An expedited model for identifying potential patients with periodic leg movements.

Periodic leg movements during sleep (PLMS) may have crucial consequences in adults. This study aimed to identify baseline characteristics, symptoms, or questionnaires that could help to identify sleep-disordered breathing patients with significant PLMS. Patients aged 20-80 years who underwent polysomnography for assessing sleep disturbance were included. Various factors such as sex, age, body measurements, symptoms, apnea-hypopnea index (AHI), and sleep quality scales were analysed to determine the presence of PLMS. The study included 1480 patients with a mean age of 46.4 ± 13.4 years, among whom 110 (7.4%) had significant PLMS with a PLM index of 15 or higher. There were no significant differences observed in terms of sex or BMI between patients with and without significant PLMS. However, the odds ratios (OR) for PLMS were 4.33, 4.41, and 4.23 in patients who were aged over 50 years, had insomnia, or had an ESS score of less than 10, respectively. Notably, the OR increased up to 67.89 times in patients who presented with all three risk factors. Our analysis identified significant risk factors for PLMS: age over 50, self-reported insomnia, and lower daytime sleepiness levels. These findings aid in identifying potential PLMS patients, facilitating confirmatory examinations and managing associated comorbidities.

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework.

Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30-90 s in advance. Preprocessed 30 s segments were time-frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique. Specific frequency bands of 0.5-50 Hz, 0.8-10 Hz, and 8-50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease.

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1.

Effects of inspiratory muscle training on blood pressure- and sleep-related outcomes in patients with obstructive sleep apnea: a meta-analysis of randomized controlled trials.

PURPOSE: Obstructive sleep apnea (OSA) is frequently accompanied by hypertension, resulting in cardiovascular comorbidities. Continuous positive airway pressure is a standard therapy for OSA but has poor adherence. Inspiratory muscle training (IMT) may reduce airway collapsibility and sympathetic output, which may decrease OSA severity and blood pressure. In this meta-analysis of randomized controlled trials (RCTs), we evaluated the efficacy of IMT in patients with OSA. METHODS: We searched PubMed, EMBASE, Cochrane Library, Web of Science, and ClinicalTrials.gov databases for relevant RCTs published before November 2022. RESULTS: Seven RCTs with a total of 160 patients with OSA were included. Compared with the control group, the IMT group exhibited significantly lower systolic and diastolic blood pressure (mean difference [MD]: - 10.77 and - 4.58 mmHg, respectively), plasma catecholamine levels (MD: - 128.64 pg/mL), Pittsburgh Sleep Quality Index (MD: - 3.06), and Epworth Sleepiness Scale score (MD: - 4.37). No significant between-group differences were observed in the apnea-hypopnea index, forced vital capacity (FVC), ratio of forced expiratory volume in 1 s to FVC, or adverse effects. The data indicate comprehensive evidence regarding the efficacy of IMT for OSA. However, the level of certainty (LOC) remains low. CONCLUSION: IMT improved blood pressure- and sleep-related outcomes without causing adverse effects and may thus be a reasonable option for lowering blood pressure in patients with OSA. However, additional studies with larger sample sizes and rigorous study designs are warranted to increase the LOC.

Associations of overnight changes in body composition with positional obstructive sleep apnea.

