Endotypic traits of supine position and supine-predominant obstructive sleep apnoea in Asian patients.

BACKGROUND: Over half of all cases of obstructive sleep apnoea (OSA) are classified as supine-related OSA; however, the pathological endotype during supine position is not fully understood. This study aims to investigate the endotypic traits of supine-predominant OSA and explore the variations in endotypic traits between the supine and lateral positions. METHODS: We prospectively recruited 689 adult patients with OSA from a single sleep centre between April 2020 and December 2022. Endotypic traits, namely arousal threshold, collapsibility, loop gain and upper airway muscle compensation, were retrieved from polysomnographic signals. We identified spOSA by a supine to non-supine apnoea-hypopnoea index (AHI) ratio >2. We cross-sectionally compared demographic and endotypic traits between supine-predominant OSA and non-positional OSA and examined the associations between supine-predominant OSA and endotypic traits. Additionally, we compared the changes in endotypic traits between supine and lateral positions in patients with supine-predominant OSA and non-positional OSA. RESULTS: In our study sample, 75.8% of patients were identified as having supine-predominant OSA. Compared to non-positional OSA, supine-predominant OSA was associated with low collapsibility (ß= -3.46 %eupnoea, 95% CI -5.93- -1.00 %eupnoea) and reduced compensation (ß= -6.79 %eupnoea, 95% CI -10.60- -2.99 %eupnoea). When transitioning from the lateral to supine position, patients with supine-predominant OSA had a substantial decrease in compensation compared to those with non-positional OSA (-11.98 versus -6.28 %eupnoea). CONCLUSIONS: Supine-predominant OSA is the prevalent phenotype of OSA in Asian patients. Inadequate upper airway compensation appears to be a crucial underlying pathology in patients with supine-predominant OSA.

Continuous positive airway pressure and adherence in patients with different endotypes of obstructive sleep apnea.

Determining the endotypes of obstructive sleep apnea (OSA) has potential implications for precision interventions. Here we assessed whether continuous positive airway pressure (CPAP) treatment outcomes differ across endotypic subgroups. We conducted a retrospective analysis of data obtained from 225 patients with moderate-to-severe OSA from a single sleep centre. Polysomnographic and CPAP titration study data were collected between May 2020 and January 2022. One-month CPAP treatment adherence was followed. Obstructive sleep apnea endotypes, namely arousal threshold, collapsibility, loop gain, and upper airway gain were estimated from polysomnography and dichotomised as high versus low. We examined associations between endotypic subgroups and (1) optimal CPAP titration pressure, (2) CPAP-related improvements in sleep architecture (proportions of slow-wave and rapid eye movement (REM) sleep), and (3) CPAP adherence. We observed that patients with high collapsibility required a higher CPAP pressure than those with low collapsibility (∆ = 0.4 cmH2 O, 95% confidence interval [CI] = 0.3-1.7). A larger increase in slow-wave sleep and in REM sleep proportions after CPAP treatment were observed in patients with a high arousal threshold, high collapsibility, high loop gain, or high upper airway gain than in those with low levels of endotypes. High loop gain and high collapsibility were independently associated with longer CPAP use hours per night (∆ = 0.6 h, 95% CI = 0.2-1.5 and ∆ = 0.3 h, 95% CI = 0.03-1.5, respectively). In conclusion, different endotypic subgroups of OSA exhibit a difference in outcomes of CPAP treatment. Knowledge of endotypes may help clinicians to understand which patients are expected to benefit most from CPAP therapy prior to its administration.

Review and perspective on sleep-disordered breathing research and translation to clinics.

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

Relationship between Symptom Profiles and Endotypes among Patients with Obstructive Sleep Apnea: A Latent Class Analysis.

