperature versus 24°C. The amount of sleep in different sleep stages was not affected by room temperature.ConclusionsUntreated patients with obstructive sleep apnea sleep longer, have better sleep efficiency, and are more alert in the morning after a night's sleep at 16°C room temperature compared with 24°C, but obstructive sleep apnea is more severe at 16°C and 20°C compared with 24°C.Clinical trial informationThis study is registered in ClinicalTrials.gov number NCT00544752.
| S-EPMC3296793 | biostudies-literature
Project description:Obstructive sleep apnea (OSA) has been linked to dysregulated metabolic states and treatment of sleep apnea may improve these conditions. Subcutaneous adipose tissue is a readily samplable fat depot that plays an important role in regulating metabolism. However, neither the pathophysiologic consequences of OSA nor the effects of continuous positive airway pressure (CPAP) in altering this compartment’s molecular pathways are understood. This study aimed to systematically identify subcutaneous adipose tissue transcriptional programs modulated in OSA and in response to its effective treatment with CPAP. Two subject groups were investigated: Study Group 1 was comprised of 10 OSA and 8 controls; Study Group 2 included 24 individuals with OSA studied at baseline and following CPAP. For each subject, genome-wide gene expression measurement of subcutaneous fat was performed. Differentially activated pathways elicited by OSA (Group 1) and in response to its treatment (Group 2) were determined using network and Gene Set Enrichment Analysis (GSEA). In Group 2, treatment of OSA with CPAP improved apnea hypopnea index, daytime sleepiness, and blood pressure, but not anthropometric measures. In Group 1, GSEA revealed many up-regulated gene sets in OSA subjects, most of which were involved in immuno-inflammatory (e.g., interferon-γ signaling), transcription, and metabolic processes such as adipogenesis. Unexpectedly, CPAP therapy in Group 2 subjects was also associated with up-regulation of several immune pathways as well as cholesterol biosynthesis. Collectively, our findings demonstrate that OSA alters distinct inflammatory and metabolic programs in subcutaneous fat, but these transcriptional signatures are not reversed with short-term effective therapy.
2019-12-21 | GSE135917 | GEO