The effects of exercise on dynamic sleep morphology in healthy controls and patients with chronic fatigue syndrome

Phenotypic Interindividual Differences in the Dynamic Structure of Sleep in Healthy Young Adults

Introduction

Evaluating the dynamic structure of sleep may yield new insights into the mechanisms underlying human sleep physiology.

Methods

We analyzed data from a 12-day, 11-night, strictly controlled laboratory study with an adaptation night, 3 iterations of a baseline night followed by a recovery night after 36 h of total sleep deprivation, and a final recovery night. All sleep opportunities were 12 h in duration (22:00-10:00) and recorded with polysomnography (PSG). The PSG records were scored for the sleep stages: rapid eye movement (REM) sleep; non-REM (NREM) stage 1 sleep (S1), stage 2 sleep (S2), and slow wave sleep (SWS); and wake (W). Phenotypic interindividual differences were assessed using indices of dynamic sleep structure - specifically sleep stage transitions and sleep cycle characteristics - and intraclass correlation coefficients across nights.

Results

NREM/REM sleep cycles and sleep stage transitions exhibited substantial and stable interindividual differences that were robust across baseline and recovery nights, suggesting that mechanisms underlying the dynamic structure of sleep are phenotypic. In addition, the dynamics of sleep stage transitions were found to be associated with sleep cycle characteristics, with a significant relationship between the length of sleep cycles and the degree to which S2-to-W/S1 and S2-to-SWS transitions were in equilibrium.

Discussion

Our findings are consistent with a model for the underlying mechanisms that involves three subsystems - characterized by S2-to-W/S1, S2-to-SWS, and S2-to-REM transitions - with S2 playing a hub-like role. Furthermore, the balance between the two subsystems within NREM sleep (S2-to-W/S1 and S2-to-SWS) may serve as a basis for the dynamic regulation of sleep structure and may represent a novel target for interventions aiming to improve sleep.

Instability of non-REM sleep in older women evaluated by sleep-stage transition and envelope analyses

Study objective

Traditionally, age-related deterioration of sleep architecture in older individuals has been evaluated by visual scoring of polysomnographic (PSG) recordings with regard to total sleep time and latencies. In the present study, we additionally compared the non-REM sleep (NREM) stage and delta, theta, alpha, and sigma wave stability between young and older subjects to extract features that may explain age-related changes in sleep.

Methods

Polysomnographic recordings were performed in 11 healthy older (72.6 ± 2.4 years) and 9 healthy young (23.3 ± 1.1 years) females. In addition to total sleep time, the sleep stage, delta power amplitude, and delta, theta, alpha, and sigma wave stability were evaluated by sleep stage transition analysis and a novel computational method based on a coefficient of variation of the envelope (CVE) analysis, respectively.

Results

In older subjects, total sleep time and slow-wave sleep (SWS) time were shorter whereas wake after sleep onset was longer. The number of SWS episodes was similar between age groups, however, sleep stage transition analysis revealed that SWS was less stable in older individuals. NREM sleep stages in descending order of delta power were: SWS, N2, and N1, and delta power during NREM sleep in older subjects was lower than in young subjects. The CVE of the delta-band is an index of delta wave stability and showed significant differences between age groups. When separately analyzed for each NREM stage, different CVE clusters in NREM were clearly observed between young and older subjects. A lower delta CVE and amplitude were also observed in older subjects compared with young subjects in N2 and SWS. Additionally, lower CVE values in the theta, alpha and sigma bands were also characteristic of older participants.

Conclusion

The present study shows a decrease of SWS stability in older subjects together with a decrease in delta wave amplitude. Interestingly, the decrease in SWS stability coincided with an increase in short-term delta, theta, sigma, and alpha power stability revealed by lower CVE. Loss of electroencephalograms (EEG) variability might be a useful marker of brain age.

Pain-Related Post-Exertional Malaise in Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) and Fibromyalgia: A Systematic Review and Three-Level Meta-Analysis

OBJECTIVE

Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) and fibromyalgia (FM) are two debilitating, moderately comorbid illnesses in which chronic musculoskeletal pain symptoms are prevalent. These individuals can experience post-exertional malaise (PEM), a phenomenon in which symptom severity is worsened for 24 hours or longer after physical stress, but the pain-related component of PEM is not well characterized.

DESIGN

Systematic review and meta-analysis.

