Mandibular Advancement vs CPAP for Blood Pressure Reduction in Patients With Obstructive Sleep Apnea.

BACKGROUND: Hypertension guidelines recommend diagnosis and treatment of obstructive sleep apnea (OSA) in patients with hypertension. The mandibular advancement device (MAD) is an oral appliance therapy for patients who decline or cannot tolerate continuous positive airway pressure (CPAP). OBJECTIVES: We compared the relative effectiveness of MAD vs CPAP in reducing 24-hour ambulatory blood pressure (BP). METHODS: In an investigator-initiated, randomized, noninferiority trial (prespecified margin 1.5 mm Hg), 321 participants aged ≥40 years with hypertension and increased cardiovascular risk were recruited at 3 public hospitals for polysomnography. Of these, 220 participants with moderate-to-severe OSA (apnea-hypopnea index ≥15 events per hour) were randomized to either MAD or CPAP (1:1). The primary outcome was the difference between the 24-hour mean arterial BP at baseline and 6 months. RESULTS: Compared with baseline, the 24-hour mean arterial BP decreased by 2.5 mm Hg (P = 0.003) at 6 months in the MAD group, whereas no change was observed in the CPAP group (P = 0.374). The between-group difference was -1.6 mm Hg (95% CI: -3.51 to 0.24, noninferiority P < 0.001). The MAD group demonstrated a larger between-group reduction in all secondary ambulatory BP parameters compared with the CPAP group, with the most pronounced effects observed in the asleep BP parameters. Both the MAD and CPAP improved daytime sleepiness, with the between-group difference similar (P = 0.384). There were no between-group differences in cardiovascular biomarkers. CONCLUSIONS: MAD is noninferior to CPAP for reducing 24-hour mean arterial BP in participants with hypertension and increased cardiovascular risk. (Cardiosleep Research Program on Obstructive Sleep Apnea, Blood Pressure Control and Maladaptive Myocardial Remodeling-Non-inferiority Trial [CRESCENT]; NCT04119999).

Obstructive sleep apnoea and nocturnal atrial fibrillation in patients with ischaemic heart disease.

INTRODUCTION: Arrhythmias, especially atrial fibrillation (AF) and ventricular arrhythmias, are independent risk factors of mortality in patients with ischaemic heart disease (IHD). While there is a growing body of evidence that suggests an association between obstructive sleep apnoea (OSA) and cardiac arrhythmias, evidence on this relationship in patients with IHD has been scant and inconsistent. We hypothesised that in patients with IHD, severe OSA is associated with an increased risk of nocturnal arrhythmias. METHODS: We studied 103 consecutive patients with IHD who underwent an overnight polysomnography. Exposed subjects were defined as patients who had an apnoea-hypopnoea index (AHI) ≥30/h (severe OSA), and nonexposed subjects were defined as patients who had an AHI <30/h (nonsevere OSA). All electrocardiograms (ECGs) were interpreted by the Somte ECG analysis software and confirmed by a physician blinded to the presence or absence of exposure. Arrhythmias were categorised as supraventricular and ventricular. Arrhythmia subtypes (ventricular, atrial and conduction delay) were analysed as dichotomous outcomes using multiple logistic regression models. RESULTS: Atrial fibrillation and AF/flutter (odds ratio 13.5, 95% confidence interval 1.66-109.83; P = 0.003) were found to be more common in the severe OSA group than in the nonsevere OSA group. This association remained significant after adjustment for potential confounders. There was no significant difference in the prevalence of ventricular and conduction delay arrhythmias between the two groups. CONCLUSION: In patients with IHD, there was a significant association between severe OSA and nocturnal AF/flutter. This underscores the need to evaluate for OSA in patients with IHD, as it may have important implications on clinical outcomes.

USNAP: fast unique dense region detection and its application to lung cancer.

MOTIVATION: Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains. RESULTS: There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph. AVAILABILITY AND IMPLEMENTATION: The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22.

The Belun sleep platform to diagnose obstructive sleep apnea in patients with hypertension and high cardiovascular risk.

