Association between slow wave sleep and blood pressure in insomnia.

STUDY OBJECTIVES: The majority of patients with insomnia exhibit abnormal sleep in objective testing (e.g., decreased sleep duration, decreased slow wave sleep (SWS). Previous studies have suggested that some of these objective measures of poor sleep, such as decreased sleep duration, are associated with a higher risk of hypertension in insomnia. We examined the relationship between SWS and morning and evening blood pressure (BP) levels in patients with clinically diagnosed insomnia. METHODS: A total of 229 normal sleepers and 1378 insomnia patients were included in this study. Insomnia was defined based on standard diagnostic criteria with symptoms lasting ≥6 months. All subjects underwent in-laboratory polysomnography. Patients were classified into quartiles of percent SWS. Evening and morning hypertension were defined using BP measurements taken in the evening before and in the morning after polysomnography, respectively. Multivariable logistic regression models were used to assess the relationship between insomnia, SWS and hypertension. RESULTS: Insomniacs with <3.5% SWS (OR 3.27, 95% CI 1.31-7.66) and those with 3.5-10.2% SWS (OR 2.38, 95% CI 1.28-5.91) had significantly greater odds of morning hypertension compared to normal sleepers. No associations were seen in insomnia with 10.2-15.8% SWS and with >15.8% SWS. Significant effect modifications by sex (p=0.043) were found, as decreased SWS was associated with morning hypertension only in men. Odds of evening hypertension were not significantly associated with SWS. CONCLUSION: Decreased SWS is associated with morning hypertension in a dose-dependent manner in insomnia, especially in men.

Gender-specific associations between sleep stages and cardiovascular risk factors.

STUDY OBJECTIVES: Sleep characteristics are associated with cardiovascular disease (CVD) risk and both sleep and CVD risk vary by gender. Our objective was to examine associations between polysomnographic sleep characteristics and CVD risk after excluding moderate-severe sleep apnea, and whether gender modifies these associations. METHODS: This was a cross-sectional study with at-home polysomnography in adults in Brazil (n= 1,102 participants with apnea-hypopnea index (AHI)<15 events/hour). Primary exposures were N3, REM, wake after sleep onset (WASO), arousal index (AI) and AHI, and outcomes were blood pressure (BP) and lipid levels. RESULTS: Associations between sleep and BP varied by gender. In women, more N3 was associated with lower systolic BP (-0.40 mmHg per 10 minutes, 95%CI -0.71, -0.09), lower diastolic BP (-0.29 mmHg per 10 minutes, 95%CI -0.50, -0.07), and lower odds of hypertension (OR 0.94, 95%CI 0.89, 0.98). In men, more WASO was associated with higher systolic BP (0.41 mmHg per 10 minutes, 95%CI 0.08, 0.74) and higher odds of hypertension (OR 1.07, 95%CI 1.01, 1.14). No interactions by gender were observed for lipids. More WASO was associated with lower total cholesterol (-0.71 per 10 minutes, 95%CI -1.37, -0.05). Higher AHI was associated with higher total cholesterol (+0.97 per event/hour, 95%CI 0.24, 1.70) and higher LDL (+0.84 per event/hour, 95%CI 0.04, 1.64). CONCLUSIONS: N3 is more strongly associated with BP in women, which is consistent with other studies demonstrating gender differences in BP control and CVD risk and adds a novel risk factor. Longitudinal and interventional studies are required to determine whether changes in N3 result in BP changes.

Strategic naps in automated driving - Sleep architecture predicts sleep inertia better than nap duration.

