Impact of Sleep and Circadian Disruption on Energy Balance and Diabetes: A Summary of Workshop Discussions

A workshop was held at the National Institute for Diabetes and Digestive and Kidney Diseases with a focus on the impact of sleep and circadian disruption on energy balance and diabetes. The workshop identified a number of key principles for research in this area and a number of specific opportunities. Studies in this area would be facilitated by active collaboration between investigators in sleep/circadian research and investigators in metabolism/diabetes. There is a need to translate the elegant findings from basic research into improving the metabolic health of the American public. There is also a need for investigators studying the impact of sleep/circadian disruption in humans to move beyond measurements of insulin and glucose and conduct more in-depth phenotyping. There is also a need for the assessments of sleep and circadian rhythms as well as assessments for sleep-disordered breathing to be incorporated into all ongoing cohort studies related to diabetes risk. Studies in humans need to complement the elegant short-term laboratory-based human studies of simulated short sleep and shift work etc. with studies in subjects in the general population with these disorders. It is conceivable that chronic adaptations occur, and if so, the mechanisms by which they occur needs to be identified and understood. Particular areas of opportunity that are ready for translation are studies to address whether CPAP treatment of patients with pre-diabetes and obstructive sleep apnea (OSA) prevents or delays the onset of diabetes and whether temporal restricted feeding has the same impact on obesity rates in humans as it does in mice

Dynamics of Snoring Sounds and Its Connection with Obstructive Sleep Apnea

Snoring is extremely common in the general population and when irregular may indicate the presence of obstructive sleep apnea. We analyze the overnight sequence of wave packets --- the snore sound --- recorded during full polysomnography in patients referred to the sleep laboratory due to suspected obstructive sleep apnea. We hypothesize that irregular snore, with duration in the range between 10 and 100 seconds, correlates with respiratory obstructive events. We find that the number of irregular snores --- easily accessible, and quantified by what we call the snore time interval index (STII) --- is in good agreement with the well-known apnea-hypopnea index, which expresses the severity of obstructive sleep apnea and is extracted only from polysomnography. In addition, the Hurst analysis of the snore sound itself, which calculates the fluctuations in the signal as a function of time interval, is used to build a classifier that is able to distinguish between patients with no or mild apnea and patients with moderate or severe apnea

Cardiac autonomic control in the obstructive sleep apnea

Introduction: The sympathetic activation is considered to be the main mechanism involved in the development of cardiovascular diseases in obstructive sleep apnea (OSA). The heart rate variability (HRV) analysis represents a non-invasive tool allowing the study of the autonomic nervous system. The impairment of HRV parameters in OSA has been documented. However, only a few studies tackled the dynamics of the autonomic nervous system during sleep in patients having OSA.Aims: To analyze the HRVover sleep stages and across sleep periods in order to clarify the impact of OSA on cardiac autonomic modulation. The second objective is to examine the nocturnal HRV of OSA patients to find out which HRV parameter is the best to reflect the symptoms severity.Methods: The study was retrospective. We have included 30 patients undergoing overnight polysomnography. Subjects were categorized into two groups according to apneahypopnea index (AHI): mild-to-moderate OSAS group (AHI: 5-30) and severe OSAS group (AHI>30). The HRV measures for participants with low apneahypopnea indices were compared to those of patients with high rates of apneahypopnea across the sleep period and sleep stages.Results: HRV measures during sleep stages for the group with low rates of apneahypopnea have indicated a parasympathetic activation during non-rapid eye movement (NREM) sleep. However, no significant difference has been observed in the high AHI group except for the mean of RR intervals (mean RR). The parasympathetic activity tended to increase across the night but without a statistical difference. After control of age and body mass index, the most significant correlation found was for the mean RR (p =0.0001, r = -0.248).Conclusion: OSA affects sympathovagal modulation during sleep, and this impact has been correlated to the severity of the disease. The mean RR seemed to be a better index allowing the sympathovagal balance appreciation during the night in OSA.Keywords: autonomic nervous system; sleep apnea; heart rate; sleep; circadia

