The prevalence of obstructive sleep apnea hypopnea syndrome (OSAHS) is high. The development of telemedicine and mobile applications play an important role in the diagnosis and screening of OSAHS patients.
To evaluate the value of smartphone snoring analysis software Mianyun Sara in screening of Chinese adults with OSAHS.
One hundred and thirty patients〔mean age (49.7±17.4) years old, 70% male and 30% female, mean body mass index (28.2±5.0) kg/m2〕who were admitted to the Sleep Center of Peking University People's Hospital from April to December 2020 were selected and underwent overnight monitoring with Mianyun Sara and polysomnography (PSG) simultaneously. The relevant indicators generated by Mianyun Sara's automatic analysis and the relevant indicators interpreted by sleep professional technicians according to the recommended guidelines, the agreement between the apnea hypopnea index (AHI) derived from this method and PSG were evaluated, as well as the sensitivity and specificity of the diagnosis of OSAHS.
(1) The total sleep time (TST) monitored by Mianyun Sara was 523.67 (497.50, 542.64) min, and the TST monitored by PSG was 408.25 (364.25, 462.50) min, the difference was statistically significant (Z=-9.540, P<0.001) . The AHI monitored by Mianyun Sara was 15.83 (6.18, 27.49) times/h, and the AHI monitored by PSG was 18.25 (6.15, 35.68) times/h, the difference was statistically significant (Z=-2.601, P=0.009) . (2) There was a positive correlation between the AHI obtained by the two monitoring methods (r=0.645, P<0.001) . Bland-Altman analysis showed that the AHIs measured by Mianyun Sara and PSG were statistically consistent, with an average difference of -5.7 times/h, and the 95% consistency limit of (-40.5, 29.2) times/h. (3) Taking AHI≥5 times/h as the gold standard for the diagnosis of OSAHS, Mianyun Sara's optimal diagnostic value for OSAHS was AHI>8.34 times/h, with a corresponding sensitivity of 83.81% and a specificity of 92.00%. The area under the curve (AUC) was 0.91 (0.84, 0.95) , the positive predictive value (PPV) was 97.8%, and the negative predictive value (NPV) was 57.5%, at different AHI thresholds (5, 15, 30 times/h) , the sensitivity/specificity corresponding to the best diagnostic value were 83.8%/92.0%, 88.2%/74.1% and 64.9%/91.4%, respectively.
Mianyun Sara has a good screening value for adult OSAHS patients and there is close agreement between Mianyun Sara and PSG.
The pathogenesis of obstructive sleep apnea (OSA) has been explained from both anatomic and non-anatomic perspectives. Previous studies have indicated that OSA is most closely associated with anatomic factors related to upper airway obstruction, but its association with non-anatomic factors for upper airway obstruction has been increasingly understood and valued. The non-anatomic parameters for evaluating therapeutic effect include pharyngeal critical closing pressure, arousal threshold, loop gain and dilator muscle dysfunction, namely PALM. Monitoring and analyzing the weight of these four factors in the pathogenesis of OSA may contribute to the guidance of individualized treatment. Loop gain is a method for measuring the gain or sensitivity of negative feedback loop of respiratory control system to estimate the ventilation volume obtained by increasing the driving force of respiration to some extent. Higher loop gain may lead to hypocapnia and inhibition of upper airway respiratory drive, thereby aggravating the severity of OSA. We detailed a method for measuring loop gain and its clinical significance in patients with OSA.
Sleep research has a major part to play in facilitating the development of sleep medicine. In China, the development of sleep medicine started later compared with other medical disciplines, and related advances have been seldom reported.
To review the development of sleep research in China by analyzing sleep research projects supported by the National Natural Science Foundation of China (NSFC) , providing data for future development of sleep research.
Data were collected from the NSFC, including the supported projects regarding sleep-disordered breathing (code H0113) and sleep and sleep disorders (code H0916) as well as those with corresponding codes involving sleep or sleep disorder of Department of Health Sciences, and supported projects involving sleep or sleep disorder in Departments of Life Sciences. The number and composition of supported projects and funding amount, geographical, regional and institutional distribution of supported projects in terms of number and funding amount were statistically analyzed.
From 1988 to 2019, the sleep research projects supported by the NSFC numbered 399 in total. The number of these projects and the founding amount for them showed an increasing trend, particularly after 2010. The types of projects were gradually enriched, among which four were key projects, while no key talent projects had been supported. The geographical and institutional distribution of supported projects was uneven, presenting a tendency of aggregation in some specific regions and institutions, and a pattern of Matthew effect. Colleges and universities were the main body of sleep research. At present, sleep research is mainly based on basic experiments and clinical applications.
In general, the level and composition distribution of sleep research projects supported by the NSFC during the period demonstrated a trend of gradual enhancement, but still need improvements. To promote the development of sleep medicine, it is recommended to strengthen the reserve force via improving the top-level design and macro layout concerning NSFC supported projects, and to achieve early prevention, appropriate diagnosis and treatment of sleep disorders via taking advantage of interdisciplinary cooperation and integration.