At higher levels of driving automation, drivers can nap during parts of the trip but must take over control in others. Awakening from a nap is marked by sleep inertia which is tackled by the NASA nap paradigm in aviation: Strategic on-flight naps are restricted to 40 min to avoid deep sleep and therefore sleep inertia. For future automated driving, there are currently no such strategies for addressing sleep inertia. Given the disparate requirements, it is uncertain whether the strategies derived from aviation can be readily applied to automated driving. Therefore, our study aimed to compare the effects of restricting the duration of nap opportunities following the NASA nap paradigm to the effects of sleep architecture on sleep inertia in takeover scenarios in automated driving. In our driving simulator study, 24 participants were invited to sleep during three automated drives. They were awakened after 20, 40, or 60 min and asked to manually complete an urban drive. We assessed how napping duration, last sleep stage before takeover, and varying proportions of light, stable, and deep sleep influenced self-reported sleepiness, takeover times, and the number of driving errors. Takeover times increased with nap duration, but sleepiness and driving errors did not. Instead, all measures were significantly influenced by sleep architecture. Sleepiness increased after awakening from light and stable sleep, and takeover times after awakening from light sleep. Takeover times also increased with higher proportions of stable sleep. The number of driving errors was significantly increased with the proportion of deep sleep and after awakenings from stable and deep sleep. These results suggest that sleep architecture, not nap duration, is crucial for predicting sleep inertia. Therefore, the NASA nap paradigm is not suitable for driving contexts. Future driver monitoring systems should assess the sleep architecture to predict and prevent sleep inertia.

Profiling the sleep architecture of ageing adults using a seven-state continuous-time Markov model.

Sleep is a complex biological process regulated by networks of neurons and environmental factors. As one falls asleep, neurotransmitters from sleep-wake regulating neurones work in synergy to control the switching of different sleep states throughout the night. As sleep disorders or underlying neuropathology can manifest as irregular switching, analysing these patterns is crucial in sleep medicine and neuroscience. While hypnograms represent the switching of sleep states well, current analyses of hypnograms often rely on oversimplified temporal descriptive statistics (TDS, e.g., total time spent in a sleep state), which miss the opportunity to study the sleep state switching by overlooking the complex structures of hypnograms. In this paper, we propose analysing sleep hypnograms using a seven-state continuous-time Markov model (CTMM). This proposed model leverages the CTMM to depict the time-varying sleep-state transitions, and probes three types of insomnia by distinguishing three types of wake states. Fitting the proposed model to data from 2056 ageing adults in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, we profiled sleep architectures in this population and identified the various associations between the sleep state transitions and demographic factors and subjective sleep questions. Ageing, sex, and race all show distinctive patterns of sleep state transitions. Furthermore, we also found that the perception of insomnia and restless sleep are significantly associated with critical transitions in the sleep architecture. By incorporating three wake states in a continuous-time Markov model, our proposed method reveals interesting insights into the relationships between objective hypnogram data and subjective sleep quality assessments.

Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study.

BACKGROUND: Sleep and its architecture are affected and changing through the whole lifespan. We know main modifications of the macro-architecture with a shorter sleep, occurring earlier and being more fragmented. We have been studying sleep micro-architecture through its pathological modification in sleep, psychiatric or neurocognitive disorders whereas we are still unable to say if the sleep micro-architecture of an old and very old person is rather normal, under physiological changes, or a concern for a future disorder to appear. We wanted to evaluate age-related changes in sleep spindle characteristics in individuals over 75 years of age compared with younger individuals. METHODS: This was an exploratory study based on retrospective and comparative laboratory-based polysomnography data registered in the normal care routine for people over 75 years of age compared to people aged 65-74 years. We were studying their sleep spindle characteristics (localization, density, frequency, amplitude, and duration) in the N2 and N3 sleep stages. ANOVA and ANCOVA using age, sex and OSA were applied. RESULTS: We included 36 participants aged > 75 years and 57 participants aged between 65 and 74 years. An OSA diagnosis was most common in both groups. Older adults receive more medication to modify their sleep. Spindle localization becomes more central after 75 years of age. Changes in the other sleep spindle characteristics between the N2 and N3 sleep stages and between the slow and fast spindles were conformed to literature data, but age was a relevant modifier only for density and duration. CONCLUSION: We observed the same sleep spindle characteristics in both age groups except for localization. We built our study on a short sample, and participants were not free of all sleep disorders. We could establish normative values through further studies with larger samples of people without any sleep disorders to understand the modifications in normal aging and pathological conditions and to reveal the predictive biomarker function of sleep spindles.