Rationale: Obstructive sleep apnea (OSA) is a heterogeneous syndrome with various endotypic traits and symptoms. A link among symptoms, endotypes, and disease prognosis has been proposed but remains unsupported by empirical data. Objectives: To link symptom profiles and endotypes by clustering endotypic traits estimated using polysomnographic signals. Methods: We recruited 509 patients with moderate to severe OSA from a single sleep center. Polysomnographic data were collected between May 2020 and January 2022. Endotypic traits, namely arousal threshold, upper airway collapsibility, loop gain, and upper airway muscle compensation, were retrieved using polysomnographic signals during non-rapid eye movement periods. We used latent class analysis to group participants into endotype clusters. Demographic and polysomnographic parameter differences were compared between clusters, and associations between endotype clusters and symptom profiles were examined using logistic regression analyses. Results: Three endotype clusters were identified, characterized by high collapsibility/loop gain, low arousal threshold, and low compensation, respectively. Patients in each cluster exhibited similar demographic characteristics, but those in the high collapsibility/loop gain cluster had the highest proportion of obesity and severe oxygen desaturation observed in polysomnographic studies. The low compensation cluster was characterized by fewer sleepy symptoms and exhibited a lower rate of diabetes mellitus. Compared with the excessively sleepy group, disturbed sleep symptoms were associated with the low arousal threshold cluster (odds ratio, 1.89; 95% confidence interval, 1.16-3.10). Excessively sleepy symptoms were associated with the high collapsibility/loop gain cluster (odds ratio, 2.16; 95% confidence interval, 1.39-3.37) compared with the minimally symptomatic group. Conclusions: Three pathological endotype clusters were identified among patients with moderate to severe OSA, each exhibiting distinct polysomnographic characteristics and clinical symptom profiles.

A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study.

Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently, the methods Sands et al. developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors' code, which is computationally expensive and requires technological expertise to run. We present a reimplementation and validation of the integrity of the original authors' code by reproducing the endo-Phenotyping Using Polysomnography (PUP) method of Sands et al. The original MATLAB methods were reprogrammed in Python; efficient algorithms were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p < 10-6 for all): ventilation at eupnea V̇ passive (ICC = 0.97), ventilation at arousal onset V̇ active (ICC = 0.97), loop gain (ICC = 0.96), and arousal threshold (ICC = 0.90). We successfully implemented the original PUP method by Sands et al. providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.

How to measure snoring? A comparison of the microphone, cannula and piezoelectric sensor.

The objective of this study was to compare to each other the methods currently recommended by the American Academy of Sleep Medicine (AASM) to measure snoring: an acoustic sensor, a piezoelectric sensor and a nasal pressure transducer (cannula). Ten subjects reporting habitual snoring were included in the study, performed at Landspitali-University Hospital, Iceland. Snoring was assessed by listening to the air medium microphone located on a patient's chest, compared to listening to two overhead air medium microphones (stereo) and manual scoring of a piezoelectric sensor and nasal cannula vibrations. The chest audio picked up the highest number of snore events of the different snore sensors. The sensitivity and positive predictive value of scoring snore events from the different sensors was compared to the chest audio: overhead audio (0.78, 0.98), cannula (0.55, 0.67) and piezoelectric sensor (0.78, 0.92), respectively. The chest audio was capable of detecting snore events with lower volume and higher fundamental frequency than the other sensors. The 200 Hz sampling rate of the cannula and piezoelectric sensor was one of their limitations for detecting snore events. The different snore sensors do not measure snore events in the same manner. This lack of consistency will affect future research on the clinical significance of snoring. Standardization of objective snore measurements is therefore needed. Based on this paper, snore measurements should be audio-based and the use of the cannula as a snore sensor be discontinued, but the piezoelectric sensor could possibly be modified for improvement.

Cyclic conductance switching in networks of redox-active molecular junctions.

Redox-active dithiolated tetrathiafulvalene derivatives (TTFdT) were inserted in two-dimensional nanoparticle arrays to build interlinked networks of molecular junctions. Upon oxidation of the TTFdT to the dication state, we observed a conductance increase of the networks by up to 1 order of magnitude. Successive oxidation and reduction cycles demonstrated a clear switching behavior of the molecular junction conductance. These results show the potential of interlinked nanoparticle arrays as chemical sensors.

Light-controlled conductance switching of ordered metal-molecule-metal devices.

We demonstrate reversible, light-controlled conductance switching of molecular devices based on photochromic diarylethene molecules. These devices consist of ordered, two-dimensional lattices of gold nanoparticles, in which neighboring particles are bridged by switchable molecules. We independently confirm that reversible isomerization of the diarylethenes employed is at the heart of the room-temperature conductance switching. For this, we take full advantage of the possibility to use optical spectroscopy to follow molecular switching in these samples.