PURPOSE: Body composition is considered to be associated with obstructive sleep apnea (OSA) severity. This cross-sectional study aimed to examine associations of overnight body composition changes with positional OSA. METHODS: The body composition of patients diagnosed with non-positional and positional OSA was measured before and after overnight polysomnography. Odds ratios (ORs) of outcome variables between the case (positional OSA) and reference (non-positional OSA) groups were examined for associations with sleep-related parameters and with changes in body composition by a logistic regression analysis. RESULTS: Among 1584 patients with OSA, we used 1056 patients with non-positional OSA as the reference group. We found that a 1-unit increase in overnight changes of total fat percentage and total fat mass were associated with 1.076-fold increased OR (95% confidence interval (CI): 1.014, 1.142) and 1.096-fold increased OR (95% CI: 1.010, 1.189) of positional OSA, respectively (all p < 0.05). Additionally, a 1-unit increase in overnight changes of lower limb fat percentage and upper limb fat mass were associated with 1.043-fold increased OR (95% CI: 1.004, 1.084) and 2.638-fold increased OR (95% CI: 1.313, 5.302) of positional OSA, respectively (all p < 0.05). We observed that a 1-unit increase in overnight changes of trunk fat percentage and trunk fat mass were associated with 1.056-fold increased OR (95% CI: 1.008, 1.106) and 1.150-fold increased OR (95% CI: 1.016, 1.301) of positional OSA, respectively (all p < 0.05). CONCLUSION: Our findings indicated that nocturnal changes in the body's composition, especially total fat mass, total fat percentage, lower limb fat percentage, upper limb fat mass, trunk fat percentage, and trunk fat mass, may be associated with increased odds ratio of positional OSA compared with non-positional OSA.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Association of air pollution exposure with exercise-induced oxygen desaturation in COPD.

BACKGROUND: There is a link between exposure to air pollution and the increased prevalence of chronic obstructive pulmonary disease (COPD) and declining pulmonary function, but the association with O2 desaturation during exercise in COPD patients with emphysema is unclear. Our aims were to estimate the prevalence of O2 desaturation during exercise in patients with COPD, and determine the association of exposure to air pollution with exercise-induced desaturation (EID), the degree of emphysema, and dynamic hyperinflation (DH). METHODS: We assessed the effects of 10-year prior to the HRCT assessment and 7 days prior to the six-minute walking test exposure to particulate matter with an aerodynamic diameter of < 10 µm (PM10) or of < 2.5 µM (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in patients with emphysema in this retrospective cohort study. EID was defined as a nadir standard pulse oximetry (SpO2) level of < 90% or a delta (△)SpO2 level of ≥ 4%. Ambient air pollutant (PM2.5, PM10, O3, and NO2) data were obtained from Taiwan Environmental Protection Administration (EPA) air-monitoring stations, usually within 10 km to each participant's home address. RESULTS: We recruited 141 subjects with emphysema. 41.1% of patients with emphysema exhibited EID, and patients with EID had more dyspnea, worse lung function, more severe emphysema, more frequent acute exacerbations, managed a shorter walking distance, had DH, and greater long-term exposure to air pollution than those without EID. We observed that levels of 10-year concentrations of PM10, PM2.5, and NO2 were significantly associated with EID, PM10 and PM2.5 were associated with the severity of emphysema, and associated with DH in patients with emphysema. In contrast, short-term exposure did not have any effect on patients. CONCLUSION: Long-term exposure to ambient PM10, PM2.5 and NO2, but not O3, was associated with EID.

3-Nitrobenzanthrone promotes malignant transformation in human lung epithelial cells through the epiregulin-signaling pathway.

Exposure to environmental and occupational contaminants leads to lung cancer. 3-Nitrobenzanthrone (3-nitro-7H-benz[de]anthracen-7-one, 3-NBA) is a potential carcinogen in ambient air or diesel particulate matter. Studies have revealed that short-term exposure to 3-NBA induces cell death, reactive oxygen species activation, and DNA adduct formation and damage. However, details of the mechanism by which chronic exposure to 3-NBA influences lung carcinogenesis remain largely unknown. In this study, human lung epithelial BEAS-2B cells were continuously exposed to 0-10-µM 3-NBA for 6 months. NanoString analysis was conducted to evaluate gene expression in the cells, revealing that 3-NBA-mediated transformation results in a distinct gene expression signature including carbon cancer metabolism, metastasis, and angiogenesis. Alterations in tumor-promoting genes such as EREG (epiregulin), SOX9, E-cadherin, TWIST, and IL-6 were involved in epithelial cell aggressiveness. Kaplan-Meier plotter analyses indicated that increased EREG and IL-6 expressions in early-stage lung cancer cells are correlated with poor survival. In vivo xenografts on 3-NBA-transformed cells exhibited prominent tumor formation and metastasis. EREG knockout cells exposed to 3-NBA for a short period exhibited high apoptosis and low colony formation. By contrast, overexpression of EREG in 3-NBA-transformed cells markedly activated the PI3K/AKT and MEK/ERK signaling pathways, resulting in tumorigenicity. Furthermore, elevated IL-6 and EREG expressions synergistically led to STAT3 signaling activation, resulting in clonogenic cell survival and migration. Taken together, chronic exposure of human lung epithelial cells to 3-NBA leads to malignant transformation, in which the EREG signaling pathway plays a pivotal mediating role. • Short-term exposure of lung epithelial cells to 3-NBA can lead to ROS production and cell apoptosis. • Long-term chronic exposure to 3-NBA upregulates the levels of tumor-promoting genes such as EREG and IL-6. • Increased EREG expression in 3-NBA-transformed cells markedly contributes to tumorigenesis through PI3K/AKT and MEK/ERK activation and synergistically enhances the IL-6/STAT3 signaling pathway, which promotes tumorigenicity.