Sleep Differences in Firefighters: Barracks vs. Home.

It has been shown that the firefighter occupation leads to poor sleep quality and sleep architecture. Disturbed sleep in these occupations can lead to deleterious outcomes including a series of chronic diseases and illnesses such as CVD. PURPOSE: The aims were (1) to quantify firefighters' sleep via polysomnography, (2) to identify differences between sleeping in the barracks versus sleeping at home, and (3) to compare firefighter data to age-matched normative data. We expected significant differences between both the home and the barrack conditions as well as significant differences when both conditions were compared to normative data. METHODS: 10 male firefighters completed 3 nights of polysomnography recordings (SleepProfilerTM (Advanced Brain Monitoring, Carlsbad, CA, USA)) counterbalanced in both their own beds or barracks. A one-way rmANOVA statistical analysis was used to determine differences in sleep values with a Bonferroni correction if a significant difference was found with significance set at p < 0.05. RESULTS: Three important variables, cortical arousals (p < 0.05), autonomic activations (p < 0.01), and spindle duration (p < 0.01), had differences that were statistically significant between sleep at home or in the barracks, with sleep in the barracks being more disturbed. Clinical differences were also observed between the home and barrack conditions and all sleep results were more deleterious when compared to normative data. CONCLUSIONS: The data demonstrates that firefighters show poor sleep quality and heavily impacted sleep architecture. This may be due to the effects of rotating shifts and occupational stress on the sleep-wake cycle. These results, when compared to age-matched normative data, show clinical manifestations of disturbed sleep in the firefighter population.

Exploring Sleep Architecture in Polish Patients with Multiple Sclerosis: A Polysomnography Study.

BACKGROUND: Sleep disturbances are a prevalent phenomenon in patients with multiple sclerosis (PwMS). The present study employs polysomnography (PSG) to quantify sleep efficiency and architecture in PwMS, aiming to elucidate the relationships between PSG parameters and factors including gender, disability level, brain lesion location, and subjective measures of insomnia, excessive daytime sleepiness (EDS), fatigue, pain, and mood disorders. METHODS: The study cohort comprised 51 adult PwMS, of whom 31 underwent overnight PSG. The demographic and clinical characteristics, including age, gender, and Expanded Disability Status Scale (EDSS), were collated. The Athens Insomnia Scale, the Epworth Sleepiness Scale, the Fatigue Severity Scale, the Modified Fatigue Impact Scale (MFIS), the Numerical Pain Rating Scale, and the Hospital Anxiety and Depression Scale were employed for the assessment of insomnia, EDS, fatigue, pain, and mood disorders. The brain and spinal cord magnetic resonance imaging (MRI) were evaluated. RESULTS: A reduced sleep efficiency was observed among 30 PwMS (aged 38.9 ± 12.9), with a mean of 80 ± 12%, especially in those with brainstem demyelinating lesions. In those PwMS aberrant sleep onset latency (SOL) and wake after sleep onset were also noted (p < 0.05). The prevalence of sleep fragmentation, as measured by the total arousal index, was greater in male PwMS than in female (p < 0.05). Higher disability according to the EDSS correlated with longer SOL (ρ = 0.48, p < 0.05), and reduced N2 sleep stage correlated with cognitive fatigue according to MFIS (ρ = -0.46, p < 0.05). Age, disease duration, insomnia, EDS, physical fatigue, and mood disorders did not impact PSG parameters. CONCLUSIONS: The study demonstrated the disruption of sleep architecture in PwMS, and highlighted the importance of a comprehensive PSG assessment of sleep disturbances in this population.