METHODS

Case-control studies involving adults with ME/CFS or FM and measuring pain symptoms before and after exposure to a standardized aerobic exercise test were included. Hedges' d effect sizes were aggregated with random-effects models, and potential moderators were explored with meta-regression analysis. Results were adjusted for nesting effects with three-level modeling.

RESULTS

Forty-five effects were extracted from 15 studies involving 306 patients and 292 healthy controls. After adjusting for nesting effects, we observed a small to moderate effect indicating higher post-exercise pain in patients than in controls (Hedges' d = 0.42; 95% confidence interval [CI]: 0.16-0.67). The mean effect was significantly moderated by pain measurement time point (b = -0.19, z = -2.57, P = 0.01), such that studies measuring pain 8-72 hours after exercise showed larger effects (d = 0.71, 95% CI = 0.28-1.14) than did those measuring pain 0-2 hours after exercise (d = 0.32, 95% CI = 0.10-0.53).

CONCLUSIONS

People with ME/CFS and FM experience small to moderate increases in pain severity after exercise, which confirms pain as a component of PEM and emphasizes its debilitating impact in ME/CFS and FM. Future directions include determining mechanisms of pain-related PEM and developing exercise prescriptions that minimize symptom exacerbation in these illnesses.

Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach

The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.

Systematic Review of Sleep Characteristics in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

(1) Background—Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multifaceted illness characterized by profound and persistent fatigue unrelieved by rest along with a range of other debilitating symptoms. Experiences of unrefreshing and disturbed sleep are frequently described by ME/CFS patients. This is the first systematic review assessing sleep characteristics in ME/CFS. The aim of this review is to determine whether there are clinical characteristics of sleep in ME/CFS patients compared to healthy controls using objective measures such as polysomnography and multiple sleep latency testing. (2) Methods—the following databases—Pubmed, Embase, Medline (EBSCO host) and Web of Science, were systematically searched for journal articles published between January 1994 to 19 February 2021. Articles that referred to polysomnography or multiple sleep latency testing and ME/CFS patients were selected, and further refined through use of specific inclusion and exclusion criteria. Quality and bias were measured using the Joanna Briggs Institute checklist. (3) Results—twenty observational studies were included in this review. The studies investigated objective measures of sleep quality in ME/CFS. Subjective measures including perceived sleep quality and other quality of life factors were also described. (4) Conclusions—Many of the parameters measured including slow- wave sleep, apnea- hypopnea index, spectral activity and multiple sleep latency testing were inconsistent across the studies. The available research on sleep quality in ME/CFS was also limited by recruitment decisions, confounding factors, small sample sizes and non-replicated findings. Future well-designed studies are required to understand sleep quality in ME/CFS patients.

Sleep stage dynamics in young patients with sleep bruxism

Study Objectives

We hypothesized that sleep stage dynamics are different in patients with sleep bruxism (SB) and that these changes are associated with the occurrence of rhythmic masticatory muscle activity (RMMA).

Methods

Fifteen healthy controls and 15 patients with SB underwent overnight polysomnography. Sleep variables and survival curves of continuous runs of each sleep stage were compared between the groups. Stage transition dynamics and the probability of stage fragmentation were analyzed for three epochs before and after the epoch with RMMA. Survival curves of continuous runs of each sleep stage, terminated with or without RMMA, were also compared.

Results

There were no significant differences in sleep variables between the groups, except for shorter sleep latency, shorter rapid eye movement (REM) latency, and longer total N1 duration in SB patients than in controls. REM sleep and N2 were significantly less continuous in SB patients than in controls. In the SB group, stage fragmentation probability was significantly increased for the epoch with RMMA compared with the baseline for all stages. Meanwhile, the occurrence of RMMA did not affect the continuity of N2 or REM; however, the occurrence of RMMA was preceded by more continuous N3 runs.

Conclusions

Sleep stage dynamics differed between SB patients and controls. RMMA does not result in sleep disruption but is likely associated with dissipation of sleep pressure. Less continuity of REM sleep in SB may provide insights into the underlying pathophysiological mechanisms of SB, which may be related to REM sleep processes such as cortical desynchronized states or brainstem activation.