Elevated tau and ß-amyloid in the serum of fibromyalgia patients.

OBJECTIVE: Fibromyalgia (FM) is a chronic widespread pain syndrome. Although its mechanism remains relatively unknown, accelerated neurodegeneration in the brain has been reported in patients with FM. Sleep disturbance can increase the risk of neurocognitive disorders, which are associated with tau and beta-amyloid (Aß) protein accumulation. We hypothesize neurodegeneration in patients with FM may be associated with sleep disturbance. METHODS: In this case-control study, we analyzed serum tau and Aß levels and their association with symptom profiles for patients with FM, by recruiting 22 patients with FM and 22 age-matched healthy participants. The visual analog scale, Fibromyalgia Impact Questionnaire, pressure pain threshold test, Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory-II, Beck Anxiety Inventory, and serum tau and beta-amyloid-42 (Aß-42) levels were recorded. The Mann-Whitney test was conducted to compare questionnaire and protein level results between the groups. Pearson correlation test was conducted to investigate the correlation of questionnaire scores with tau and Aß-42 levels in patients with FM. The significance level was set at P < .05. RESULTS: Serum tau and Aß-42 levels were significantly higher in patients with FM than in controls. A positive correlation between serum tau levels and PSQI scores was observed in patients with FM (r = 0.476, P = .025). We found that only sleep disturbance in patients with FM was significantly associated with higher serum tau levels among all symptom scores. CONCLUSIONS: We suggest sleep disturbance may play a vital role in the pathomechanism of accelerated neurodegeneration in FM.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Impaired lnc-IL7R modulatory mechanism of Toll-like receptors is associated with an exacerbator phenotype of chronic obstructive pulmonary disease.

Patients with chronic obstructive pulmonary disease (COPD) are susceptible to bacterial infections, which worsen lung inflammation and contribute to lung function decline and acute exacerbation. Long noncoding (lnc) RNAs are emerging regulators of inflammation with unknown clinical relevance. Herein, we report that levels of the Toll-like receptor (TLR)-related lnc interleukin (IL) 7 receptor (IL7R) were significantly reduced in peripheral blood mononuclear cells from patients with COPD compared with those from normal controls, and the levels were correlated with pulmonary function. Moreover lnc-IL7R levels were reduced in lavaged alveolar macrophages and primary human small airway epithelial cells (HSAEpCs) from patients with COPD. Lnc-IL7R knockdown in primary human macrophages, HSAEpCs, and human pulmonary microvascular endothelial cells (HPMECs) significantly augmented the induction of proinflammatory mediators after TLR2/4 activation. By contrast, lnc-IL7R overexpression attenuated inflammation after TLR2/4 activation. Similar results with lnc-IL7R-mediated inflammation were observed in COPD HSAEpCs. Mechanistically, lnc-IL7R mediated a repressive chromatin state of the proinflammatory gene promoter as a result of decreased acetylation (H3K9ac) and increased methylation (H3K9me3 and H3K27me3). Plasma lnc-IL7R levels were reduced in patients with COPD who experienced more acute exacerbation in the previous year. Notably, patients with lower lnc-IL7R levels in the subsequent year had increased exacerbation risk. Low lnc-IL7R expression in COPD may augment TLR2/4-mediated inflammation and be associated with acute exacerbation.

