Obstructive sleep apnea alters sleep stage transition dynamics

Detection of Nocturnal Slow Wave Sleep Based on Cardiorespiratory Activity in Healthy Adults

Human slow wave sleep (SWS) during bedtime is paramount for energy conservation and memory consolidation. This study aims at automatically detecting SWS from nocturnal sleep using cardiorespiratory signals that can be acquired with unobtrusive sensors in a home-based scenario. From the signals, time-dependent features are extracted for continuous 30-s epochs. To reduce the measuring noise, body motion artifacts, and/or within-subject variability in physiology conveyed by the features, and thus, enhance the detection performance, we propose to smooth the features over each night using a spline fitting method. In addition, it was found that the changes in cardiorespiratory activity precede the transitions between SWS and the other sleep stages (non-SWS). To this matter, a novel scheme is proposed that performs the SWS detection for each epoch using the feature values prior to that epoch. Experiments were conducted with a large dataset of 325 overnight polysomnography (PSG) recordings using a linear discriminant classifier and tenfold cross validation. Features were selected with a correlation-based method. Results show that the performance in classifying SWS and non-SWS can be significantly improved when smoothing the features and using the preceding feature values of 5-min earlier. We achieved a Cohen's Kappa coefficient of 0.57 (at an accuracy of 88.8%) using only six selected features for 257 recordings with a minimum of 30-min overnight SWS that were considered representative of their habitual sleeping pattern at home. These features included the standard deviation, low-frequency spectral power, and detrended fluctuation of heartbeat intervals as well as the variations of respiratory frequency and upper and lower respiratory envelopes. A marked drop in Kappa to 0.21 was observed for the other nights with SWS time of less than 30 min, which were found to more likely occur in elderly. This will be the future challenge in cardiorespiratory-based SWS detection.

Development of the National Healthy Sleep Awareness Project Sleep Health Surveillance Questions

OBJECTIVES

For the first time ever, as emphasized by inclusion in the Healthy People 2020 goals, sleep health is an emphasis of national health aims. The National Healthy Sleep Awareness Project (NHSAP) was tasked to propose questions for inclusion in the next Behavioral Risk Factor Surveillance System (BRFSS), a survey that includes a number of questions that target behaviors thought to impact health, as a means to measure community sleep health. The total number of questions could not exceed five, and had to include an assessment of the risk for obstructive sleep apnea (OSA).

METHODS

An appointed workgroup met via teleconference and face-to-face venues to develop an inventory of published survey questions being used to identify sleep health, to develop a framework on which to analyze the strengths and weaknesses of current survey questions concerning sleep, and to develop recommendations for sleep health and disease surveillance questions going forward.

RESULTS

The recommendation was to focus on certain existing BRFSS questions pertaining to sleep duration, quality, satisfaction, daytime alertness, and to add to these other BRFSS existing questions to make a modified STOP-BANG questionnaire (minus the N for neck circumference) to assess for risk of OSA.

CONCLUSIONS

Sleep health is an important dimension of health that has previously received less attention in national health surveys. We believe that 5 questions recommended for the upcoming BRFSS question banks will assist as important measures of sleep health, and may help to evaluate the effectiveness of interventions to improve sleep health in our nation.

Long-term compliance with continuous positive airway pressure in Saudi patients with obstructive sleep apnea. A prospective cohort study

OBJECTIVES

To evaluate continuous positive airway pressure (CPAP) compliance and define predictors of CPAP compliance among Saudi patients with obstructive sleep apnea (OSA) after applying an educational program. 

METHODS

This prospective cohort study included consecutive patients diagnosed to have OSA based on polysomnography between January 2012 and January 2014 in King Saud University, Riyadh, Kingdom of Saudi Arabia. All patients had educational sessions on OSA and CPAP therapy before sleep study, and formal hands-on training on CPAP machines on day one, day 7, and day 14 after diagnosis. The follow-up in the clinic was carried out at one, 4, and 10 months after initiating CPAP therapy. Continuous positive airway pressure compliance was assessed objectively. Logistic regression model was used to assess the predictors of CPAP adherence.

RESULTS

The study comprised 156 patients with a mean age of 51.9±12.1 years, body mass index of 38.4±10.6 kg/m2, and apnea hypopnea index of 63.7±39.3 events/hour. All patients were using CPAP at month one, 89.7% at month 4, and 83% at month 10. The persistence of CPAP-related side effects and comorbid bronchial asthma remained as independent predictors of CPAP compliance at the end of the study.

CONCLUSION

With intensive education, support, and close monitoring, more than 80% of Saudi patients with OSA continued to use CPAP after 10 months of initiating CPAP therapy.

Nocturnal Sleep Dynamics Identify Narcolepsy Type 1

STUDY OBJECTIVES

To evaluate the reliability of nocturnal sleep dynamics in the differential diagnosis of central disorders of hypersomnolence.