The Ecology of Human Sleep (EcoSleep) Cohort Study: Protocol for a longitudinal repeated measurement burst design study to assess the relationship between sleep determinants and outcomes under real-world conditions across time of year.

The interplay of daily life factors, including mood, physical activity, or light exposure, influences sleep architecture and quality. Laboratory-based studies often isolate these determinants to establish causality, thereby sacrificing ecological validity. Furthermore, little is known about time-of-year changes in sleep and circadian-related variables at high resolution, including the magnitude of individual change across time of year under real-world conditions. The Ecology of Human Sleep (EcoSleep) cohort study will investigate the combined impact of sleep determinants on individuals' daily sleep episodes to elucidate which waking events modify sleep patterns. A second goal is to describe high-resolution individual sleep and circadian-related changes across the year to understand intra- and inter-individual variability. This study is a prospective cohort study with a measurement-burst design. Healthy adults aged 18-35 years (N = 12) will be enrolled for 12 months. Participants will continuously wear actimeters and pendant-attached light loggers. A subgroup will also measure interstitial fluid glucose levels (six paticipants). Every 4 weeks, all participants will undergo three consecutive measurement days of four ecological momentary assessments each day ('bursts') to sample sleep determinants during wake. Participants will also continuously wear temperature loggers (iButtons) during the bursts. Body weight will be captured before and after the bursts in the laboratory. The bursts will be separated by two at-home electroencephalogram recordings each night. Circadian phase and amplitude will be estimated during the bursts from hair follicles, and habitual melatonin onset will be derived through saliva sampling. Environmental parameters (bedroom temperature, humidity, and air pressure) will be recorded continuously.

Sleep architecture in idiopathic hypersomnia: the influence of age, sex, and body mass index.

This study aimed to progress the understanding of idiopathic hypersomnia (IH) by assessing the moderating influence of individual characteristics, such as age, sex, and body mass index (BMI) on sleep architecture. In this retrospective study, 76 IH participants (38.1 ± 11.3 years; 40 women) underwent a clinical interview, an in-laboratory polysomnography with a maximal 9-h time in bed and a multiple sleep latency test (MSLT). They were compared to 106 healthy controls (38.1 ± 14.1 years; 60 women). Multiple regressions were used to assess moderating influence of age, sex, and BMI on sleep variables. We used correlations to assess whether sleep variables were associated with Epworth Sleepiness Scale scores and mean sleep onset latency on the MSLT in IH participants. Compared to controls, IH participants had shorter sleep latency (p = 0.002), longer total sleep time (p < 0.001), more time spent in N2 sleep (p = 0.008), and showed trends for a higher sleep efficiency (p = 0.023) and more time spent in rapid eye movement (REM) sleep (p = 0.022). No significant moderating influence of age, sex, or BMI was found. More severe self-reported sleepiness in IH patients was correlated with shorter REM sleep latency and less N1 sleep in terms of proportion and duration (ps < 0.01). This study shows that, when compared to healthy controls, patients with IH had no anomalies in their sleep architecture that can explain their excessive daytime sleepiness. Moreover, there is no moderating influence of age, sex, and BMI, suggesting that the absence of major group differences is relatively robust.

Polysomnography parameters in a large cohort of people with multiple sclerosis.