Subjective sleep quality and association with depression syndrome, chronic diseases and health-related physical fitness in the middle-aged and elderly.

BACKGROUND: As a complex phenomenon, sleep quality is difficult to objectively define and measure, and multiple factors related to sleep quality, such as age, lifestyle, physical activity, and physical fitness, feature prominently in older adult populations. The aim of the present study was to evaluate subjective sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and to associate sleep quality with health-related physical fitness factors, depressive symptoms, and the number of chronic diseases in the middle-aged and elderly. METHODS: We enrolled a total of 283 middle-aged and elderly participants from a rehabilitation clinic or health examination department. The PSQI was used to evaluate sleep quality. The health-related fitness assessment included anthropometric and physical fitness parameters. Depressive symptoms were measured with the Center for Epidemiologic Studies Depression Scale (CES-D) short form. Data were analyzed with SPSS 18.0, and descriptive statistics and logistic regression analysis were used for the analyses. RESULTS: Overall, 27.9% of participants in this study demonstrated bad sleepers (with a PSQI score of > 5), 10.2% of study participants frequently used sleep medication to help them fall asleep, and 6.0% reported having significant depressive symptoms (with a CES-D score of ≥10). There are two major findings: (1) depression symptoms, the number of chronic diseases, self-rated health, and arthritis were significantly associated with a poor sleep quality, and (2) the 2-min step test was associated with longer sleep latency. These results confirmed that the 2-min step was associated with a longer sleep latency among the health-related physical fitness items. CONCLUSIONS: Our study found that depressive syndrome, chronic disease numbers, a poor self-rated health status, and arthritis were the main risk factors that influenced subjective sleep quality.

Suppressor of variegation 3-9 homologue 1 impairment and neutrophil-skewed systemic inflammation are associated with comorbidities in COPD.

BACKGROUND: Systemic manifestations and comorbidities are characteristics of chronic obstructive pulmonary disease (COPD) and are probably due to systemic inflammation. The histone methyltransferase SUV39H1 controls the Th1/Th2 balance. We previously reported that reduced SUV39H1 expression contributed to abnormal inflammation in COPD. Here, we aimed to determine whether impaired SUV39H1 expression in COPD patients associated with neutrophilic/eosinophilic inflammation responses and comorbidities. METHODS: A total of 213 COPD patients and 13 healthy controls were recruited from the Shuang Ho Hospital, Taipei Medical University. SUV39H1 levels in peripheral blood mononuclear cells (PBMCs) from 13 healthy and 30 COPD participants were measured by immunoblotting. We classified the patients into two groups based on low (fold change, FC < 0.5) and high SUV39H1 expression (FC ≥ 0.5) compared to normal controls. Clinical outcomes including neutrophil or eosinophil counts associated with SUV39H1-related inflammation were evaluated by Chi square analyses or Mann-Whitney U test. The correlations between the percentage of neutrophils and number of COPD comorbidities or Charlson Comorbidity Index (CCI) scores were performed by Spearman's rank analysis. RESULTS: Low SUV39H1 expression group had high neutrophil counts relative to high SUV39H1expression group. In the COPD cohort, the high comorbidity group (≥ 2 comorbidities) had higher counts of whole white blood cell (WBC) and neutrophil, and lower proportion of eosinophil and eosinophil/neutrophil, as compared with low comorbidity group (0 and 1 comorbidities). The quantity of neutrophils was associated with COPD comorbidities (Spearman's r = 0.388, p < 0.001), but not with CCI scores. We also found that the high comorbidity group had more exacerbations per year compared with low comorbidity group (1.5 vs. 0.9 average exacerbations, p = 0.005). However, there were no significant differences between groups with these non-frequent (0-1 exacerbation) and frequent exacerbations per year (> 1 exacerbation) in numbers of WBC and proportion of neutrophils, eosinophils or eosinophil/neutrophil. Finally, patients with high comorbidities had lower SUV39H1 levels in their PBMCs than did those with low comorbidities. CONCLUSION: Blood neutrophil counts are associated with comorbidities in COPD patients. Impaired SUV39H1 expression in PBMCs from COPD patients are correlated with neutrophilic inflammation and comorbidities.