DESIGN

Cross-sectional.

SETTING

Sleep laboratory.

PATIENTS

One hundred seventy-five patients with hypocretin-deficient narcolepsy type 1 (NT1, n = 79), narcolepsy type 2 (NT2, n = 22), idiopathic hypersomnia (IH, n = 22), and "subjective" hypersomnolence (sHS, n = 52).

INTERVENTIONS

None.

METHODS

Polysomnographic (PSG) work-up included 48 h of continuous PSG recording. From nocturnal PSG conventional sleep macrostructure, occurrence of sleep onset rapid eye movement period (SOREMP), sleep stages distribution, and sleep stage transitions were calculated. Patient groups were compared, and receiver operating characteristic (ROC) curve analysis was used to test the diagnostic utility of nocturnal PSG data to identify NT1.

RESULTS

Sleep macrostructure was substantially stable in the 2 nights of each diagnostic group. NT1 and NT2 patients had lower latency to rapid eye movement (REM) sleep, and NT1 patients showed the highest number of awakenings, sleep stage transitions, and more time spent in N1 sleep, as well as most SOREMPs at daytime PSG and at multiple sleep latency test (MSLT) than all other groups. ROC curve analysis showed that nocturnal SOREMP (area under the curve of 0.724 ± 0.041, P < 0.0001), percent of total sleep time spent in N1 (0.896 ± 0.023, P < 0.0001), and the wakefulness-sleep transition index (0.796 ± 0.034, P < 0.0001) had a good sensitivity and specificity profile to identify NT1 sleep, especially when used in combination (0.903 ± 0.023, P < 0.0001), similarly to SOREMP number at continuous daytime PSG (0.899 ± 0.026, P < 0.0001) and at MSLT (0.956 ± 0.015, P < 0.0001).

CONCLUSIONS

Sleep macrostructure (i.e. SOREMP, N1 timing) including stage transitions reliably identifies hypocretin-deficient narcolepsy type 1 among central disorders of hypersomnolence.

Two subtypes of positional obstructive sleep apnea: Supine-predominant and supine-isolated

OBJECTIVES

The body position has a strong influence on obstructive sleep apnea (OSA). The purpose of this study is to compare the clinical features of two subtypes of positional OSA (POSA), namely supine-predominant OSA (spOSA) and supine-isolated OSA (siOSA), so as to discuss whether the two groups can be classified separately.

METHODS

A total of 279 consecutive patients with OSA were enrolled. The POSA was defined as having an overall apnea-hypopnea index (AHI) ⩾ 5 with supine AHI > 2 times the non-supine AHI. Only those with ⩾ 30 min spent in the supine and non-supine sleeping positions were included, and split night studies were excluded from the study. Patients were considered spOSA unless their non-supine AHI was negligible (<5) (siOSA). The clinical and polysomnographic characteristics of both groups were compared.

RESULTS

Two hundred and sixteen subjects (77.4%) met the criteria for POSA, with 158 (73.1%) of them classified as spOSA, and 58 (26.9%) as siOSA. The siOSA patients had lower arousal indices, but poorer quality of sleep, and were more depressed and anxious compared with the spOSA subjects.

CONCLUSIONS

Those with siOSA and spOSA show different clinical features.

SIGNIFICANCE

These findings suggest that a more detailed sub-classification of POSA is needed.

Accumulated deep sleep is a powerful predictor of LH pulse onset in pubertal children

CONTEXT

During puberty, reactivation of the reproductive axis occurs during sleep, with LH pulses specifically tied to deep sleep. This association suggests that deep sleep may stimulate LH secretion, but there have been no interventional studies to determine the characteristics of deep sleep required for LH pulse initiation.

OBJECTIVE

The objective of this study was to determine the effect of deep sleep fragmentation on LH secretion in pubertal children.

DESIGN AND SETTING

Studies were performed in a clinical research center.

SUBJECTS

Fourteen healthy pubertal children (11.3-14.1 y) participated in the study.

INTERVENTIONS

Subjects were randomized to two overnight studies with polysomnography and frequent blood sampling, with or without deep sleep disruption via auditory stimuli.

RESULTS

An average of 68.1 ±10.7 (± SE) auditory stimuli were delivered to interrupt deep sleep during the disruption night, limiting deep sleep to only brief episodes (average length disrupted 1.3 ± 0.2 min vs normal 7.1 ± 0.8 min, P < .001), and increasing the number of transitions between non-rapid eye movement (NREM), REM, and wake (disrupted 274.5 ± 33.4 vs normal 131.2 ± 8.1, P = .001). There were no differences in mean LH (normal: 3.2 ± 0.4 vs disrupted: 3.2 ± 0.5 IU/L), LH pulse frequency (0.6 ± 0.06 vs 0.6 ± 0.07 pulses/h), or LH pulse amplitude (2.8 ± 0.4 vs 2.8 ± 0.4 IU/L) between the two nights. Poisson process modeling demonstrated that the accumulation of deep sleep in the 20 minutes before an LH pulse, whether consolidated or fragmented, was a significant predictor of LH pulse onset (P < .001).

