Purpose: This study aimed to evaluate nocturnal sleep structure and anxiety, depression, and fatigue in patients with narcolepsy type 1 (NT1).
Methods: 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.
Results: 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 (Padj=0.007), decreased sleep efficiency (Padj=0.002), shortening of sleep onset latency (Padj<0.001), elevated wake after sleep onset (Padj=0.002), increased N1% (Padj=0.006), and reduced N2%, N3%, and REM% (Padj=0.007, Padj<0.001, Padj=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 (Padj<0.001), and increased brain fatigue (Padj=0.020) in brain function state monitoring. FSS scores were positively correlated with brain fatigue (Padj<0.001) and mean sleep latency was inversely correlated with FSS scores and brain fatigue (Padj=0.013, Padj=0.029). Additionally, ESS scores and brain fatigue decreased after 3 months of therapy (P=0.012, P=0.030).
Conclusion: 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.
{"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> <sub>adj</sub>=0.007), decreased sleep efficiency (<i>P</i> <sub>adj</sub>=0.002), shortening of sleep onset latency (<i>P</i> <sub>adj</sub><0.001), elevated wake after sleep onset (<i>P</i> <sub>adj</sub>=0.002), increased N1% (<i>P</i> <sub>adj</sub>=0.006), and reduced N2%, N3%, and REM% (<i>P</i> <sub>adj</sub>=0.007, <i>P</i> <sub>adj</sub><0.001, <i>P</i> <sub>adj</sub>=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> <sub>adj</sub><0.001), and increased brain fatigue (<i>P</i> <sub>adj</sub>=0.020) in brain function state monitoring. FSS scores were positively correlated with brain fatigue (<i>P</i> <sub>adj</sub><0.001) and mean sleep latency was inversely correlated with FSS scores and brain fatigue (<i>P</i> <sub>adj</sub>=0.013, <i>P</i> <sub>adj</sub>=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}
Purpose: 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.
Methods: Summary statistics were acquired from the genome-wide association studies (GWASs) on OSA (Ncase = 41,704; Ncontrol = 335,573) and BMI (Noverall = 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.
Results: A positive significant global genetic correlation between OSA and BMI was observed (rg = 0.52, P = 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.
Conclusion: Our comprehensive genome-wide cross-trait analysis indicates a shared genetic architecture between OSA and BMI, offering new perspectives on the possible mechanisms involved.
目的：阻塞性睡眠呼吸暂停（OSA）与体重指数（BMI）之间存在互为并发症的关系，但两者之间的共同遗传结构仍不清楚。本研究旨在探索它们之间的遗传重叠：从有关 OSA（Ncase = 41,704; Ncontrol = 335,573 ）和 BMI（Noverall = 461,460 ）的全基因组关联研究（GWASs）中获得摘要统计。为了量化全局和局部遗传相关性、推断双向因果关系、检测独立的多效基因位点并研究潜在的合并基因，我们进行了全面的全基因组跨性状分析：结果：观察到 OSA 与体重指数之间存在明显的整体遗传正相关（r g = 0.52，P = 2.85e-122），并得到三个局部信号的支持。孟德尔随机分析证实了双向因果关系。在跨性状 GWAS 的荟萃分析中，共发现 151 个单核苷酸多态性在 OSA 和 BMI 之间具有多向性。此外，我们还发现 OSA 和 BMI 之间的遗传关联主要集中在 12 个脑区。最后，在指定的脑区中，共观察到134对表达-组织对OSA和BMI有显著影响：结论：我们的全基因组跨性状综合分析表明，OSA 和 BMI 之间存在共同的遗传结构，为研究其中可能的机制提供了新的视角。
{"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<sub>case</sub> = 41,704; N<sub>control</sub> = 335,573) and BMI (N<sub>overall</sub> = 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> <sub>g</sub> = 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}
Purpose: 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. Patients and Methods: 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. Results: 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). Conclusion: 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.
{"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}
Background: 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. Methodology: 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)]. Results: 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 > 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 > 3 hours, smoking, university- level education, nocturnal work, severe depression, impaired well-being status, insomnia, and EDS. Conclusion: 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.
{"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 > 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 > 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}
Yi Yang, Jingxian Wang, Qing Su, Jinhuan Yang, Zhiyuan Bo, Chongming Zheng, Yitong Xie, Kaiwen Chen, Juejin Wang, Gang Chen, Yi Wang
Background: 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. Methods: 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). Results: Poor sleep quality could lead to PLC through the MR analysis (P = 0.026). The case–control study uncovered that Actinobacteria had mediation effects on the relationship between PSQI score, self-sleep quality, and the occurrence of PLC (P = 0.048, P = 0.046). Actinobacteria and Bifidobacterium could inhibit the development of PLC caused by short night sleep duration (P = 0.021, P = 0.022). Erysipelotrichales could weaken the influence of daytime dysfunction on PLC (P = 0.033). Roseburia modulated the contribution of nocturnal insomnia and poor sleep quality to PLC (P = 0.009, P = 0.017). Conclusion: 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.
{"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}
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.
{"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}
Nan Jiang, Chunmei Yang, Jia Le Wang, Xiao Ye, Bin Yang
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.
