Nature and Science of Sleep最新文献

{"title":"Differences and Risk Factors of Peripheral Blood Immune Cells in Patients with Obstructive Sleep Apnea.","authors":"Ying Jiang, Chuankai Lin, Min Xu, Taiwen Zhu, Xuhong Li, Wei Wang","doi":"10.2147/NSS.S458098","DOIUrl":"10.2147/NSS.S458098","url":null,"abstract":"<p><strong>Introduction: </strong>Obstructive sleep apnea (OSA) is a respiratory disorder characterized by chronic intermittent hypoxia and fragmented sleep, leading to inflammatory response and oxidative stress. However, the differences in immune inflammatory response in OSA patients with different severity remain unclear.</p><p><strong>Purpose: </strong>This study aims to examine the differences in peripheral blood immune cells and their risk factors in OSA patients.</p><p><strong>Patients and methods: </strong>A total of 277 snoring patients from the Sleep Respiratory Disorder Monitoring Center of Zhongnan Hospital of Wuhan University were recruited in this study. According to the diagnosis and severity criteria of OSA, the included patients were further divided into simple snoring, mild, moderate, and severe groups. Peripheral blood immune cell counts including white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, red blood cells, platelets, and polysomnography indicators were collected from the patients.</p><p><strong>Results: </strong>Compared with simple snoring patients, the OSA patients had increased circular monocyte and basophil count levels. In addition, correlation analysis results indicated that monocyte count was positively associated with chronic obstructive pulmonary disease (COPD), smoking, apnea-hypopnea index (AHI), the longest apnea duration, and Oxygen desaturation index (ODI), and negatively correlated with average SpO₂ in snoring patients. Finally, multiple linear regression analysis revealed that AHI, COPD, smoking, and maximum heart rate were independent predictors of monocyte count.</p><p><strong>Conclusion: </strong>OSA patients had a significant increase in their peripheral blood monocyte count. AHI, COPD, smoking, and maximum heart rate were risk factors for increased peripheral blood monocyte count in OSA patients. These findings suggest that peripheral blood monocytes can be considered an inflammatory biomarker of OSA.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141331473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Changed Nocturnal Sleep Structure and Higher Anxiety, Depression, and Fatigue in Patients with Narcolepsy Type 1.","authors":"Jieyang Yu, Yanan Zhang, Lijia Cai, Qingqing Sun, Wanru Li, Junfang Zhou, Jianmin Liang, Zan Wang","doi":"10.2147/NSS.S452665","DOIUrl":"10.2147/NSS.S452665","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate nocturnal sleep structure and anxiety, depression, and fatigue in patients with narcolepsy type 1 (NT1).</p><p><strong>Methods: </strong>Thirty NT1 patients and thirty-five healthy controls were enrolled and evaluated using the Epworth sleepiness scale (ESS), Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, Fatigue Severity Scale (FSS), polysomnography, multiple sleep latency test, and brain function state monitoring. Statistical analyses were performed using SPSS Statistics for Windows, version 23.0. Benjamini-Hochberg correction was performed to control the false discovery rate.</p><p><strong>Results: </strong>Apart from typical clinical manifestations, patients with NT1 are prone to comorbidities such as nocturnal sleep disorders, anxiety, depression, and fatigue. Compared with the control group, patients with NT1 exhibited abnormal sleep structure, including increased total sleep time (<i>P</i> _adj=0.007), decreased sleep efficiency (<i>P</i> _adj=0.002), shortening of sleep onset latency (<i>P</i> _adj<0.001), elevated wake after sleep onset (<i>P</i> _adj=0.002), increased N1% (<i>P</i> _adj=0.006), and reduced N2%, N3%, and REM% (<i>P</i> _adj=0.007, <i>P</i> _adj<0.001, <i>P</i> _adj=0.013). Thirty-seven percent of patients had moderate to severe obstructive sleep apnea-hypopnea syndrome. And sixty percent of patients were complicated with REM sleep without atonia. Patients with NT1 displayed increased anxiety propensity (<i>P</i> _adj<0.001), and increased brain fatigue (<i>P</i> _adj=0.020) in brain function state monitoring. FSS scores were positively correlated with brain fatigue (<i>P</i> _adj<0.001) and mean sleep latency was inversely correlated with FSS scores and brain fatigue (<i>P</i> _adj=0.013, <i>P</i> _adj=0.029). Additionally, ESS scores and brain fatigue decreased after 3 months of therapy (<i>P</i>=0.012, <i>P</i>=0.030).