CONCLUSION

In pubertal children, nocturnal LH augmentation and pulse patterning are resistant to deep sleep fragmentation. These data suggest that, even when fragmented, deep sleep is strongly related to activation of the GnRH pulse generator.

Movement distribution: a new measure of sleep fragmentation in children with upper airway obstruction

STUDY OBJECTIVES

To develop a measure of sleep fragmentation in children with upper airway obstruction based on survival curve analysis of sleep continuity.

DESIGN

Prospective repeated measures.

SETTING

Hospital sleep laboratory.

PARTICIPANTS

92 children aged 3.0 to 12.9 years undergoing 2 overnight polysomnographic (PSG) sleep studies, 6 months apart. Subjects were divided into 3 groups based on their obstructive apnea and hypopnea index (OAHI) and other upper airway obstruction (UAO) symptoms: primary snorers (PS; n = 24, OAHI <1), those with obstructive sleep apnea syndrome (OSAS; n = 20, OAHI ≥1) and non-snoring controls (C; n = 48, OAHI <1).

INTERVENTIONS

Subjects in the PS and OSAS groups underwent tonsillectomy and adenoidectomy between PSG assessments.

MEASUREMENTS AND RESULTS

Post hoc measures of movement and contiguous sleep epochs were exported and analyzed using Kaplan-Meier estimates of survival to generate survival curves for the 3 groups. Statistically significant differences were found between these group curves for sleep continuity (P < 0.05) when using movement events as the sleep fragmenting event, but not if stage 1 NREM sleep or awakenings were used.

CONCLUSION

Using conventional indices of sleep fragmentation in survival curve analysis of sleep continuity does not provide a useful measure of sleep fragmentation in children with upper airway obstruction. However, when sleep continuity is defined as the time between gross body movements, a potentially useful clinical measure is produced.

Sleep-wake misperception in sleep apnea patients undergoing diagnostic versus titration polysomnography

OBJECTIVE

Insomnia is commonly co-morbid with obstructive sleep apnea. Among patients reporting insomnia symptoms, sleep misperception occurs when self-reported sleep duration under-estimates objective measures. Misperception represents a clinical challenge since insomnia management is based entirely on patient self-report. We tested the hypothesis that misperception occurring in sleep apnea patients would improve with subsequent treatment.

METHODS

We compared subjective sleep-wake reports with objective sleep in adults with obstructive sleep apnea (n=405) in two nights of polysomnography (diagnostic and treatment) within a median interval of 92 days.

RESULTS

Sleep latency was generally over-estimated, while wake after sleep onset and number of awakenings were under-estimated. None of these estimations differed between diagnostic and treatment polysomnograms. We observed a large spectrum of total sleep time misperception values during the diagnostic polysomnogram, with one third of the cohort under-estimating their total sleep time by at least 60 min. Of those with >60 minute misperception, we observed improved total sleep time perception during treatment polysomnography. Improved perception correlated with improvements in self-reported sleep quality and response confidence. We found no polysomnogram or demographic predictors of total sleep time misperception for the diagnostic polysomnogram, nor did we find objective correlates of improved perception during titration.

CONCLUSION

Our results suggest that misperception may improve with treatment of obstructive sleep apnea in patients who also exhibit misperception. Within subject changes in misperception are consistent with misperception being, at least to some extent, a state characteristic, which has implications for management of patients with comorbid insomnia and sleep apnea.

Coupled flip-flop model for REM sleep regulation in the rat

Recent experimental studies investigating the neuronal regulation of rapid eye movement (REM) sleep have identified mutually inhibitory synaptic projections among REM sleep-promoting (REM-on) and REM sleep-inhibiting (REM-off) neuronal populations that act to maintain the REM sleep state and control its onset and offset. The control mechanism of mutually inhibitory synaptic interactions mirrors the proposed flip-flop switch for sleep-wake regulation consisting of mutually inhibitory synaptic projections between wake- and sleep-promoting neuronal populations. While a number of synaptic projections have been identified between these REM-on/REM-off populations and wake/sleep-promoting populations, the specific interactions that govern behavioral state transitions have not been completely determined. Using a minimal mathematical model, we investigated behavioral state transition dynamics dictated by a system of coupled flip-flops, one to control transitions between wake and sleep states, and another to control transitions into and out of REM sleep. The model describes the neurotransmitter-mediated inhibitory interactions between a wake- and sleep-promoting population, and between a REM-on and REM-off population. We proposed interactions between the wake/sleep and REM-on/REM-off flip-flops to replicate the behavioral state statistics and probabilities of behavioral state transitions measured from experimental recordings of rat sleep under ad libitum conditions and after 24 h of REM sleep deprivation. Reliable transitions from REM sleep to wake, as dictated by the data, indicated the necessity of an excitatory projection from the REM-on population to the wake-promoting population. To replicate the increase in REM-wake-REM transitions observed after 24 h REM sleep deprivation required that this excitatory projection promote transient activation of the wake-promoting population. Obtaining the reliable wake-nonREM sleep transitions observed in the data required that activity of the wake-promoting population modulated the interaction between the REM-on and REM-off populations. This analysis suggests neuronal processes to be targeted in further experimental studies of the regulatory mechanisms of REM sleep.

