Clinical characteristics of Asian patients with sleep apnea with low arousal threshold and sleep structure change with continuous positive airway pressure

Low arousal threshold is associated with altered functional connectivity of the ascending reticular activating system in patients with obstructive sleep apnea

A low arousal threshold (LAT) is a pathophysiological trait of obstructive sleep apnea (OSA) that may be associated with brainstem ascending reticular activating system-cortical functional connectivity changes. We evaluated resting-state connectivity between the brainstem nuclei and 105 cortical/subcortical regions in OSA patients with or without a LAT and healthy controls. Twenty-five patients with moderate to severe OSA with an apnea-hypopnea index between 20 and 40/hr (15 with and 10 without a LAT) and 15 age- and sex-matched controls were evaluated. Participants underwent functional magnetic resonance imaging after overnight polysomnography. Three brainstem nuclei-the locus coeruleus (LC), laterodorsal tegmental nucleus (LDTg), and ventral tegmental area (VTA)-associated with OSA in our previous study were used as seeds. Functional connectivity values of the two brainstem nuclei (LC and LDTg) significantly differed among the three groups. The connectivity of the LC with the precuneus was stronger in OSA patients than in controls regardless of the concomitant LAT. The connectivity between the LDTg and the posterior cingulate cortex was also stronger in OSA patients regardless of the LAT. Moreover, OSA patients without a LAT showed stronger LDTg-posterior cingulate cortex connectivity than those with a LAT (post hoc p = 0.013), and this connectivity strength was negatively correlated with the minimum oxygen saturation in OSA patients (r = - 0.463, p = 0.023). The LAT in OSA patients was associated with altered LDTg-posterior cingulate cortex connectivity. This result may suggested that cholinergic activity may play a role in the LAT in OSA patients.

The Association of High Arousal Threshold with Hypertension and Diabetes in Obstructive Sleep Apnea

Objective

Compared to low arousal threshold (AT), high AT is an easily overlooked characteristic for obstructive sleep apnea (OSA) severity estimation. This study aims to evaluate the relationship between high AT, hypertension and diabetes in OSA, compared to those with apnea-hypopnea index (AHI).

Methods

A total of 3400 adults diagnosed with OSA were retrospectively recruited. Propensity score matching (PSM) was conducted to further categorize these patients into the low and high AT groups based on the strategy established by previous literature. The different degrees of AHI and quantified AT (AT score) were subsequently measured. The correlation of AT and AHI with the occurrence of various comorbidities in OSA was estimated by logistic regression analysis with odds ratio (OR).

Results

After PSM, 938 pairs of patients arose. The median AT score of high and low AT group was 21.7 and 12.2 scores, and the adjusted OR of high AT for hypertension and diabetes was 1.31 (95% CI = 1.07-1.62, P < 0.01) and 1.45 (95% CI = 1.01-2.08, P < 0.05), respectively. Compared to low AT score group, the OR significantly increased in patients with very high AT score (30 ≤ AT score), especially for diabetes (OR = 1.79, 95% CI = 1.02-3.13, P < 0.05). The significant association was not observed in AHI with increasing prevalent diabetes.

Conclusion

Higher AT is significantly associated with increased prevalence of hypertension and diabetes in patients with OSA. Compared with AHI, AT score is a potentially comprehensive indicator for better evaluating the relationship between OSA and related comorbidities.

Impacts of Different Noise Environments on Polysomnographic Monitoring Parameters and Sleep Quality Among Hospitalized Patients

BACKGROUND

Excessive noise levels may decrease patients' sleep quality and increase the risk of sleep disorders in patients. Given that only a few studies have been conducted on noise levels and sleep quality in hospitalized patients, this study investigated the effects of different noise environments on polysomnographic parameters and sleep in hospitalized patients. It also analyzed the factors associated with patients' sleep quality.

METHODS

A sample of 244 cases of hospitalized patients were retrospectively selected from March 2020 to March 2023. A total of 122 patients without ward noise reduction treatment were set as the control group. A total of 122 patients who were treated with ward noise reduction were set as the observation group. The polysomnographic monitoring parameters and sleep conditions levels were compared between the two groups, after which logistic regression was used to analyze the relevant factors that affected patients' sleep.

RESULTS

The incidence of noise level, rapid eye movement stage (R) phase proportion, nonrapid eye movement stage 1 (N1) phase proportion, and poorer sleep quality all had higher levels in the control group than in the observation group. In comparison, nonrapid eye movement stage 2 (N2) phase proportion, total sleep time (TST), and sleep efficiency (SE) were all lower than those in the observation group (P < 0.05). Regression analysis revealed that the need for surgery, having diabetes mellitus, higher noise level and low N2 percentage levels were all associated factors affecting the sleep quality of patients.

CONCLUSION

Environments with higher levels of noise can lead to patients' poorer sleep quality. Thus, it is necessary to actively implement noise management measures to avoid higher noise levels and maintain good sleep quality among patients.

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.

Respiratory arousal threshold among patients with isolated sleep apnea and with comorbid insomnia (COMISA)

Insomnia and obstructive sleep apnea (OSA) are common sleep disorders and frequently coexist (COMISA). Arousals from sleep may be a common link explaining the frequent comorbidity of both disorders. Respiratory arousal threshold (AT) is a physiologic measurement of the level of respiratory effort to trigger an arousal from sleep. The impact of COMISA on AT is not known. We hypothesized that a low AT is more common among COMISA than among patients with OSA without insomnia. Participants referred for OSA diagnosis underwent a type 3 sleep study and answered the insomnia severity index (ISI) questionnaire and the Epworth sleepiness scale. Participants with an ISI score ≥ 15 were defined as having insomnia. Sleep apnea was defined as an apnea hypopnea index (AHI) ≥ 15 events/h. Low AT was determined using a previously validated score based on 3 polysomnography variables (AHI, nadir SpO2 and the frequency of hypopneas). OSA-only (n = 51) and COMISA (n = 52) participants had similar age (61[52-68] vs 60[53-65] years), body-mass index (31.3[27.7-36.2] vs 32.2[29.5-38.3] kg/m2) and OSA severity (40.2[27.5-60] vs 37.55[27.9-65.2] events/h): all p = NS. OSA-only group had significantly more males than the COMISA group (58% vs 33%, p = 0.013. The proportion of participants with a low AT among OSA-only and COMISA groups was similar (29 vs 33%, p = NS). The similar proportion of low AT among COMISA and patients with OSA suggests that the respiratory arousal threshold may not be related to the increased arousability of insomnia.

Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features

Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features.