STUDY OBJECTIVE: Current hypertension guidelines recommend that at-risk individuals be screened for obstructive sleep apnea (OSA). The Belun Ring is a wearable OSA diagnostic device worn on the palmar side of the proximal phalanx of the index finger. METHODS: We recruited 129 participants (age: 60 ±â€Š8 years, male sex: 88%, BMI: 27 ±â€Š4 kg/m 2 ) with hypertension and high cardiovascular risk for a simultaneous polysomnography and Belun Ring monitoring for one night. Epworth Sleepiness Scale score more than 10 was detected in 27 (21.0%) participants. RESULTS: In the 127 participants who completed the study, the apnea-hypopnea index (AHI) derived from polysomnography was 18.1 (interquartile range: 33.0) events/h and that derived from the Belun Ring was 19.5 (interquartile range: 23.3) events/h [intraclass correlation coefficient: 0.882, 95% confidence interval (95% CI): 0.837-0.916]. A Bland-Altman plot showed the difference between the Belun Ring and polysomnography AHIs to be -1.3 ±â€Š10.4 events/h. Area under the receiver operating characteristic for the Belun Ring AHI was 0.961 (95% CI: 0.932-0.990, P  < 0.001). When the Belun Ring AHI of at least 15 events/h was used to diagnose OSA, the sensitivity, specificity, positive predictive value, and negative predictive value were 95.7, 77.6, 85.3, and 93.8%, respectively. The overall accuracy was 87.4%. The Cohen's kappa agreement was 0.74 ±â€Š0.09 ( P  < 0.001). Similar results were obtained when the oxygen desaturation index was used to diagnose OSA. CONCLUSION: A high prevalence of OSA was detected in patients with hypertension and high cardiovascular risk. The Belun Ring is a reliable device for OSA diagnosis similar to polysomnography.

Hour-to-hour variability of respiratory sleep indices and ambulatory blood pressure.

STUDY OBJECTIVES: Respiratory sleep indices are traditionally reported on the basis of the average total sleep time. The relationship between the hour-to-hour variability of these parameters and blood pressure (BP) has not been reported. METHODS: We evaluated the associations of the hour-to-hour variability of the apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and lowest oxygen saturation with the 24-h ambulatory BP in patients with hypertension and newly diagnosed obstructive sleep apnea. A total of 147 patients underwent polysomnography, based on which obstructive sleep apnea was diagnosed in 106 patients; these patients underwent 24-h ambulatory BP monitoring within the next 30 days. Each polysomnogram was divided into hourly reports to calculate the variability of the respiratory sleep indices. Variability independent of the mean was considered the primary measure of variability. RESULTS: The median number of hourly polysomnogram reports was 7 (range, 4-8). The hour-to-hour variability of both AHI and ODI, but not of the lowest oxygen saturation, was correlated with the 24-h pulse pressure, 24-h systolic BP, and awake systolic BP (p < 0.05 for all). The fully adjusted linear regression analysis indicated that the hour-to-hour variability of AHI and ODI remained associated with the 24-h pulse pressure (AHI: ß coefficient, 0.264 [95% CI = 0.033-0.495], p = 0.026; ODI: ß coefficient, 0.450 [95% CI = 0.174-0.726], p = 0.002). CONCLUSIONS: The hour-to-hour variability of AHI and ODI is independently associated with the 24-h pulse pressure. Further investigations are warranted to evaluate the clinical relevance of this new-found association.

Asian Pacific Society of Cardiology Consensus Statements on the Diagnosis and Management of Obstructive Sleep Apnoea in Patients with Cardiovascular Disease.

Obstructive sleep apnoea (OSA) is strongly associated with cardiovascular disease (CVD). However, evidence supporting this association in the Asian population is scarce. Given the differences in the epidemiology of CVD and cardiovascular risk factors, as well as differences in the availability of healthcare resources between Asian and Western countries, an Asian Pacific Society of Cardiology (APSC) working group developed consensus recommendations on the management of OSA in patients with CVD in the Asia-Pacific region. The APSC expert panel reviewed and appraised the available evidence using the Grading of Recommendations Assessment, Development, and Evaluation system. Consensus recommendations were developed and put to an online vote. Consensus was reached when 80% of votes for a given recommendation were in support of 'agree' or 'neutral.' The resulting statements provide guidance on the assessment and treatment of OSA in patients with CVD in the Asia-Pacific region. The APSC hopes for these recommendations to pave the way for screening, early diagnosis and treatment of OSA in the Asia-Pacific region.