Markov modeling of sleep stage transitions and ultradian REM sleep rhythm

OBJECTIVE

One of the highly characteristic features of sleep is the cyclic occurrence of non-rapid eye movement (NREM) and REM sleep, which is referred to as the ultradian rhythm of sleep. Even though REM sleep was discovered over half a century ago, surprisingly, the mechanism of the ultradian REM sleep rhythm has not yet been fully elucidated. In the present study, we aim to provide a mechanistic insight into the generation of the ultradian REM sleep rhythm. Approach and Main results: By simulating hypnograms with the dynamic features of sleep stage transitions, i.e. stage transition probabilities and stage-specific survival time functions, we show that the second-order Markov transition probabilities and the stage-specific survival time functions can reproduce the central position (∼90 min) of the REM-onset intervals (ROIs), but with a larger variance in distribution. In addition, we demonstrate the direct effect of the increased probability of the transitions from light to deep sleep within NREM sleep on the prolongation of the ROIs in a dose-response manner.

SIGNIFICANCE

These results suggest that dynamic sleep stage transitions constitute the basis of the formation of the ultradian rhythm of sleep; however, further elaboration of the model would be required to reduce the variability in rhythmicity.

Frontier studies on fatigue, autonomic nerve dysfunction, and sleep-rhythm disorder

Fatigue is defined as a condition or phenomenon of decreased ability and efficiency of mental and/or physical activities, caused by excessive mental or physical activities, diseases, or syndromes. It is often accompanied by a peculiar sense of discomfort, a desire to rest, and reduced motivation, referred to as fatigue sensation. Acute fatigue is a normal condition or phenomenon that disappears after a period of rest; in contrast, chronic fatigue, lasting at least 6 months, does not disappear after ordinary rest. Chronic fatigue impairs activities and contributes to various medical conditions, such as cardiovascular disease, epileptic seizures, and death. In addition, many people complain of chronic fatigue. For example, in Japan, more than one third of the general adult population complains of chronic fatigue. It would thus be of great value to clarify the mechanisms underlying chronic fatigue and to develop efficient treatment methods to overcome it. Here, we review data primarily from behavioral, electrophysiological, and neuroimaging experiments related to neural dysfunction as well as autonomic nervous system, sleep, and circadian rhythm disorders in fatigue. These data provide new perspectives on the mechanisms underlying chronic fatigue and on overcoming it.

Chromosomal DNA damage in APOE ɛ4 carriers and noncarriers does not appear to be different

DNA damage may play a key role in promoting disease-onset and accelerated disease progression in Alzheimer's disease (AD) by increasing the rates of neuronal cell death. The ɛ4 allele of the APOE gene is the best characterised genetic risk factor for AD, however, it is unknown if APOE ɛ4 carriers exhibit increased levels of DNA damage which may contribute to increased AD risk. 175 healthy participants (aged 34-67 years old) from South Australia were recruited into the study and provided a single blood sample for the isolation of peripheral blood lymphocytes, APOE genotyping and lymphocyte chromosomal DNA damage analysis using the Cytokinesis-Block micronucleus cytome (CBMN-Cyt) assay with the micronucleus index being the primary outcome measure. When compared to non-APOE ɛ4 carriers, APOE ɛ4 carriers did not exhibit altered rates of i) cell division, represented by the nuclear division index (NDI, P = 0.372), ii) cell death as represented by apoptotic (P = 0.457) and necrotic (P = 0.393) frequencies and iii) chromosomal DNA damage as indicated by the number of micronuclei (MNi, P = 0.795), nucleoplasmic bridges (NPBs, P = 0.221) or nuclear buds (NBUDs, P = 0.293) scored in binucleated cells. In conclusion, although we and others have previously shown that rates of chromosomal DNA damage measured using the CBMN-Cyt assay are elevated in individuals with cognitive impairment, in this South Australian cohort the frequency of genome instability is not substantially influenced by the presence of the APOE ɛ4 allele.

Myalgic Encephalomyelitis: Symptoms and Biomarkers

Myalgic Encephalomyelitis (ME) continues to cause significant morbidity worldwide with an estimated one million cases in the United States. Hurdles to establishing consensus to achieve accurate evaluation of patients with ME continue, fueled by poor agreement about case definitions, slow progress in development of standardized diagnostic approaches, and issues surrounding research priorities. Because there are other medical problems, such as early MS and Parkinson's Disease, which have some similar clinical presentations, it is critical to accurately diagnose ME to make a differential diagnosis. In this article, we explore and summarize advances in the physiological and neurological approaches to understanding, diagnosing, and treating ME. We identify key areas and approaches to elucidate the core and secondary symptom clusters in ME so as to provide some practical suggestions in evaluation of ME for clinicians and researchers. This review, therefore, represents a synthesis of key discussions in the literature, and has important implications for a better understanding of ME, its biological markers, and diagnostic criteria. There is a clear need for more longitudinal studies in this area with larger data sets, which correct for multiple testing.