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep

Heart rate variability in normal and pathological sleep

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. the application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). in summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. the use of these tools could provide important information of clinical and prognostic relevance.European Regional Development Fund-Project FNUSA-ICRCUniv Milan, L Sacco Hosp, Dept Biomed & Clin Sci L Sacco, Div Med & Pathophysiol, I-20157 Milan, ItalyOsped Niguarda Ca Granda, Ctr Epilepsy Surg C Munari, Ctr Sleep Med, Dept Neurosci, Milan, ItalyUniversidade Federal de São Paulo, Inst Sci & Technol, Dept Sci & Technol, São Paulo, BrazilUniv Fdn Cardiol, Inst Cardiol Rio Grande do Sul, Porto Alegre, RS, BrazilFdn S Maugeri, Sleep Med Unit, Veruno, ItalySt Annes Univ Hosp, Int Clin Res Ctr, Brno, Czech RepublicUniversidade Federal de São Paulo, Inst Sci & Technol, Dept Sci & Technol, São Paulo, BrazilEuropean Regional Development Fund-Project FNUSA-ICRC: CZ.1.05/1.1.00/02.0123Web of Scienc

Validation of peripheral arterial tonometry as tool for sleep assessment in chronic obstructive pulmonary disease

Obstructive sleep apnea (OSA) worsens outcomes in Chronic Obstructive Pulmonary Disease (COPD), and reduced sleep quality is common in these patients. Thus, objective sleep monitoring is needed, but polysomnography (PSG) is cumbersome and costly. The WatchPAT determines sleep by a pre-programmed algorithm and has demonstrated moderate agreement with PSG in detecting sleep stages in normal subjects and in OSA patients. Here, we validated WatchPAT against PSG in COPD patients, hypothesizing agreement in line with previous OSA studies. 16 COPD patients (7 men, mean age 61 years), underwent simultaneous overnight recordings with PSG and WatchPAT. Accuracy in wake and sleep staging, and concordance regarding total sleep time (TST), sleep efficiency (SE), and apnea hypopnea index (AHI) was calculated. Compared to the best fit PSG score, WatchPAT obtained 93% sensitivity (WatchPAT = sleep when PSG = sleep), 52% specificity (WatchPAT = wake when PSG = wake), 86% positive and 71% negative predictive value, Cohen’s Kappa (κ) = 0.496. Overall agreement between WatchPat and PSG in detecting all sleep stages was 63%, κ = 0.418. The mean(standard deviation) differences in TST, SE and AHI was 25(61) minutes (p = 0.119), 5(15) % (p = 0.166), and 1(5) (p = 0.536), respectively. We conclude that in COPD-patients, WatchPAT detects sleep stages in moderate to fair agreement with PSG, and AHI correlates well.Obstructive sleep apnea (OSA) worsens outcomes in Chronic Obstructive Pulmonary Disease (COPD), and reduced sleep quality is common in these patients. Thus, objective sleep monitoring is needed, but polysomnography (PSG) is cumbersome and costly. The WatchPAT determines sleep by a pre-programmed algorithm and has demonstrated moderate agreement with PSG in detecting sleep stages in normal subjects and in OSA patients. Here, we validated WatchPAT against PSG in COPD patients, hypothesizing agreement in line with previous OSA studies. 16 COPD patients (7 men, mean age 61 years), underwent simultaneous overnight recordings with PSG and WatchPAT. Accuracy in wake and sleep staging, and concordance regarding total sleep time (TST), sleep efficiency (SE), and apnea hypopnea index (AHI) was calculated. Compared to the best fit PSG score, WatchPAT obtained 93% sensitivity (WatchPAT = sleep when PSG = sleep), 52% specificity (WatchPAT = wake when PSG = wake), 86% positive and 71% negative predictive value, Cohen’s Kappa (κ) = 0.496. Overall agreement between WatchPat and PSG in detecting all sleep stages was 63%, κ = 0.418. The mean(standard deviation) differences in TST, SE and AHI was 25(61) minutes (p = 0.119), 5(15) % (p = 0.166), and 1(5) (p = 0.536), respectively. We conclude that in COPD-patients, WatchPAT detects sleep stages in moderate to fair agreement with PSG, and AHI correlates well.publishedVersio

