Mediating role of obstructive sleep apnea in altering slow-wave activity and elevating Alzheimer's disease risk: Pilot study from a northern Taiwan cohort.

OBJECTIVES: Obstructive sleep apnea is associated with alterations in slow-wave activity during sleep, potentially increasing the risk of Alzheimer's disease. This study investigated the associations between obstructive sleep apnea manifestations such as respiratory events, hypoxia, arousal, slow-wave patterns, and neurochemical biomarker levels. METHODS: Individuals with suspected obstructive sleep apnea underwent polysomnography. Sleep disorder indices, oxygen metrics, and slow-wave activity data were obtained from the polysomnography, and blood samples were taken the following morning to determine the plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aß42) by using an ultrasensitive immunomagnetic reduction assay. Subsequently, the participants were categorized into groups with low and high Alzheimer's disease risk on the basis of their computed product Aß42 × T-Tau. Intergroup differences and the associations and mediation effects between sleep-related parameters and neurochemical biomarkers were analyzed. RESULTS: Forty-two participants were enrolled, with 21 assigned to each of the low- and high-risk groups. High-risk individuals had a higher apnea-hypopnea index, oxygen desaturation index (≥3%, ODI-3%), fraction of total sleep time with oxygen desaturation (SpO2-90% TST), and arousal index and greater peak-to-peak amplitude and slope in slow-wave activity, with a correspondingly shorter duration, than did low-risk individuals. Furthermore, indices such as the apnea-hypopnea index, ODI-3% and SpO2-90% TST were found to indirectly affect slow-wave activity, thereby raising the Aß42 × T-Tau level. CONCLUSIONS: Obstructive sleep apnea manifestations, such as respiratory events and hypoxia, may influence slow-wave sleep activity (functioning as intermediaries) and may be linked to elevated neurochemical biomarker levels. However, a longitudinal study is necessary to determine causal relationships among these factors. STATEMENT OF SIGNIFICANCE: This research aims to bridge gaps in understanding how obstructive sleep apnea is associated with an elevated risk of Alzheimer's disease, providing valuable knowledge for sleep and cognitive health.

Efficacy of Aromatherapy Against Behavioral and Psychological Disturbances in People With Dementia: A Meta-Analysis of Randomized Controlled Trials.

OBJECTIVES: Behavioral and psychological symptoms of dementia (BPSD) are common in people with dementia. Aromatherapy may reduce the frequency and severity of BPSD. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy of aromatherapy in relieving BPSD and improving functional ability in people with dementia. DESIGN: Systematic review and meta-analysis. SETTING AND PARTICIPANTS: Patients with dementia receiving aromatherapy. METHODS: A literature search was conducted using PubMed, Embase, and Cochrane Library for RCTs published before March 2024 comparing aromatherapy with control treatments in patients with dementia. RESULTS: There were 15 trials involving 821 patients. Overall, significant reduction in BPSD was observed after 1 month of aromatherapy treatment. Among 15 trials, 9 reported the Cohen-Mansfield Agitation Inventory (CMAI) score, and 7 evaluated the Neuropsychiatric Inventory (NPI) score. The meta-analysis showed significant improvement in CMAI score [weighted mean difference (WMD) -6.31, 95% CI -9.52 to -3.11] and NPI score (WMD -8.07, 95% CI -13.53 to -2.61) in patients receiving 3 to 4 weeks of aromatherapy compared with the control group. Four of the 15 trials reported improvement in depressive mood and 3 trials reported no significant improvement in functional ability. CONCLUSIONS AND IMPLICATIONS: In conclusion, aromatherapy is a safe and viable nonpharmacologic treatment to improve BPSD in people with dementia and its combination with massage showed higher efficacy.

Combining a 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 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using a 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, 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 apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 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. CITATION: Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.

Application of blood-based biomarkers of Alzheimer's disease in clinical practice: Recommendations from Taiwan Dementia Society.

Blood-based biomarkers (BBM) are potentially powerful tools that assist in the biological diagnosis of Alzheimer's disease (AD) in vivo with minimal invasiveness, relatively low cost, and good accessibility. This review summarizes current evidence for using BBMs in AD, focusing on amyloid, tau, and biomarkers for neurodegeneration. Blood-based phosphorylated tau and the Aß42/Aß40 ratio showed consistent concordance with brain pathology measured by CSF or PET in the research setting. In addition, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are neurodegenerative biomarkers that show the potential to assist in the differential diagnosis of AD. Other pathology-specific biomarkers, such as α-synuclein and TAR DNA-binding protein 43 (TDP-43), can potentially detect AD concurrent pathology. Based on current evidence, the working group from the Taiwan Dementia Society (TDS) achieved consensus recommendations on the appropriate use of BBMs for AD in clinical practice. BBMs may assist clinical diagnosis and prognosis in AD subjects with cognitive symptoms; however, the results should be interpreted by dementia specialists and combining biochemical, neuropsychological, and neuroimaging information. Further studies are needed to evaluate BBMs' real-world performance and potential impact on clinical decision-making.