Comparison of Hospital-Based and Home-Based Obstructive Sleep Apnoea Severity Measurements with a Single-Lead Electrocardiogram Patch.

Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear. We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx). After the PSG, all participants were instructed to continue wearing the RootiRx over three subsequent nights. Scores on OSA indices-namely, the apnoea-hypopnea index, chest effort index (CEI), cyclic variation of heart rate index (CVHRI), and combined CVHRI and CEI (Rx index), were determined. The patients were divided into three groups based on PSG-determined OSA severity. The variables (various severity groups and environmental measurements) were subjected to mean comparisons, and their correlations were examined by Pearson's correlation coefficient. The hospital-based CVHRI, CEI, and Rx index differed significantly among the severity groups. All three groups exhibited a significantly lower percentage of supine sleep time in the home-based assessment, compared with the hospital-based assessment. The percentage of supine sleep time (∆Supine%) exhibited a significant but weak to moderate positive correlation with each of the OSA indices. A significant but weak-to-moderate correlation between the ∆Supine% and ∆Rx index was still observed among the patients with high sleep efficiency (≥80%), who could reduce the effect of short sleep duration, leading to underestimation of the patients' OSA severity. The high supine percentage of sleep may cause OSA indices' overestimation in the hospital-based examination. Sleep recording at home with patch-type wearable devices may aid in accurate OSA diagnosis.

Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages.

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.

Correction: REM-related obstructive sleep apnea and vertigo: A retrospective case-control study.

[This corrects the article DOI: 10.1371/journal.pone.0252844.].

Combining wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of two levels of OSA severity (i.e., moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using the wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index (AHI), results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between AHI and RDIPSG_TST were identified. In terms of the agreement between the two devices, the average difference between PSG-based AHI and radar-based RDIPSG_TST was 0.59 events/h, while 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy, and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work.

Nocturnal CPAP improves walking capacity in COPD patients with obstructive sleep apnoea.

BACKGROUND: Exercise limitation is an important issue in patients with chronic obstructive pulmonary disease (COPD), and it often co-exists with obstructive sleep apnoea (overlap syndrome). This study examined the effects of nocturnal continuous positive airway pressure (CPAP) treatment on walking capacity in COPD patients with or without obstructive sleep apnoea. METHODS: Forty-four stable moderate-to-severe COPD patients were recruited and completed this study. They all underwent polysomnography, CPAP titration, accommodation, and treatment with adequate pressure. The incremental shuttle walking test was used to measure walking capacity at baseline and after two nights of CPAP treatment. Urinary catecholamine and heart rate variability were measured before and after CPAP treatment. RESULTS: After two nights of CPAP treatment, the apnoea-hypopnoea index and oxygen desaturation index significantly improved in both overlap syndrome and COPD patients, however these changes were significantly greater in the overlap syndrome than in the COPD group. Sleep architecture and autonomic dysfunction significantly improved in the overlap syndrome group but not in the COPD group. CPAP treatment was associated with an increased walking capacity from baseline from 226.4 ± 95.3 m to 288.6 ± 94.6 m (P < 0.05), and decreased urinary catecholamine levels, pre-exercise heart rate, oxygenation, and Borg scale in the overlap syndrome group. An improvement in the apnoea-hypopnoea index was an independent factor associated with the increase in walking distance (r = 0.564). CONCLUSION: Nocturnal CPAP may improve walking capacity in COPD patients with overlap syndrome. TRIAL REGISTRATION: NCT00914264.

The joint association of ambient air pollution and different sleep posture with mild obstructive sleep apnea: A study conducted at Taipei Sleep Center.