Physiologically-based modeling of sleep-wake regulatory networks

Mathematical modeling has played a significant role in building our understanding of sleep-wake and circadian behavior. Over the past 40 years, phenomenological models, including the two-process model and oscillator models, helped frame experimental results and guide progress in understanding the interaction of homeostatic and circadian influences on sleep and understanding the generation of rapid eye movement sleep cycling. Recent advances in the clarification of the neural anatomy and physiology involved in the regulation of sleep and circadian rhythms have motivated the development of more detailed and physiologically-based mathematical models that extend the approach introduced by the classical reciprocal-interaction model. Using mathematical formalisms developed in the field of computational neuroscience to model neuronal population activity, these models investigate the dynamics of proposed conceptual models of sleep-wake regulatory networks with a focus on generating appropriate sleep and wake state transition patterns as well as simulating disease states and experimental protocols. In this review, we discuss several recent physiologically-based mathematical models of sleep-wake regulatory networks. We identify common features among these models in their network structures, model dynamics and approaches for model validation. We describe how the model analysis technique of fast-slow decomposition, which exploits the naturally occurring multiple timescales of sleep-wake behavior, can be applied to understand model dynamics in these networks. Our purpose in identifying commonalities among these models is to propel understanding of both the mathematical models and their underlying conceptual models, and focus directions for future experimental and theoretical work.

Association of naso-Oro-pharyngeal structures with the sleep architecture in suspected obstructive sleep apnea

The study was conducted to find out the association of various naso-oro-pharyngeal structures with sleep macro-architecture in suspected obstructive sleep apnea subjects. Study included 51 subjects with suspected obstructive sleep apnea. Subjects with possible central apnea and those consuming any substance that can affect sleep architecture were excluded. Level I polysomnography was performed after thorough physical examination. Overnight study was scored in 30 s epochs to find out the polysomnographic variables. Surgical treatment was offered wherever indicated. Subjects with moderate to severe obstructive sleep apnea were manually titrated on CPAP with the polysomnogram. SPSS v 17.0 was used for statistical analysis. We did not find any difference in the sleep architecture between genders. Sleep Efficiency was better in subjects with dental overjet, dental attrition, high tongue base, macroglossia, lesser oral cavity volume, edematous uvula, increased submental fat, hypertrophied facial muscles and Mallampatti grade III-IV. Shorter Sleep Latency was seen in subjects with tender TMJ and Mallampatti Gr III-IV. REM latency was shorter in subjects with high tongue base, macroglossia and hypertrophied muscles of mastication. Increased REM was observed in subjects with high tongue base, edematous uvula and tender TMJ. Enlarged tonsils had reversed effect with poor sleep efficiency, increased REM latency and decreased REM. CPAP therapy (N = 20) lessened awake time, decreased N2 and increased REM. Oro-pharyngeal structures affect the sleep architecture in suspected OSA subjects. Nasal structures do not affect the sleep architecture in these subjects and enlarged tonsils have opposite effect. Sleep architecture changes on the titration night with CPAP.

The ontogeny of sleep-wake cycles in zebrafish: a comparison to humans

Zebrafish (Danio rerio) are used extensively in sleep research; both to further understanding of sleep in general and also as a model of human sleep. To date, sleep studies have been performed in larval and adult zebrafish but no efforts have been made to document the ontogeny of zebrafish sleep-wake cycles. Because sleep differs across phylogeny and ontogeny it is important to validate the use of zebrafish in elucidating the neural substrates of sleep. Here we describe the development of sleep and wake across the zebrafish lifespan and how it compares to humans. We find power-law distributions to best fit wake bout data but demonstrate that exponential distributions, previously used to describe sleep bout distributions, fail to adequately account for the data in either species. Regardless, the data reveal remarkable similarities in the ontogeny of sleep cycles in zebrafish and humans. Moreover, as seen in other organisms, zebrafish sleep levels are highest early in ontogeny and sleep and wake bouts gradually consolidate to form the adult sleep pattern. Finally, sleep percentage, bout duration, bout number, and sleep fragmentation are shown to allow for meaningful comparisons between zebrafish and human sleep.