Efficacy of Vestibular Rehabilitation in Vestibular Neuritis: A Systematic Review and Meta-analysis.

OBJECTIVE: This study aimed to evaluate the efficacy of vestibular rehabilitation in vestibular neuritis. DESIGN: A randomized controlled trial was collected from MEDLINE, Embase, Cochrane Library, PEDro, LILACS, and Google Scholar before May 2023. RESULTS: This study included 12 randomized controlled trials involving 536 patients with vestibular neuritis. Vestibular rehabilitation was comparable with steroids in dizziness handicap inventory score at the first, sixth, and 12th months (pooled mean differences: -4.00, -0.21, and -0.31, respectively); caloric lateralization at the third, sixth, and 12th months (pooled mean difference: 1.10, 4.76, and -0.31, respectively); and abnormal numbers of vestibular-evoked myogenic potentials at the first, sixth, and 12th months. Patients receiving a combination of rehabilitation and steroid exhibited significant improvement in dizziness handicap inventory score at the first, third, and 12th months (mean difference: -14.86, pooled mean difference: -4.63, mean difference: -9.50, respectively); caloric lateralization at the first and third months (pooled mean difference: -10.28, pooled mean difference: -8.12, respectively); and numbers of vestibular-evoked myogenic potentials at the first and third months (risk ratios: 0.66 and 0.60, respectively) than did those receiving steroids alone. CONCLUSIONS: Vestibular rehabilitation is recommended for patients with vestibular neuritis. A combination of vestibular rehabilitation and steroids is more effective than steroids alone in the treatment of patients with vestibular neuritis.

Digital phenotyping: An equal opportunity approach to reducing disparities in Alzheimer's disease and related dementia research.

A rapidly aging world population is fueling a concomitant increase in Alzheimer's disease (AD) and related dementias (ADRD). Scientific inquiry, however, has largely focused on White populations in Australia, the European Union, and North America. As such, there is an incomplete understanding of AD in other populations. In this perspective, we describe research efforts and challenges of cohort studies from three regions of the world: Central America, East Africa, and East Asia. These cohorts are engaging with the Davos Alzheimer's Collaborative (DAC), a global partnership that brings together cohorts from around the world to advance understanding of AD. Each cohort is poised to leverage the widespread use of mobile devices to integrate digital phenotyping into current methodologies and mitigate the lack of representativeness in AD research of racial and ethnic minorities across the globe. In addition to methods that these three cohorts are already using, DAC has developed a digital phenotyping protocol that can collect ADRD-related data remotely via smartphone and/or in clinic via a tablet to generate a common data elements digital dataset that can be harmonized with additional clinical and molecular data being collected at each cohort site and when combined across cohorts and made accessible can provide a global data resource that is more racially/ethnically represented of the world population.

Association between air pollutant exposure, body water distribution and sleep disorder indices in individuals with low-arousal-threshold obstructive sleep apnoea.

BACKGROUND: Air pollution may alter body water distribution, it may also be linked to low-arousal-threshold obstructive sleep apnoea (low-ArTH OSA). Here, we explored the mediation effects of air pollution on body water distribution and low-ArTH OSA manifestations. METHODS: In this retrospective study, we obtained sleep centre data from healthy participants and patients with low-ArTH OSA (N=1924) in northern Taiwan. Air pollutant exposure at different time intervals (1, 3, 6 and 12 months) was estimated using the nearest station estimation method, and government air-quality data were also obtained. Regression models were used to assess the associations of estimated exposure, sleep disorder indices and body water distribution with the risk of low-ArTH OSA. Mediation analysis was performed to explore the relationships between air pollution, body water distribution and sleep disorder indices. RESULTS: First, exposure to particulate matter (PM) with a diameter of ≤10 µm (PM10) for 1 and 3 months and exposure to PM with a diameter of ≤2.5 µm (PM2.5) for 3 months were significantly associated with the Apnoea-Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Arousal Index (ArI) and intracellular-to-extracellular water ratio (I-E water ratio). Significant associations were observed between the risk of low-ArTH OSA and 1- month exposure to PM10 (OR 1.42, 95% CI 1.09 to 1.84), PM2.5 (OR 1.33, 95% CI 1.02 to 1.74) and ozone (OR 1.27, 95% CI 1.01 to 1.6). I-E water ratio alternation caused by 1-month exposure to PM10 and 3-month exposure to PM2.5 and PM10 had partial mediation effects on AHI and ODI. CONCLUSION: Air pollution can directly increase sleep disorder indices (AHI, ODI and ArI) and alter body water distribution, thus mediating the risk of low-ArTH OSA.

Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

Objective: Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence. Methods: Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results: InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions: The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

Clinical trials of new drugs for Alzheimer disease: a 2020-2023 update.

Alzheimer's disease (AD) is the leading cause of dementia, presenting a significant unmet medical need worldwide. The pathogenesis of AD involves various pathophysiological events, including the accumulation of amyloid and tau, neuro-inflammation, and neuronal injury. Clinical trials focusing on new drugs for AD were documented in 2020, but subsequent developments have emerged since then. Notably, the US-FDA has approved Aducanumab and Lecanemab, both antibodies targeting amyloid, marking the end of a nearly two-decade period without new AD drugs. In this comprehensive report, we review all trials listed in clinicaltrials.gov, elucidating their underlying mechanisms and study designs. Ongoing clinical trials are investigating numerous promising new drugs for AD. The main trends in these trials involve pathophysiology-based, disease-modifying therapies and the recruitment of participants in earlier stages of the disease. These trends underscore the significance of conducting fundamental research on pathophysiology, prevention, and intervention prior to the occurrence of brain damage caused by AD.

Therapeutic hypothermia in patients after cardiac arrest: A systematic review and meta-analysis of randomized controlled trials.

OBJECTIVE: Targeted temperature management (TTM) with therapeutic hypothermia (TH) has been used to improve neurological outcomes in patients after cardiac arrest; however, several trials have reported conflicting results regarding its effectiveness. This systematic review and meta-analysis assessed whether TH was associated with better survival and neurological outcomes after cardiac arrest. METHOD: We searched online databases for relevant studies published before May 2023. Randomized controlled trials (RCTs) comparing TH and normothermia in post-cardiac-arrest patients were selected. Neurological outcomes and all-cause mortality were assessed as the primary and secondary outcomes, respectively. A subgroup analysis according to initial electrocardiography (ECG) rhythm was performed. RESULT: Nine RCTs (4058 patients) were included. The neurological prognosis was significantly better in patients with an initial shockable rhythm after cardiac arrest (RR = 0.87, 95% confidence interval [CI] = 0.76-0.99, P = 0.04), especially in those with earlier TH initiation (<120 min) and prolonged TH duration (≥24 h). However, the mortality rate after TH was not lower than that after normothermia (RR = 0.91, 95% CI = 0.79-1.05). In patients with an initial nonshockable rhythm, TH did not provide significantly more neurological or survival benefits (RR = 0.98, 95% CI = 0.93-1.03 and RR = 1.00, 95% CI = 0.95-1.05, respectively). CONCLUSION: Current evidence with a moderate level of certainty suggests that TH has potential neurological benefits for patients with an initial shockable rhythm after cardiac arrest, especially in those with faster TH initiation and longer TH maintenance.

Associations between air pollution, intracellular-to-extracellular water distribution, and obstructive sleep apnea manifestations.

Background: Exposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations. Objectives: This study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution. Methods: This retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined. Results: Significant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA. Conclusion: Because exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the relationship between air pollution, body fluid parameters, and OSA severity.

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 Low Arousal Threshold Obstructive Sleep Apnea Manifestations with Body Fat and Water Distribution.

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E-I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E-I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