BACKGROUND: Disordered and disturbed sleep is quite common among people with multiple sclerosis (PwMS). It is associated with fatigue one of most disabling symptoms in MS. This study aims at comparing polysomnographic (PSG) sleep parameters in a large single cohort of PwMS from a single center to that of the published norms. Hence establishing PSG parameters in PwMS. METHODS: This is a retrospective review of 299 consecutive adult PwMS who were seen and evaluated with an overnight PSG at a Comprehensive MS Care Center between 11/19/2001 to 9/17/2014. Data extracted from the PSG included Total Sleep Time (TST), sleep efficiency (SE), sleep onset latency (SOL), Relative REM latency, total apnea-hypopnea indices (AHI), spontaneous arousal indices (AI), total periodic leg movements indices (PLMI) and, sleep architecture metrics including percentage spent in stages N1/N2, N3, and REM. RESULTS: PwMS, compared to normative data, had, on average, 85.9 min shorter TST (p < 0.001), 27.3 min longer SOL (p < 0.0001), 62.1 min longer REM latency (p < 0.0001), 10.7 % lower SE (p < 0.0001), 16.4 % more N1/N2 (p < 0.0001) and 11.4 % less N3 (p < 0.0001). REM latency The prevalence of Obstructive Sleep Apnea (OSA) was high at 60.7 % and the mean AHI was higher by 11.1 events per hour (p < 0.0001). CONCLUSIONS: This study establishes PSG parameters in the largest PwMS cohort reported to date. It is important to be vigilant of sleep complaints in PwMS. Future prospective large single cohort studies with standardized methods are needed to further understand sleep disturbances in PwMS as well as their causes and implications.

Hyperarousal features in the sleep architecture of individuals with and without insomnia.

Sleep architecture encodes relevant information on the structure of sleep and has been used to assess hyperarousal in insomnia. This study investigated whether polysomnography-derived sleep architecture displays signs of hyperarousal in individuals with insomnia compared with individuals without insomnia. Data from Phase 3 clinical trials, private clinics and a cohort study were analysed. A comprehensive set of sleep architecture features previously associated with hyperarousal were retrospectively analysed focusing on sleep-wake transition probabilities, electroencephalographic spectra and sleep spindles, and enriched with a novel machine learning algorithm called the Wake Electroencephalographic Similarity Index. This analysis included 1710 individuals with insomnia and 1455 individuals without insomnia. Results indicate that individuals with insomnia had a higher likelihood of waking from all sleep stages, and showed increased relative alpha during Wake and N1 sleep and increased theta power during Wake when compared with individuals without insomnia. Relative delta power was decreased and Wake Electroencephalographic Similarity Index scores were elevated across all sleep stages except N3, suggesting more wake-like activity during these stages in individuals with insomnia. Additionally, sleep spindle density was decreased, and spindle dispersion was increased in individuals with insomnia. These findings suggest that insomnia is characterized by a dysfunction in sleep quality with a continuous hyperarousal, evidenced by changes in sleep-wake architecture.

Worse sleep architecture but not self-reported insomnia and sleepiness is associated with higher cortisol levels in menopausal women.

OBJECTIVE: Worsening of sleep quality during menopause is well recognized. However, the underlying hormonal regulation is insufficiently described. In this study, we evaluated associations between sleep and cortisol levels. STUDY DESIGN: Seventeen perimenopausal and 18 postmenopausal women were enrolled in a three-night sleep study. Diurnal blood sampling was performed during the third night and the following day. MAIN OUTCOME MEASURES: Self-reported insomnia and sleepiness were evaluated with the Basic Nordic Sleep Questionnaire and sleep architecture with all-night polysomnography. Diurnal cortisol samples were collected at 20-min intervals. Correlation analyses and generalized linear models adjusted by age, body mass index, vasomotor symptoms and depressive symptoms were conducted. RESULTS: In correlation analyses, self-reported insomnia and sleepiness were not associated with cortisol levels. Lower sleep efficiency, slow-wave sleep and stage 1 percentages, number of slow-wave sleep and of rapid-eye-movement (REM) periods, longer slow-wave sleep latency and higher wake after sleep onset percentage were associated with higher cortisol levels (all p < 0.05). Further, lower slow-wave sleep percentage and longer slow-wave sleep latency correlated with steeper daytime cortisol slope (i.e. day cortisol decrease, both p < 0.05). In adjusted generalized linear models, lower sleep efficiency and number of rapid-eye-movement periods as well as higher wake after sleep onset percentage correlated with higher cortisol levels; lower slow-wave sleep percentage correlated with higher cortisol awakening response. CONCLUSIONS: Worse sleep architecture but not worse self-reported insomnia and sleepiness was associated with higher cortisol levels. This is important for understanding sleep in women, especially during the menopausal period.