BACKGROUND: Growing evidence suggests the detrimental impact of supine position and air pollution on obstructive sleep apnea (OSA), as well as the potential benefits of nonsupine positions. However, their interaction effects on OSA remain unclear. OBJECTIVES: To evaluate the interaction effects of air pollution (NO2/PM2.5) and sleep position on OSA on additive and multiplicative scales. METHODS: This study included 3330 individuals. Personal exposure to air pollution was assessed using a spatiotemporal model. OSA was diagnosed through polysomnography. The associations of supine and nonsupine positions and air pollutants with mild-OSA and their interaction effects on mild-OSA. were explored through generalized logistic regression. RESULTS: Supine position and high NO2 level independently increased the risk of mild-OSA, while PM2.5 was not associated with mild-OSA. Significant interactions were observed between supine position and NO2 at different lag periods (0-7 days, 0-1 year, and 0-2 years) (P = 0.042, 0.013, and 0.010, respectively). The relative excess risks due to interactions on the additive scale for 1-week, 1-year, and 2-year NO2 exposure and supine position were 0.63 (95 % CI: 0.10-1.16), 0.56 (95 % CI: 0.13-0.99), and 0.64 (95 % CI: 0.18-1.10); the corresponding odds ratios for interactions on the multiplicative scale were 1.45 (95 % CI: 1.01-2.07), 1.55 (95 % CI: 1.09-2.22), and 1.60 (95 % CI: 1.12-2.28). The positive interactions persisted in men and participants with obesity. No interaction was observed between nonsupine position and NO2 levels; nevertheless, significant interactions were noted on both the negative additive and multiplicative scales in men. CONCLUSION: Prolonged supine sleep significantly increased the risk of mild-OSA, particularly in men and individuals with obesity. Although the benefits of nonsupine position are considerably less than the risks of NO2 exposure, avoiding prolonged supine sleep may reduce the risk of mild-OSA caused by high levels of NO2 in men.

Air pollution exacerbates mild obstructive sleep apnea by disrupting nocturnal changes in lower-limb body composition: a cross-sectional study conducted in urban northern Taiwan.

BACKGROUND: Few studies have explored the role of body composition linking air pollution to obstructive sleep apnea (OSA). OBJECTIVE: To estimate the effects of air pollution on body composition and OSA, and that of body composition on OSA. METHODS: This study included 3550 individuals. A spatiotemporal model estimated personal exposure. Nocturnal changes in body composition were assessed through bioelectric impedance analysis. OSA was diagnosed using polysomnography. A generalized linear model was used to evaluate the absolute nocturnal changes in body composition associated with an interquartile range (IQR) increase in pollutants. A generalized logistic model was used to estimate odds ratios (ORs) of mild-OSA compared to non-OSA. Association between body composition and apnea-hypopnea index (AHI) was investigated through partial least squares (PLS) regression. RESULTS: Nocturnal changes in lower-limb body composition were associated with NO2 and PM2.5 in all patients. In participants with AHI <15, both short- and long-term NO2 exposures affected body composition and mild-OSA, while PM2.5 was not associated with either outcome. In a PLS model incorporating eight NO2-associated lower-limb parameters, the variable importance projection scores (VIP) of left leg impedance (LLIMP), predicted muscle mass (LLPMM), fat-free mass (LLFFM), and right leg impedance (RLIMP) exceeded 1; the corresponding coefficients ranked in the top four for AHI prediction. The adjusted OR (mild vs. non-OSA) was 1.67 (95 % CI: 1.36-2.03) associated with an IQR increase in prediction value estimated from body compositions. Notably, the two-pollutant model investigating the effects of pollutants on body compositions revealed associations of four parameters (LLIMP, LLPMM, LLFFM, and RLIMP) with NO2 in all lags, which indicates their indispensability in the association between NO2 and AHI. CONCLUSIONS: NO2 exacerbates mild-OSA by disrupting nocturnal changes in lower-limb body composition of patients with AHI <15. PM2.5 was associated with nocturnal changes in lower-limb body composition but not with mild-OSA.