{"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}
Objective: Compared to low arousal threshold (AT), high AT is an easily overlooked characteristic for obstructive sleep apnea (OSA) severity estimation. This study aims to evaluate the relationship between high AT, hypertension and diabetes in OSA, compared to those with apnea–hypopnea index (AHI). Methods: A total of 3400 adults diagnosed with OSA were retrospectively recruited. Propensity score matching (PSM) was conducted to further categorize these patients into the low and high AT groups based on the strategy established by previous literature. The different degrees of AHI and quantified AT (AT score) were subsequently measured. The correlation of AT and AHI with the occurrence of various comorbidities in OSA was estimated by logistic regression analysis with odds ratio (OR). Results: After PSM, 938 pairs of patients arose. The median AT score of high and low AT group was 21.7 and 12.2 scores, and the adjusted OR of high AT for hypertension and diabetes was 1.31 (95% CI = 1.07– 1.62, P < 0.01) and 1.45 (95% CI = 1.01– 2.08, P < 0.05), respectively. Compared to low AT score group, the OR significantly increased in patients with very high AT score (30 ≤ AT score), especially for diabetes (OR = 1.79, 95% CI = 1.02– 3.13, P < 0.05). The significant association was not observed in AHI with increasing prevalent diabetes. Conclusion: Higher AT is significantly associated with increased prevalence of hypertension and diabetes in patients with OSA. Compared with AHI, AT score is a potentially comprehensive indicator for better evaluating the relationship between OSA and related comorbidities.
目的：与低唤醒阈值（AT）相比，高唤醒阈值是阻塞性睡眠呼吸暂停（OSA）严重程度评估中一个容易被忽视的特征。本研究旨在评估与呼吸暂停-低通气指数（AHI）相比，高唤醒阈、高血压和糖尿病与 OSA 之间的关系：方法：回顾性招募了 3400 名确诊为 OSA 的成年人。方法：共回顾性招募了 3400 名确诊为 OSA 的成人，并根据以往文献确定的策略进行倾向得分匹配（PSM），进一步将这些患者分为低 AT 组和高 AT 组。随后测量了不同程度的 AHI 和量化的 AT（AT 评分）。结果显示，在进行 PSM 分析后，938 对患者的 AT 和 AHI 与 OSA 中各种合并症的发生率之间存在相关性：结果：经过 PSM，共出现 938 对患者。高AT组和低AT组的中位AT评分分别为21.7分和12.2分，高AT组高血压和糖尿病的调整OR分别为1.31（95% CI = 1.07- 1.62，P< 0.01）和1.45（95% CI = 1.01- 2.08，P< 0.05）。与低AT评分组相比，极高AT评分（30≤AT评分）患者的OR显著增加，尤其是糖尿病患者（OR = 1.79，95% CI = 1.02- 3.13，P < 0.05）。结论：AT越高，糖尿病患病率越高：结论：在 OSA 患者中，AT 值越高，高血压和糖尿病的患病率越高。关键词：阻塞性睡眠呼吸暂停；唤醒阈值；呼吸暂停-低通气指数；高血压；糖尿病
{"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> < 0.01) and 1.45 (95% CI = 1.01– 2.08, <em>P</em> < 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> < 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}
Yuanhang Pan, Di Zhao, Xinbo Zhang, Na Yuan, Lei Yang, Yuanyuan Jia, Yanzhao Guo, Ze Chen, Zezhi Wang, Shuyi Qu, Junxiang Bao, Yonghong Liu
Background: Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1. Objective: The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early. Methods: Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP). Results: Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA. Conclusion: The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.
背景：白天过度嗜睡（EDS）是阻塞性睡眠呼吸暂停（OSA）和1型嗜睡症（NT1）的常见症状，而后者可能总是被忽视。机器学习（ML）模型可以实现对这些病症的早期检测，但从未应用于 NT1 的诊断：该研究旨在开发 ML 预测模型，以帮助非睡眠专科临床医生及早发现 OSA 患者合并 NT1 的高概率：方法：共收集并分析了三个睡眠中心 246 名 OSA 患者的临床特征，以建立九个 ML 模型。特征选择采用 LASSO 回归。采用接收者操作曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）等各种指标来评估和比较这些多重L模型的性能。通过夏普利相加解释（SHAP）证明了模型的可解释性：结果：根据对 AUC、DCA 和校准曲线的分析，与其他机器学习（ML）模型相比，梯度提升机（GBM）模型表现出更优越的性能。按特征重要性排序，GBM 模型使用的前五个特征分别是发病年龄、总肢体运动指数、睡眠潜伏期、非快速眼动（REM）睡眠第二阶段和 OSA 的严重程度：该研究为早期识别 OSA 患者的 NT1 提供了一个简单可行的基于 ML 的筛查模型，值得在更广泛的临床实践中进一步验证。
{"title":"Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness","authors":"Yuanhang Pan, Di Zhao, Xinbo Zhang, Na Yuan, Lei Yang, Yuanyuan Jia, Yanzhao Guo, Ze Chen, Zezhi Wang, Shuyi Qu, Junxiang Bao, Yonghong Liu","doi":"10.2147/nss.s456903","DOIUrl":"https://doi.org/10.2147/nss.s456903","url":null,"abstract":"<strong>Background:</strong> Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1.<br/><strong>Objective:</strong> The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early.<br/><strong>Methods:</strong> Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP).<br/><strong>Results:</strong> Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA.<br/><strong>Conclusion:</strong> The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.<br/><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":"141192558","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}
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