Statistical properties of sleep-wake behavior in the rat and their relation to circadian and ultradian phases

STUDY OBJECTIVES

To examine the statistical characteristics of short-term sleep-wake architecture and to evaluate their dependence on ultradian and circadian phase.

DESIGN

Observational, time series.

SETTING

Laboratory.

PARTICIPANTS

Ten male adult Sprague-Dawley rats.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

States of wakefulness (WAKE), rapid eye movement sleep (REM) and nonrapid eye movement sleep (NREM) were recorded in 5-sec epochs over 7 consecutive days. State bout durations were analyzed using parametric regression of survival curves, comparing exponential, biexponential, and power law models. WAKE survival curves were best fit by biexponential models, suggesting that there are two statistically distinct stochastic mechanisms generating two types of WAKE--"brief" WAKE and "long" WAKE. Exponential time constants varied as a function of circadian and ultradian phase, with "long" WAKE showing the largest effect. NREM survival curves exhibited biexponential and monoexponential distributions in light and dark, respectively, with weak effects of ultradian phase. REM survival curves approximated a monoexponential distribution that varied with circadian but not ultradian phase. χ(2) analysis was used in a three-state Markov model to evaluate whether conditional state transition probabilities exhibit the property of first-order dependence. This was partially confirmed, but only after accounting for heterogeneity associated with circadian and ultradian phase. However, there was evidence of residual second-order dependence indicating that additional sources of statistical heterogeneity may remain to be identified.

CONCLUSIONS

Sleep-wake state is regulated over short timescales by stochastic mechanisms. When the major sources of heterogeneity are taken into account, including two-component WAKE and NREM states, the sleep-wake system of the rat behaves, to a reasonable approximation, as a Markovian system that is modulated over ultradian and circadian timescales.

Technical advances in the characterization of the complexity of sleep and sleep disorders

The current clinical standard for quantifying sleep physiology is the laboratory polysomnogram, from which basic sleep-wake stages are determined. However, the complexity of sleep physiology has inspired alternative metrics that are providing additional insights into the rich dynamics of sleep. Electro-encephalography, magneto-encephalography, and functional magnetic resonance imaging represent advanced imaging modalities for understanding brain dynamics. These methods are complemented by autonomic measurements that provide additional important insights. We review here the spectrum of approaches that have been leveraged towards improved understanding of the complexity of sleep.

Impact of obstructive sleep apnea on sleep-wake stage ratio

Patients with obstructive sleep apnea (OSA) experience fragmented sleep and exhibit different sleep architectures. While polysomnographic metrics for quantifying sleep architecture are studied, there is little information about the impact of OSA on the ratio of different sleep-wake stages (wake, W; rapid eye movement, REM; non-REM stages 1 to 3, N1 to N3). This study, therefore, aims to investigate the relationship between apnea-hypopnea index (AHI, a measure of OSA severity) and all possible ratios of sleep-wake stages. Sleep architectures of 24 adult subjects with suspected OSA were constructed according to the American Academy of Sleep Medicine scoring manual, and subsequently analyzed through various correlation (Pearson, Spearman, and Kendall) and regression (linear, logarithmic, exponential, and power-law) approaches. Results show a statistically significant positive, linear and monotonic correlation between AHI and REM/N3, as well as between AHI and N1/W (p-values &#60; 0.05). These findings imply that patients with increased severity of OSA may spend more time in REM than deep sleep, and in light sleep than wake (or less time in deep sleep than REM, and in wake than light sleep). A power-law regression model may possibly explain the relationships of AHI-REM/N3 and AHI-N1/W, and predict the value of AHI using REM/N3 or N1/W.

Sleep Fragmentation and the Risk of Incident Alzheimer's Disease and Cognitive Decline in Older Persons

OBJECTIVE

Cross-sectional studies suggest that sleep fragmentation is associated with cognitive performance in older adults. We tested the hypothesis that sleep fragmentation is associated with incident Alzheimer's disease (AD) and the rate of cognitive decline in older adults.

DESIGN

Prospective cohort study.

SETTING

Community-based.

PARTICIPANTS

737 community dwelling older adults without dementia.