</p><p><strong>Conclusion: </strong>NT1 patients had abnormal nocturnal sleep structures, who showed increased anxiety, depression, and fatigue. Excessive daytime sleepiness and fatigue improved after 3 months of treatment with methylphenidate hydrochloride prolonged-release tablets in combination with venlafaxine.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11170032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141317836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Shared Genetic Architecture Between Obstructive Sleep Apnea and Body Mass Index.","authors":"Peng Zhou, Ling Li, Zehua Lin, Xiaoping Ming, Yiwei Feng, Yifan Hu, Xiong Chen","doi":"10.2147/NSS.S459136","DOIUrl":"10.2147/NSS.S459136","url":null,"abstract":"<p><strong>Purpose: </strong>The reciprocal comorbidity of obstructive sleep apnea (OSA) and body mass index (BMI) has been observed, yet the shared genetic architecture between them remains unclear. This study aimed to explore the genetic overlaps between them.</p><p><strong>Methods: </strong>Summary statistics were acquired from the genome-wide association studies (GWASs) on OSA (N_case = 41,704; N_control = 335,573) and BMI (N_overall = 461,460). A comprehensive genome-wide cross-trait analysis was performed to quantify global and local genetic correlation, infer the bidirectional causal relationships, detect independent pleiotropic loci, and investigate potential comorbid genes.</p><p><strong>Results: </strong>A positive significant global genetic correlation between OSA and BMI was observed (<i>r</i> _g = 0.52, <i>P</i> = 2.85e-122), which was supported by three local signal. The Mendelian randomization analysis confirmed bidirectional causal associations. In the meta-analysis of cross-traits GWAS, a total of 151 single-nucleotide polymorphisms were found to be pleiotropic between OSA and BMI. Additionally, we discovered that the genetic association between OSA and BMI is concentrated in 12 brain regions. Finally, a total 134 expression-tissue pairs were observed to have a significant impact on both OSA and BMI within the specified brain regions.</p><p><strong>Conclusion: </strong>Our comprehensive genome-wide cross-trait analysis indicates a shared genetic architecture between OSA and BMI, offering new perspectives on the possible mechanisms involved.</p>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11166156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning Model for Predicting Sleep and Wakefulness Based on Accelerometry, Skin Temperature and Contextual Information","authors":"Aleksej Logacjov, Eivind Schjelderup Skarpsno, Atle Kongsvold, Kerstin Bach, Paul Jarle Mork","doi":"10.2147/nss.s452799","DOIUrl":"https://doi.org/10.2147/nss.s452799","url":null,"abstract":"<strong>Purpose:</strong> Body-worn accelerometers are commonly used to estimate sleep duration in population-based studies. However, since accelerometry-based sleep/wake-scoring relies on detecting body movements, the prediction of sleep duration remains a challenge. The aim was to develop and evaluate the performance of a machine learning (ML) model to predict accelerometry-based sleep duration and to explore if this prediction can be improved by adding skin temperature data, circadian rhythm based on the estimated midpoint of sleep, and cyclic time features to the model.<br/><strong>Patients and Methods:</strong> Twenty-nine adults (17 females), mean (SD) age 40.2 (15.0) years (range 17– 70) participated in the study. Overnight polysomnography (PSG) was recorded in a sleep laboratory or at home along with body movement by two accelerometers with an embedded skin temperature sensor (AX3, Axivity, UK) positioned at the low back and thigh. The PSG scoring of sleep/wake was used as ground truth for training the ML model.<br/><strong>Results:</strong> Based on pure accelerometer data input to the ML model, the specificity and sensitivity for predicting sleep/wake was 0.52 (SD 0.24) and 0.95 (SD 0.03), respectively. Adding skin temperature data and contextual information to the ML model improved the specificity to 0.72 (SD 0.20), while sensitivity remained unchanged at 0.95 (SD 0.05). Correspondingly, sleep overestimation was reduced from 54 min (228 min, limits of agreement range [LoAR]) to 19 min (154 min LoAR).<br/><strong>Conclusion:</strong> An ML model can predict sleep/wake periods with excellent sensitivity and moderate specificity based on a dual-accelerometer set-up when adding skin temperature data and contextual information to the model.