Mindfulness or meditation therapy for Parkinson's disease: A systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disorder worldwide. Mindfulness and meditation therapies have been demonstrated as effective alternative treatments for patients with neurological disorders. However, the effects of mindfulness and meditation therapies on PD remain unclear. This meta-analysis investigated the effects of mindfulness and meditation therapies in PD patients. METHODS: A literature search was conducted using PubMed, Embase, Cochrane Library, and ClinicalTrials.gov for randomized controlled trials comparing mindfulness and meditation therapies with control treatments in patients with PD. RESULTS: Nine articles involving eight trials were included, with a total of 337 patients. Our meta-analysis revealed that mindfulness and meditation therapies significantly improved Unified Parkinson's Disease Rating Scale-Part III score (mean difference [MD] = -6.31, 95% confidence interval [95% CI] = -8.57 to -4.05) and cognitive function (standard mean difference [SMD] = 0.62, 95% CI = 0.23 to 1.02). However, no significant differences were discovered between mindfulness therapies and control in gait velocity (MD = 0.05, 95% CI = -0.23 to 0.34), Parkinson's Disease Questionnaire-39 Summary Index (MD = 0.51, 95% CI = -1.12 to 2.14), activities of daily living (SMD = -1.65, 95% CI = -3.74 to 0.45), depression (SMD = -0.43, 95% CI = -0.97 to 0.11), anxiety (SMD = -0.80, 95% CI = -1.78 to 0.19), pain (SMD = 0.79, 95% CI = -1.06 to 2.63), or sleep disturbance (SMD = -0.67, 95% CI = -1.58 to 0.24). CONCLUSION: Mindfulness and meditation therapies may serve as complementary and alternative treatments for PD patients.

Higher Particulate Matter Deposition in Alveolar Region Could Accelerate Body Fat Accumulation in Obstructive Sleep Apnea.

We conducted a cross-sectional study to investigate associations of particulate matter (PM) of less than 2.5 µm in aerodynamic diameter (PM2.5) and PM deposition with nocturnal changes in body composition in obstructive sleep apnea (OSA) patients. A bioelectric impedance analysis was used to measure the pre- and postsleep body composition of 185 OSA patients. Annual exposure to PM2.5 was estimated by the hybrid kriging/land-use regression model. A multiple-path particle dosimetry model was employed to estimate PM deposition in lung regions. We observed that an increase in the interquartile range (IQR) (1 µg/m3) of PM2.5 was associated with a 20.1% increase in right arm fat percentage and a 0.012 kg increase in right arm fat mass in OSA (p < 0.05). We observed that a 1 µg/m3 increase in PM deposition in lung regions (i.e., total lung region, head and nasal region, tracheobronchial region, and alveolar region) was associated with increases in changes of fat percentage and fat mass of the right arm (ß coefficient) (p < 0.05). The ß coefficients decreased as follows: alveolar region > head and nasal region > tracheobronchial region > total lung region (p < 0.05). Our findings demonstrated that an increase in PM deposition in lung regions, especially in the alveolar region, could be associated with nocturnal changes in the fat percentage and fat mass of the right arm. PM deposition in the alveolar region could accelerate the body fat accumulation in OSA.

Continuous Positive Airway Pressure Reduces Plasma Neurochemical Levels in Patients with OSA: A Pilot Study.

Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aß42), and the product of the two (Aß42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAß42: 0.58 pg/mL; ΔAß42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aß42 decreased considerably in the CPAP group (ΔT-Tau: -2.22 pg/mL; ΔAß42 × T-Tau: -44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea-hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events.

Objectives: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

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.

Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers.

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers.

Neuromuscular Electrical Stimulation of Upper Limbs in Patients With Cerebral Palsy: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: The aim of the study is to assess the effects of neuromuscular electrical stimulation on the upper limbs of patients with cerebral palsy. DESIGN: We searched PubMed, Cochrane, Embase, and Scopus databases for randomized controlled trials examining the effects of neuromuscular electrical stimulation on the upper limbs of children with cerebral palsy. RESULTS: Eight randomized controlled trials ( N = 294) were included in the meta-analysis. Compared with traditional physical therapy, sensorimotor training and task-oriented training, constraint-induced movement therapy, dynamic bracing, and conventional robot-assisted therapy, neuromuscular electrical stimulation in combination with these therapies resulted in significantly greater functional scale scores (standardized mean difference = 0.80; 95% confidence interval = 0.54 to 1.06), muscle strength of upper limbs (standardized mean difference = 0.57; 95% confidence interval = 0.25 to 0.88), and spasticity of upper limbs (relative risk = 2.53; 95% confidence interval = 1.46 to 4.39; standardized mean difference = -0.18; 95% confidence interval = -0.29 to -0.06) but did not improve the wrist range of motion (standardized mean difference = 0.43; 95% confidence interval = -0.04 to 0.91). In addition, the effect of neuromuscular electrical stimulation on functional scale scores remained after 3-mo follow-up (standardized mean difference = 0.68; 95% confidence interval = 0.16 to 1.2). CONCLUSIONS: Neuromuscular electrical stimulation effectively improved hand function, muscle strength, and spasticity in patients with cerebral palsy.