MEASUREMENTS AND RESULTS

Sleep fragmentation was quantified from up to 10 consecutive days of actigraphy. Subjects underwent annual evaluation for AD with 19 neuropsychological tests. Over a follow-up period of up to 6 years (mean 3.3 years), 97 individuals developed AD. In a Cox proportional hazards model controlling for age, sex, and education, a higher level of sleep fragmentation was associated with an increased risk of AD (HR = 1.22, 95%CI 1.03-1.44, P = 0.02 per 1SD increase in sleep fragmentation). An individual with high sleep fragmentation (90^th percentile) had a 1.5-fold risk of developing AD as compared with someone with low sleep fragmentation (10^th percentile). The association of sleep fragmentation with incident AD did not vary along demographic lines and was unchanged after controlling for potential confounders including total daily rest time, chronic medical conditions, and the use of common medications which can affect sleep. In a linear mixed effect analysis, a 0.01 unit increase in sleep fragmentation was associated with a 22% increase in the annual rate of cognitive decline relative to the average rate of decline in the cohort (Estimate = -0.016, SE = 0.007, P = 0.03).

CONCLUSIONS

Sleep fragmentation in older adults is associated with incident AD and the rate of cognitive decline.

CITATION

Lim ASP; Kowgier M; Yu L; Buchman AS; Bennett DA. Sleep fragmentation and the risk of incident alzheimer's disease and cognitive decline in older persons. SLEEP 2013;36(7):1027-1032.

Recent developments in home sleep-monitoring devices

Improving our understanding of sleep physiology and pathophysiology is an important goal for both medical and general wellness reasons. Although the gold standard for assessing sleep remains the laboratory polysomnogram, there is an increasing interest in portable monitoring devices that provide the opportunity for assessing sleep in real-world environments such as the home. Portable devices allow repeated measurements, evaluation of temporal patterns, and self-experimentation. We review recent developments in devices designed to monitor sleep-wake activity, as well as monitors designed for other purposes that could in principle be applied in the field of sleep (such as cardiac or respiratory sensing). As the body of supporting validation data grows, these devices hold promise for a variety of health and wellness goals. From a clinical and research standpoint, the capacity to obtain longitudinal sleep-wake data may improve disease phenotyping, individualized treatment decisions, and individualized health optimization. From a wellness standpoint, commercially available devices may allow individuals to track their own sleep with the goal of finding patterns and correlations with modifiable behaviors such as exercise, diet, and sleep aids.

Sleep duration and disorders in pregnancy: implications for glucose metabolism and pregnancy outcomes

Humans have an innate requirement for sleep that is intrinsically governed by circadian and endocrine systems. More recently, reduced sleep duration has gained significant attention for its possible contribution to metabolic dysfunction. Significant evidence suggests that reduced sleep duration may elevate the risk for impaired glucose functioning, insulin resistance and type 2 diabetes. However, to date, few studies have determined the implications of reduced sleep duration with regard to glucose control during pregnancy. With the high prevalence of overweight and obesity in women of reproductive age, the occurrence of gestational diabetes mellitus (GDM) is increasing. GDM results in elevated risk of maternal and fetal complications, as well as increased risk of type 2 diabetes postpartum. Infants born to women with GDM also carry a life-long risk of obesity and type 2 diabetes. The impact of reduced sleep on glucose management during pregnancy has not yet been fully assessed and a paucity of literature currently exits. Herein, we review the association between reduced sleep and impaired carbohydrate metabolism and propose how reduced sleep during pregnancy may result in further dysfunction of the carbohydrate axis. A particular focus will be given to sleep-disordered breathing, as well as GDM-complicated pregnancies. Putative mechanisms of action by which reduced sleep may adversely affect maternal and infant outcomes are also discussed. Finally, we will outline important research questions that need to be addressed.

Time course of EEG slow-wave activity in pre-school children with sleep disordered breathing: a possible mechanism for daytime deficits?

BACKGROUND

Daytime deficits in children with sleep disordered breathing (SDB) are theorized to result from hypoxic insult to the developing brain or fragmented sleep. Yet, these do not explain why deficits occur in primary snorers (PS). The time course of slow wave EEG activity (SWA), a proxy of homeostatic regulation and cortical maturation, may provide insight.

METHODS

Clinical and control subjects (N=175: mean age 4.3±0.9 y: 61% male) participated in overnight polysomnography (PSG). Standard sleep scoring and power spectral analyses were conducted on EEG (C4/A1; 0.5-<3.9Hz). Univariate ANOVA's evaluated group differences in sleep stages and respiratory parameters. Repeated-measures ANCOVA evaluated group differences in the time course of SWA.

RESULTS

Four groups were classified: controls (OAHI ≤ 1 event/h; no clinical history); PS (OAHI ≤ 1 event/h; clinical history); mild OSA (OAHI=1-5 events/h); and moderate to severe OSA (MS OSA: OAHI>5 events/h). Group differences were found in the percentage of time spent in NREM Stages 1 and 4 (p<0.001) and in the time course of SWA. PS and Mild OSA children had higher SWA in the first NREM period than controls (p<0.05). All SDB groups had higher SWA in the fourth NREM period (p<0.01).

CONCLUSIONS

These results suggest enhanced sleep pressure but impaired restorative sleep function in pre-school children with SDB, providing new insights into the possible mechanism for daytime deficits observed in all severities of SDB.