Improving Analysis and Annotation of Microarray Data with Protein Interactions.

Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.

Modeling tumor progression via the comparison of stage-specific graphs.

Can we use graph mining algorithms to find patterns in tumor molecular mechanisms? Can we model disease progression with multiple time-specific graph comparison algorithms? In this paper, we will focus on this area. Our main contributions are 1) we proposed the Temporal-Omics (Temp-O) workflow to model tumor progression in non-small cell lung cancer (NSCLC) using graph comparisons between multiple stage-specific graphs, and 2) we showed that temporal structures are meaningful in the tumor progression of NSCLC. Other identified temporal structures that were not highlighted in this paper may also be used to gain insights to possible novel mechanisms. Importantly, the Temp-O workflow is generic; while we applied it on NSCLC, it can be applied in other cancers and diseases. We used gene expression data from tumor samples across disease stages to model lung cancer progression, creating stage-specific tumor graphs. Validating our findings in independent datasets showed that differences in temporal network structures capture diverse mechanisms in NSCLC. Furthermore, results showed that structures are consistent and potentially biologically important as we observed that genes with similar protein names were captured in the same cliques for all cliques in all datasets. Importantly, the identified temporal structures are meaningful in the tumor progression of NSCLC as they agree with the molecular mechanism in the tumor progression or carcinogenesis of NSCLC. In particular, the identified major histocompatibility complex of class II temporal structures capture mechanisms concerning carcinogenesis; the proteasome temporal structures capture mechanisms that are in early or late stages of lung cancer; the ribosomal cliques capture the role of ribosome biosynthesis in cancer development and sustainment. Further, on a large independent dataset we validated that temporal network structures identified proteins that are prognostic for overall survival in NSCLC adenocarcinoma.

Comparative network analysis via differential graphlet communities.

While current protein interaction data provides a rich resource for molecular biology, it mostly lacks condition-specific details. Abundance of mRNA data for most diseases provides potential to model condition-specific transcriptional changes. Transcriptional data enables modeling disease mechanisms, and in turn provide potential treatments. While approaches to compare networks constructed from healthy and disease samples have been developed, they do not provide the complete comparison, evaluations are performed on very small networks, or no systematic network analyses are performed on differential network structures. We propose a novel method for efficiently exploiting network structure information in the comparison between any graphs, and validate results in non-small cell lung cancer. We introduce the notion of differential graphlet community to detect deregulated subgraphs between any graphs such that the network structure information is exploited. The differential graphlet community approach systematically captures network structure differences between any graphs. Instead of using connectivity of each protein or each edge, we used shortest path distributions on differential graphlet communities in order to exploit network structure information on identified deregulated subgraphs. We validated the method by analyzing three non-small cell lung cancer datasets and validated results on four independent datasets. We observed that the shortest path lengths are significantly longer for normal graphs than for tumor graphs between genes that are in differential graphlet communities, suggesting that tumor cells create "shortcuts" between biological processes that may not be present in normal conditions.

Integration, visualization and analysis of human interactome.

Data integration and visualization are crucial to obtain meaningful hypotheses from the diversity of 'omics' fields and the large volume of heterogeneous and distributed data sets. In this review we focus on network analysis as a key technique to integrate, visualize and extrapolate relevant information from diverse data. We first describe challenges in integrating different types of data and then focus on systematically exploring network properties to gain insight into network function. We also describe the relationship between network structures and function of elements that form it. Next, we highlight the role of the interactome in connecting data derived from different experiments, and we stress the importance of network analysis to recognize interaction context-specific features. Finally, we present an example integration to demonstrate the value of the network approach in cancer research, and highlight the importance of dynamic data in the specific context of signaling pathways.