Prevalence and Characteristics of Patients with Obstructive Sleep Apnea and Chronic Obstructive Pulmonary Disease: Overlap Syndrome.

Overlap syndrome (OVS) is a distinct clinical entity that seems to result in potential cardiovascular consequences. We aimed to estimate the prevalence and risk factors for OVS in OSA patients and analyze clinical and PSG characteristics associated with OVS. In this cross-sectional study, 2616 patients evaluated for OSA underwent type-1 polysomnography (PSG). They were grouped as pure OSA (AHI > 15/h) and OVS patients. Demographics, PSG data, pulmonary function tests and arterial blood gases (ABGs) were compared between groups after adjustments for confounders. OSA was diagnosed in 2108 out of 2616 patients. Of those, 398 (19%) had OVS. Independent predictors of OVS were older age [OR: 5.386 (4.153-6.987)], current/former smoking [OR: 11.577 (7.232-18.532)], BMI [OR: 2.901 (2.082-4.044)] and ABG measurements [PaCO2 ≥ 45 OR: 4.648 (3.078-7.019), PO2 [OR: 0.934 (0.920-0.949)], HCO3- [OR: 1.196 (1.133-1.263), all p < 0.001]. OVS was also associated with prevalent hypertension [OR: 1.345 (1.030-1.758), p = 0.03] and cardiovascular disease [OR: 1.617 (1.229-2.126), p < 0.001], depressive symptoms [OR: 1.741 (1.230-2.465), p = 0.002] and nocturia [OR: 1.944 (1.378-2.742), p < 0.001], as well as with indices of OSA severity. Disturbances in sleep architecture were more prominent in OVS expressed by lower %N3 and REM% and higher arousal index. Our data suggest that OVS is prevalent among OSA patients, with distinct clinical and PSG characteristics. These characteristics could be utilized as predictive factors for early identification and further evaluation of these patients towards desirable patient-reported outcomes.

Dopamine agonists in restless leg syndrome treatment and their effects on sleep parameters: A systematic review and meta-analysis.

BACKGROUND: Dopamine agonists (DAs) constitute the standard therapeutic scheme for restless leg syndrome (RLS) because they have been proven to be effective. However, DAs may change sleep parameters, thus having adverse effects on patient condition. This meta-analysis clarified the effects of DAs used in RLS treatment on the sleep architecture. METHODS: PubMed, Embase, and Cochrane Central databases were searched for randomized control trials (RCT) (up to October 2023) that discussed the effects of DAs on sleep architecture in patients with RLS. A meta-analysis employing a random-effects model was conducted. The patients were divided into subgroups according to individual DAs and treatment duration (1 day or ≥4 weeks). RESULTS: Thirteen eligible randomized placebo-controlled trials were included in the assessment. The effects of three DAs (i.e., pramipexole, ropinirole, and rotigotine) on rapid eye movement (REM) sleep, slow-wave sleep (SWS), and sleep efficiency (SE) were analyzed. Overall, pramipexole significantly improved SE but decreased the percentage of REM sleep among treated patients. Ropinirole also enhanced SE compared with the placebo group. Rotigotine did not affect SE and REM sleep. Subgroup analysis found that pramipexole used for 1 day and ≥4 weeks significantly diminished the percentage of REM sleep. Ropinirole used for 1 day showed similar REM sleep patterns. Finally, none of the three DAs affected SWS. CONCLUSIONS: This meta-analysis demonstrated that DAs significantly affect sleep parameters.

"Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data.

BACKGROUND: Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. NEW METHOD: Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. RESULTS: Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. COMPARISON WITH EXISTING METHODS: Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. CONCLUSIONS: The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

The Effect of Type-2 Diabetes Mellitus on Sleep Architecture and Sleep Apnea Severity in Patients With Obstructive Sleep Apnea Syndrome.