Decreased nocturnal awakenings in young adults performing bikram yoga: a low-constraint home sleep monitoring study

This pilot study evaluated the impact of Bikram Yoga on subjective and objective sleep parameters. We compared subjective (diary) and objective (headband sleep monitor) sleep measures on yoga versus nonyoga days during a 14-day period. Subjects (n = 13) were not constrained regarding yoga-practice days, other exercise, caffeine, alcohol, or naps. These activities did not segregate by choice of yoga days. Standard sleep metrics were unaffected by yoga, including sleep latency, total sleep time, and percentage of time spent in rapid eye movement (REM), light non-REM, deep non-REM, or wake after sleep onset (WASO). Consistent with prior work, transition probability analysis was a more sensitive index of sleep architecture changes than standard metrics. Specifically, Bikram Yoga was associated with significantly faster return to sleep after nocturnal awakenings. We conclude that objective home sleep monitoring is feasible in a low-constraint, real-world study design. Further studies on patients with insomnia will determine whether the results generalize or not.

Quantification of the fragmentation of rest-activity patterns in elderly individuals using a state transition analysis

OBJECTIVES

Recent interest in the temporal dynamics of behavioral states has spurred the development of analytical approaches for their quantification. Several analytical approaches for polysomnographic data have been described. However, polysomnography is cumbersome, perturbs behavior, and is limited to short recordings. Although less physiologically comprehensive than polysomnography, actigraphy is nonintrusive, amenable to long recordings, and suited to use in subjects' natural environments, and provides an indirect measure of behavioral state. We developed a probabilistic state transition model to quantify the fragmentation of human rest-activity patterns from actigraphic data. We then applied this to the study of the temporal dynamics of rest-activity patterns in older individuals.

DESIGN

Cross-sectional.

SETTING

Community-based.

PARTICIPANTS

621 community-dwelling individuals without dementia participating in the Rush Memory and Aging Project.

MEASUREMENTS AND RESULTS

We analyzed actigraphic data collected for up to 11 days. We processed each record to give a series of transitions between the states of rest and activity, calculated the probabilities of such transitions, and described their evolution as a function of time. From these analyses, we derived metrics of the fragmentation of rest or activity at scales of seconds to minutes. Regression modeling of the relationship of these metrics with clinical variables revealed significant associations with age, even after adjusting for sex, body mass index, and a broad range of medical comorbidities.

CONCLUSIONS

Probabilistic analyses of the transition dynamics of rest-activity data provide a high-throughput, automated, quantitative, and noninvasive method of assessing the fragmentation of behavioral states suitable for large scale human and animal studies; these methods reveal age-associated changes in the fragmentation of rest-activity patterns akin to those described using polysomnographic methods.

Spontaneous hemodynamic oscillations during human sleep and sleep stage transitions characterized with near-infrared spectroscopy

Understanding the interaction between the nervous system and cerebral vasculature is fundamental to forming a complete picture of the neurophysiology of sleep and its role in maintaining physiological homeostasis. However, the intrinsic hemodynamics of slow-wave sleep (SWS) are still poorly known. We carried out 30 all-night sleep measurements with combined near-infrared spectroscopy (NIRS) and polysomnography to investigate spontaneous hemodynamic behavior in SWS compared to light (LS) and rapid-eye-movement sleep (REM). In particular, we concentrated on slow oscillations (3-150 mHz) in oxy- and deoxyhemoglobin concentrations, heart rate, arterial oxygen saturation, and the pulsation amplitude of the photoplethysmographic signal. We also analyzed the behavior of these variables during sleep stage transitions. The results indicate that slow spontaneous cortical and systemic hemodynamic activity is reduced in SWS compared to LS, REM, and wakefulness. This behavior may be explained by neuronal synchronization observed in electrophysiological studies of SWS and a reduction in autonomic nervous system activity. Also, sleep stage transitions are asymmetric, so that the SWS-to-LS and LS-to-REM transitions, which are associated with an increase in the complexity of cortical electrophysiological activity, are characterized by more dramatic hemodynamic changes than the opposite transitions. Thus, it appears that while the onset of SWS and termination of REM occur only as gradual processes over time, the termination of SWS and onset of REM may be triggered more abruptly by a particular physiological event or condition. The results suggest that scalp hemodynamic changes should be considered alongside cortical hemodynamic changes in NIRS sleep studies to assess the interaction between the autonomic and central nervous systems.