Insomnia disorder is the most prevalent sleep disorder with high heterogeneityand can be divided into multiple subtypes. Significant differences may be found in symptoms, pathophysiology and therapeutic responsesacross its subtypes. Current classification methods for subtypes of insomnia disorder are mainly based on clinical characteristics of insomnia, subjective and objective sleep duration, and non-insomnia-related clinical characteristics.We systematically and comprehensively discussed the advantages, limitations and clinical significance of the above-mentioned three classification methods, and the differences in pathophysiological mechanism, treatment responses and clinical outcomes according to subtypes classified by each of the three, which will contribute to making an appropriatediagnosis, formulating an individualized treatment measure and developing more practical and operable classification methodsregardinginsomnia disorder, and may be seen as directions for deepening and meticulously exploring the pathophysiological mechanism of different subtypes of insomnia disorder.
Many factors are associated with hypertension, the most prevalent chronic disease, among which, the association of sleep disturbance and hypertension has received wide attention as sleep medicine advances rapidly in recent years. However, relevant studies on sleep disturbance and hypertension have some limitations, and there is no bibliometric analysis of hotspots about sleep disturbance and hypertension.
To review and summarize the research hotspots and trends of literature related to sleep disturbances and hypertension.
Studies about sleep disturbance and hypertension were searched in Web of Science Core Collection from inception to June 30, 2021 using "hypertension" and "sleep disturbance" "insomnia" "sleep deprivation" "sleep fragmentation" and "short-term sleep" as subject headings. CiteSpace 5.7.R5W was used for visual analysis.
In total, 4 589 studies were included for analysis. The number of studies generally showed an increasing trend, with a peak in 2018, and a rapider growth rate between 2011 and 2021. The top 10 most frequently used keywords in the studies published between 2011 and 2021 were hypertension, blood pressure, prevalence, obstructive sleep apnea, risk factor, sleep, cardiovascular disease, positive airway pressure, obesity and insomnia. The tag clusters were sleep time, sleep quality, sleep-disordered breathing, obstructive sleep apnea, sleep apnea, insomnia, stress, sleep, epidemiology, heart failure and symptoms. Keyword clustering analysis revealed that major directions in the studies published between 2011 and 2021 were: (1) the association of sleep-disordered breathing, especially obstructive sleep apnea, and hypertension; (2) the association of sleep time and blood pressure; (3) the association of sleep quality and blood pressure. The most frequently cited studies were mainly about sleep apnea, obstructive sleep apnea and short-term sleep. REDLINE was the most prolific author, and the largest group of authors was formed with her as the core. The US was the most prolific country, and the most prolific institution was the University of Pittsburgh.
The research on sleep disturbance and hypertension had become increasingly popular. The research hotspots of this field had changed greatly in 2011 and 2018. The effects of obstructive sleep apnea and sleep duration on hypertension were the mostly focused hotspots.
Sleep problems are increasingly common in residents with the acceleration of pace of life. Studies have shown that sleep duration is associated with chronic diseases such as hypertension and diabetes, but there is a lack of research on its association with hyperuricemia.
To assess the association and its dose-response level between sleep duration and hyperuricemia.
Data stemmed from the 2019 surveillance of epidemiology and risk factors of chronic diseases in adult residents in Haidian District, Beijing, involving individuals aged 18-79 years old, with an experience of living in Haidian District at least six months. The information was obtained via a face-to-face questionnaire survey, including the following aspects: demographics (sex, age, education level, marital status, occupation) , lifestyle factors (smoking and drinking, physical activity level, sleep duration) , history of chronic diseases (hypertension and diabetes) , height, weight, blood pressure, laboratory indices (fasting blood glucose, serum uric acid, and serum creatinine) . A multivariable Logistic regression model was used to assess the association between sleep duration and the risk of hyperuricemia, whose dose-response relationship was analysed using restricted cubic spline regression.
A total of 5 380 people were enrolled, with an average age of (46.9±16.0) years and an average sleep duration of (7.24±1.16) hours. Univariate Logistic regression analysis showed that, compared with those with 7-9 hours of sleep duration, the risk of hyperuricemia increased in those with less than 7 hours of sleep duration and in those with greater than 9 hours of sleep duration 〔OR (95%CI) =1.30 (1.12, 1.51) ; OR (95%CI) =1.48 (1.15, 1.89) 〕. After adjusting for age, gender, education level, marital status, occupation, smoking, drinking, physical activity level, BMI, hypertension, diabetes, and serum creatinine, the risk of hyperuricemia still increased in those with less than 7 hours of sleep duration and in those with greater than 9 hours of sleep duration 〔OR (95%CI) =1.37 (1.17, 1.62) ; OR (95%CI) =1.39 (1.07, 1.81) 〕. Restricted cubic spline regression analysis showed that sleep duration had a U-shaped association with hyperuricemia (non-linear test, χ2=27.530, P<0.001) .
Too longer or shorter sleep duration was a factor responsible for increased risk of hyperuricemia among adults in Haidian District of Beijing.