Automated Sleep Apnea Quantification Based on Respiratory Movement

Obstructive sleep apnea (OSA) is a prevalent and treatable disorder of neurological and medical importance that is traditionally diagnosed through multi-channel laboratory polysomnography(PSG). However, OSA testing is increasingly performed with portable home devices using limited physiological channels. We tested the hypothesis that single channel respiratory effort alone could support automated quantification of apnea and hypopnea events. We developed a respiratory event detection algorithm applied to thoracic strain-belt data from patients with variable degrees of sleep apnea. We optimized parameters on a training set (n=57) and then tested performance on a validation set (n=59). The optimized algorithm correlated significantly with manual scoring in the validation set (R2 = 0.73 for training set, R2 = 0.55 for validation set; p0.92 and >0.85 using apnea-hypopnea index cutoff values of 5 and 15, respectively. Our findings demonstrate that manually scored AHI values can be approximated from thoracic movements alone. This finding has potential applications for automating laboratory PSG analysis as well as improving the performance of limited channel home monitors

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.Peer Reviewe

Objective Quantification of Daytime Sleepiness

BACKGROUND: Sleep problems affect people of all ages, race, gender, and socioeconomic classifications. Undiagnosed sleep disorders significantly and adversely impact a person’s level of academic achievement, job performance, and subsequently, socioeconomic status. Undiagnosed sleep disorders also negatively impact both direct and indirect costs for employers, the national government, and the general public. Sleepiness has significant implications on quality of life by impacting occupational performance, driving ability, cognition, memory, and overall health. The purpose of this study is to describe the prevalence of daytime sleepiness, as well as other quantitative predictors of sleep continuity and quality. METHODS: Population data from the CDC program in fatigue surveillance were used for this secondary analysis seeking to characterize sleep quality and continuity variables. Each participant underwent a standard nocturnal polysomnography and a standard multiple sleep latency test (MSLT) on the subsequent day. Frequency and chi-square tests were used to describe the sample. One-Way Analysis of Variance (ANOVA) was used to compare sleep related variables of groups with sleep latencies of \u3c5 \u3eminutes, 5-10 minutes, and \u3e10 minutes. Bivariate and multivariate logistic regression was used to examine the association of the sleep variables with sleep latency time. RESULTS: The mean (SD) sleep latency of the sample was 8.8 (4.9) minutes. Twenty-four individuals had ≥1 SOREM, and approximately 50% of participants (n = 100) met clinical criteria for a sleep disorder. Individuals with shorter sleep latencies, compared to those with longer latencies reported higher levels of subjective sleepiness, had higher sleep efficiency percentages, and longer sleep times. The Epworth Sleepiness Scale, sleep efficiency percentage, total sleep time, the presence of a sleep disorder, and limb movement index were positively associated with a mean sleep latency of \u3c5 \u3eminutes. CONCLUSIONS: The presence of a significant percentage of sleep disorders within our study sample validate prior suggestions that such disorders remain unrecognized, undiagnosed, and untreated. In addition, our findings confirm questionnaire-based surveys that suggest a significant number of the population is excessively sleepy, or hypersomnolent. Therefore, the high prevalence of sleep disorders and the negative public health effects of daytime sleepiness demand attention. Further studies are now required to better quantify levels daytime sleepiness, within a population based sample, to better understand their impact upon morbidity and mortality. This will not only expand on our current understanding of daytime sleepiness, but it will also raise awareness surrounding its significance and relation to public health