Probabilistic sleep architecture models in patients with and without sleep apnea

Sleep fragmentation of any cause is disruptive to the rejuvenating value of sleep. However, methods to quantify sleep architecture remain limited. We have previously shown that human sleep-wake stage distributions exhibit multi-exponential dynamics, which are fragmented by obstructive sleep apnea (OSA), suggesting that Markov models may be a useful method to quantify architecture in health and disease. Sleep stage data were obtained from two subsets of the Sleep Heart Health Study database: control subjects with no medications, no OSA, no medical co-morbidities and no sleepiness (n = 374); and subjects with severe OSA (n = 338). Sleep architecture was simplified into three stages: wake after sleep onset (WASO); non-rapid eye movement (NREM) sleep; and rapid eye movement (REM) sleep. The connectivity and transition rates among eight 'generator' states of a first-order continuous-time Markov model were inferred from the observed ('phenotypic') distributions: three exponentials each of NREM sleep and WASO; and two exponentials of REM sleep. Ultradian REM cycling was accomplished by imposing time-variation to REM state entry rates. Fragmentation in subjects with severe OSA involved faster transition probabilities as well as additional state transition paths within the model. The Markov models exhibit two important features of human sleep architecture: multi-exponential stage dynamics (accounting for observed bout distributions); and probabilistic transitions (an inherent source of variability). In addition, the model quantifies the fragmentation associated with severe OSA. Markov sleep models may prove important for quantifying sleep disruption to provide objective metrics to correlate with endpoints ranging from sleepiness to cardiovascular morbidity.

Modeling the temporal architecture of rat sleep-wake behavior

The fine architecture of sleep-wake behavior shows a distinct dynamic structure with distributions of rat sleep and wake bout durations displaying qualitatively different profiles. Wake bout durations follow a power-law relation whereas sleep bout durations are exponentially distributed. We show that a physiologically-based sleep-wake regulatory network model with an underlying deterministic structure governing neuronal interactions can generate realistic rat sleep-wake behavior as assessed by both standard summary statistics and survival analysis of bout distributions. Obtaining appropriate bout duration distributions depended on stochastic elements included in the model, the existence of multiple mechanisms for state transitions, and specific relationships among time constants governing state maintenance. This model provides a novel framework for exploring the disruptions of sleep-wake architecture associated with pharmacological, genetic, and disease states.

Dynamics of sleep-wake cyclicity at night across the human lifespan

Studies in adult mammals (rats, cats, mice, and humans) have revealed a surprising regularity in the duration of sleep and wake bouts. In particular, wake bout durations exhibit a power-law distribution whereas sleep bout durations exhibit an exponential distribution. Moreover, in rodents, sleep bouts exhibit an exponential distribution at all ages examined, whereas wake bout durations exhibit exponential distributions early in ontogeny with a clear power-law emerging only at the older ages. Thus, the data examined thus far suggests a similar developmental trajectory for a wide range of mammals which in turn may offer a novel metric to directly compare human and animal sleep-wake data. Therefore, we tested the generalizability of these findings by examining the distributions of sleep and wake bouts during the night in a healthy human sample - from premature infants to 70-year-olds. We find that sleep bouts elongate over the first years. At the same time wake bouts shorten but elongate again with increasing age. Moreover, sleep bout durations exhibit exponential distributions at all ages tested, except for the youngest (premature infants). Wake bouts exhibit a power-law distribution - but only during a restricted time window during adulthood. We conclude that the developmental trajectory of human sleep-wake cycles does not map well onto those of rodents; however, the method of characterizing sleep-wake cycles, using bout distribution, holds great promise for classifying sleep, its disorders, and tracking its developmental milestones across the lifespan in humans.

Power law versus exponential state transition dynamics: application to sleep-wake architecture

BACKGROUND

Despite the common experience that interrupted sleep has a negative impact on waking function, the features of human sleep-wake architecture that best distinguish sleep continuity versus fragmentation remain elusive. In this regard, there is growing interest in characterizing sleep architecture using models of the temporal dynamics of sleep-wake stage transitions. In humans and other mammals, the state transitions defining sleep and wake bout durations have been described with exponential and power law models, respectively. However, sleep-wake stage distributions are often complex, and distinguishing between exponential and power law processes is not always straightforward. Although mono-exponential distributions are distinct from power law distributions, multi-exponential distributions may in fact resemble power laws by appearing linear on a log-log plot.

METHODOLOGY/PRINCIPAL FINDINGS

To characterize the parameters that may allow these distributions to mimic one another, we systematically fitted multi-exponential-generated distributions with a power law model, and power law-generated distributions with multi-exponential models. We used the Kolmogorov-Smirnov method to investigate goodness of fit for the "incorrect" model over a range of parameters. The "zone of mimicry" of parameters that increased the risk of mistakenly accepting power law fitting resembled empiric time constants obtained in human sleep and wake bout distributions.

CONCLUSIONS/SIGNIFICANCE

Recognizing this uncertainty in model distinction impacts interpretation of transition dynamics (self-organizing versus probabilistic), and the generation of predictive models for clinical classification of normal and pathological sleep architecture.