<br/><br/><strong>Keywords:</strong> actigraphy, epidemiology, sedentary behaviors, sleep quality, supervised machine learning, support vector machines<br/>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sleep Habits and Disturbances Among Tunisian Adults: A Cross-Sectional Online Survey","authors":"Sameh Msaad, Nouha Ketata, Nesrine Kammoun, Rahma Gargouri, Rim Khemakhem, Sourour Abid, Saeb Bader, Sabrine Efidha, Narjes Abid, Jamel El Ghoul, Imen Sahnoun, Hazem Altalaa, Jihen Jdidi, Mohamed Jlidi, Nadia Keskes Boudaouara, Imen Gargouri, Najla Bahloul, Samy Kammoun","doi":"10.2147/nss.s456879","DOIUrl":"https://doi.org/10.2147/nss.s456879","url":null,"abstract":"<strong>Background:</strong> Sleep quality and disturbances have gained heightened scholarly attention due to their well-established association with both mental and physical health. This study aims to assess sleep-wake habits and disturbances in Tunisian adults.<br/><strong>Methodology:</strong> This cross-sectional study employed an online questionnaire to assess 3074 adults ≥ 18 years. Primary outcomes, including sleep quality, daytime vigilance, mood, and subjective well-being, were measured using validated questionnaires [the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI), the Epworth Sleepiness Scale (ESS), the Patient Health Questionnaire (PHQ)-9, and the World Health Organisation-Five Well-Being Index (WHO-5)].<br/><strong>Results:</strong> Less than two-thirds (n= 1941; 63.1%) of participants were females and the mean age was 36.25± 13.56. The prevalence of poor sleep quality was 53.8% when defined as a PSQI &gt; 5. The prevalence of insomnia, short sleep duration, long sleep duration, EDS, severe depression, and poor well-being were 14.5%, 34.7%, 12.3%, 32.4%, 7.4%, and 40.2%, respectively. Some factors were associated with an increased likelihood of poor sleep quality, including female gender, chronic hypnotics use, internet use close to bedtime, daily time spent on the internet &gt; 3 hours, smoking, university- level education, nocturnal work, severe depression, impaired well-being status, insomnia, and EDS.<br/><strong>Conclusion:</strong> The high prevalence of sleep-wake disturbances among Tunisian adults emphasizes the need for an appropriate screening strategy for high-risk groups. Individuals with unhealthy habits and routines were significantly more likely to experience these kinds of disturbances. Consequently, there is a pressing need for educational programs on sleep to foster healthier sleep patterns.<br/><br/>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal Relationships Between Circulating Inflammatory Proteins and Obstructive Sleep Apnea: A Bidirectional Mendelian Randomization Study","authors":"Zhengjie Chen, Jinjie Zeng, Xiang Pei, Jingjing Zhao, Fang Zhao, Guoxin Zhang, Kexin Liang, Jiarong Li, Xiaoyun Zhao","doi":"10.2147/nss.s458637","DOIUrl":"https://doi.org/10.2147/nss.s458637","url":null,"abstract":"","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Mediation/Moderation Effects of Gut Microbiota on Sleep Quality and Primary Liver Cancer: A Mendelian Randomization and Case–Control Study","authors":"Yi Yang, Jingxian Wang, Qing Su, Jinhuan Yang, Zhiyuan Bo, Chongming Zheng, Yitong Xie, Kaiwen Chen, Juejin Wang, Gang Chen, Yi Wang","doi":"10.2147/nss.s458491","DOIUrl":"https://doi.org/10.2147/nss.s458491","url":null,"abstract":"<strong>Background:</strong> Primary liver cancer (PLC) is a fatal malignancy, sleep quality and gut microbiota were shown to be associated with PLC. However, the mechanism of how sleep quality affects PLC is unclear. This study aims to investigate the mediation/moderation effects of gut microbiota on sleep quality and the occurrence of PLC.<br/><strong>Methods:</strong> The causality of sleep quality and the occurrence of PLC was detected through the Mendelian randomization (MR) analysis based on the data including 305,359 individuals (Finland Database) and 456,348 participants (UK Biobank). The primary method used for MR analysis was inverse-variance weighted analysis. Gut microbiota’ mediation/moderation effects were uncovered in the case–control study including 254 patients with PLC and 193 people with benign liver diseases through the mediation/moderation effect analyses. People’s sleep quality was evaluated through the Pittsburgh sleep quality index (PSQI).