INTRODUCTION: Obstructive sleep apnea syndrome (OSAS) is a severe condition that is characterized by recurrent partial or complete breathing interruptions during sleep, leading to insulin resistance, microvascular complications, and cardiovascular complications. It is of great importance to know the impact of type 2 diabetes mellitus (DM), which is prevalent in the world and in our country, Turkey, leads to significant mortality and morbidity, significantly affects the quality of life, and requires continuous follow-up, on sleep in patients with OSAS and to raise awareness on this issue. In this study, we aimed to determine the effects of diabetes on sleep duration and sleep architecture in patients with OSAS and to investigate the relationship between OSAS severity and DM control. METHODS: Fifty diabetic and 42 non-diabetic patients diagnosed with OSAS at the Sleep Disorders Center of Süreyyapasa Chest Diseases and Thoracic Surgery Training and Research Hospital, Istanbul, Turkey, between October 2022 and March 2023 were included in the study. Polysomnographic and biochemical parameters of the two groups were compared. The effect of OSAS severity and sleep architecture on diabetes control was investigated. RESULTS: No significant difference was found between diabetic and non-diabetic patients in terms of total sleep duration, sleep efficiency, and sleep latency, whereas REM (rapid eye movement) latency was prolonged and REM sleep duration and percentage were significantly lower in diabetic patients. The severity of OSAS was found to be greater in diabetic patients and they spent significantly more time below 90% saturation during sleep. No correlation was found between the groups in the glycated hemoglobin (HbA1c) parameter, which we examined in terms of diabetes control, sleep architecture, and OSAS severity. CONCLUSION: The presence of diabetes aggravates the severity of OSAS, prolongs the transition to REM sleep, and leads to a decrease in REM duration. Sleep is essential for both mental and physical well-being. In this regard, it is of utmost importance to examine diabetic patients for OSAS and to perform polysomnography in appropriate patients.

Variations in the sleep-related breathing disorder index on polysomnography between men with HIV and controls: a matched case-control study.

BACKGROUND: Both sleep-related breathing disorders (SRBDs) and HIV infection can interfere with normal sleep architecture, and also cause physical and psychological distress. We aimed to understand the differences in the obstructive patterns, sleep architecture, physical and psychological distress when compared between people living with HIV (PLWH) and matched the severity of SRBDs controls. METHODS: A comparative study using matched case-control design was conducted. Men with HIV infection (case group) were enrolled from 2016 to 2019. A control group with HIV seronegative men were matched for SRBDs severity, and were selected from sleep medicine center database for comparison. RESULTS: The mean age of the 108 men (including 54 cases and 54 matched controls) was 33.75 years. Central-apnea index (CI) was higher in the case group rather than matched controls (mean CI, 0.34 vs. 0.17, p = 0.049). PLWH had a lower mean percentage of stage 3 sleep (10.26% vs. 13.94%, p = 0.034) and a higher percentage of rapid eye movement sleep (20.59% vs. 17.85%, p = 0.011) compared to matched controls. Nocturnal enuresis and sleepiness causing traffic accidents were more frequent complaint in PLWH compared to controls. CONCLUSIONS: Early detected SRBDs and subtypes in PLWH to begin treatment for the underlying cause could reduce the risk of sleepiness-related traffic accidents.

Sleep macro-architecture in patients with Parkinson's disease does not change during the first night of neurostimulation in a pilot study.