<br/><strong>Results:</strong> Poor sleep quality could lead to PLC through the MR analysis (<em>P</em> = 0.026). The case–control study uncovered that <em>Actinobacteria</em> had mediation effects on the relationship between PSQI score, self-sleep quality, and the occurrence of PLC (<em>P</em> = 0.048, <em>P</em> = 0.046). <em>Actinobacteria</em> and <em>Bifidobacterium</em> could inhibit the development of PLC caused by short night sleep duration (<em>P</em> = 0.021, <em>P</em> = 0.022). <em>Erysipelotrichales</em> could weaken the influence of daytime dysfunction on PLC (<em>P</em> = 0.033). <em>Roseburia</em> modulated the contribution of nocturnal insomnia and poor sleep quality to PLC (<em>P</em> = 0.009, <em>P</em> = 0.017).<br/><strong>Conclusion:</strong> Poor sleep quality was associated with PLC. Gut microbiota’ mediation/moderation effects on poor sleep quality and the occurrence of PLC prompted an insightful idea for the prevention of PLC.<br/><br/>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141192609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data","authors":"Jian Cui, Yunliang Sun, Haifeng Jing, Qiang Chen, Zhihao Huang, Xin Qi, Hao Cui","doi":"10.2147/nss.s463897","DOIUrl":"https://doi.org/10.2147/nss.s463897","url":null,"abstract":"Purpose: Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency. Methods: The study involved 50 normal and 100 obstructive sleep apnea–hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM). Results: The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%. Conclusion: A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141412246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Association Between Sleep Problems and Attentional Network Functions in Patients with Self-Limited Epilepsy with Centrotemporal Spikes","authors":"Nan Jiang, Chunmei Yang, Jia Le Wang, Xiao Ye, Bin Yang","doi":"10.2147/nss.s460558","DOIUrl":"https://doi.org/10.2147/nss.s460558","url":null,"abstract":"Purpose: To investigate sleep problems in children with self-limited epilepsy with central temporal spiking (SeLECTS) and to assess the relationship between sleep problems and attention network dysfunction. Patients and methods: 107 children 6–14 years of age with SeLECTS and 90 age-and sex-matched healthy controls were recruited for this study. The sleep status of these participants was evaluated using the Children’s Sleep Habits Questionnaire (CSHQ), while attentional network function was assessed with the attention network function test (ANT). Results: Together, these analyses revealed that children with SeLECTS exhibited higher total CSHQ scores and sleep disorder incidence relative to healthy controls (P< 0.001). Children with SeLECTS had higher scores in delayed sleep onset, sleep duration, night awakenings, parasomnias, daytime sleepiness and sleep anxiety (P<0.01). Total CSHQ scores were negatively correlated with average ANT correct rates ( ρ = −0.253, P<0.01), while they were positively correlated with total reaction time ( ρ =0.367, P<0.01) and negatively correlated with the efficiency of the alerting and executive control networks ( ρ =−0.344 P<0.01; ρ =−0.418 P<0.01). Conclusion: Children with SeLECTS face a higher risk of experiencing sleep disorders relative to age-matched healthy children, while also demonstrating that the magnitude of the impairment of attentional network function in these children is positively correlated with sleep disorder severity. Thus, the prognosis and quality of life of children with SeLECTS can be improved by interventions addressing sleep disorders.","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141412825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Association of High Arousal Threshold with Hypertension and Diabetes in Obstructive Sleep Apnea","authors":"Donghao Wang, Yuting Zhang, Qiming Gan, Xiaofen Su, Haojie Zhang, Yanyan Zhou, Zhiyang Zhuang, Jingcun Wang, Yutong Ding, Dongxing Zhao, Nuofu Zhang","doi":"10.2147/nss.s457679","DOIUrl":"https://doi.org/10.2147/nss.s457679","url":null,"abstract":"<strong>Objective:</strong> 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).<br/><strong>Methods:</strong> 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).<br/><strong>Results:</strong> 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, <em>P</em> &lt; 0.01) and 1.45 (95% CI = 1.01– 2.08, <em>P</em> &lt; 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, <em>P</em> &lt; 0.05). The significant association was not observed in AHI with increasing prevalent diabetes.<br/><strong>Conclusion:</strong> 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.<br/><br/><strong>Keywords:</strong> obstructive sleep apnea, arousal threshold, apnea–hypopnea index, hypertension, diabetes<br/>","PeriodicalId":18896,"journal":{"name":"Nature and Science of Sleep","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141192559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}