STUDY OBJECTIVES: A growing body of literature suggests that deep brain stimulation to treat motor symptoms of Parkinson's disease may also ameliorate certain sleep deficits. Many foundational studies have examined the impact of stimulation on sleep following several months of therapy, leaving an open question regarding the time course for improvement. It is unknown whether sleep improvement will immediately follow onset of therapy or accrete over a prolonged period of stimulation. The objective of our study was to address this knowledge gap by assessing the impact of deep brain stimulation on sleep macro-architecture during the first nights of stimulation. METHODS: Polysomnograms were recorded for 3 consecutive nights in 14 patients with advanced Parkinson's disease (10 male, 4 female; age: 53-74 years), with intermittent, unilateral subthalamic nucleus deep brain stimulation on the final night or 2. Sleep scoring was determined manually by a consensus of 4 experts. Sleep macro-architecture was objectively quantified using the percentage, latency, and mean bout length of wake after sleep onset and on each stage of sleep (rapid eye movement and non-rapid eye movement stages 1, 2, 3). RESULTS: Sleep was found to be highly disrupted in all nights. Sleep architecture on nights without stimulation was consistent with prior results in treatment naive patients with Parkinson's disease. No statistically significant difference was observed due to stimulation. CONCLUSIONS: These objective measures suggest that 1 night of intermittent subthreshold stimulation appears insufficient to impact sleep macro-architecture. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Adaptive Neurostimulation to Restore Sleep in Parkinson's Disease; URL: https://clinicaltrials.gov/ct2/show/NCT04620551; Identifier: NCT04620551. CITATION: Das R, Gliske SV, West LC, et al. Sleep macro-architecture in patients with Parkinson's disease does not change during the first night of neurostimulation in a pilot study. J Clin Sleep Med. 2024;20(9):1489-1496.

Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.

The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understanding of nocturnal polysomnography narcolepsy type 1 features, there remains a need for improving methods to differentiate narcolepsy type 1 nighttime sleep features from those of individuals without narcolepsy type 1. We aimed to develop a machine learning framework for identifying sleep features to discriminate narcolepsy type 1 from clinical controls, narcolepsy type 2 and idiopathic hypersomnia. The population included polysomnography data from 350 drug-free individuals (114 narcolepsy type 1, 90 narcolepsy type 2, 105 idiopathic hypersomnia, and 41 clinical controls) collected at the National Reference Centers for Narcolepsy in Montpelier, France. Several sets of nocturnal sleep features were explored, as well as the value of time-resolving sleep architecture by analysing sleep per quarter-night. Several patterns of nighttime sleep evolution emerged that differed between narcolepsy type 1, clinical controls, narcolepsy type 2 and idiopathic hypersomnia, with increased nighttime instability observed in patients with narcolepsy type 1. Using machine learning models, we identified rapid eye movement sleep onset as the best single polysomnography feature to distinguish narcolepsy type 1 from controls, narcolepsy type 2 and idiopathic hypersomnia. By combining multiple feature sets capturing different aspects of sleep across quarter-night periods, we were able to further improve between-group discrimination and could identify the most discriminative sleep features. Our results highlight salient polysomnography features and the relevance of assessing their time-dependent changes during sleep that could aid diagnosis and measure the impact of novel therapeutics in future clinical trials.

Timing of Deep and REM Sleep Based on Fitbit Sleep Staging in Young Healthy Adults under Real-Life Conditions.

Sleep timing is controlled by intrinsic homeostatic and circadian components. The circadian component controls the chronotype, which is defined by the propensity to sleep at a particular clock time. However, sleep timing can be significantly affected by external factors such as the morning alarm clock. In this study, we analysed the timing of deep and REM sleep as well as the composition of REM sleep using Fitbit sleep staging in young healthy adults (n = 59) under real-life conditions. Sleep stage percentiles were correlated with the timing of total sleep in time after sleep onset for the homeostatic component and in clock time for the circadian component. Regarding the circadian component, the phase of total sleep is most strongly associated with the phases of early deep sleep and REM sleep. Furthermore, a stronger phase relationship between deep and REM sleep with total sleep is associated with greater consolidation of REM sleep. Chronotype-dependent sleep loss correlates negatively with the strength of the phase relationship between deep sleep and total sleep. In conclusion, the interaction of the circadian component of sleep timing with the timing of sleep stages is associated with REM sleep quality. In particular, the interaction of the circadian component of sleep timing with deep sleep seems to be more vulnerable to external factors.