SLEEP

Winiger, Evan A; Huggett, Spencer B; Hatoum, Alexander S; Friedman, Naomi P; Drake, Christopher L; Wright, Kenneth P; Hewitt, John K

doi: 10.1093/sleep/zsz293pmid: 31855253

Study ObjectivesEstimate the genetic and environmental influences on the relationship between onset of regular cannabis use and young adult insomnia.MethodsIn a population-based twin cohort of 1882 twins (56% female, mean age = 22.99, SD = 2.97) we explored the genetic/environmental etiology of the relationship between onset of regular cannabis use and insomnia-related outcomes via multivariate twin models.ResultsControlling for sex, current depression symptoms, and prior diagnosis of an anxiety or depression disorder, adult twins who reported early onset for regular cannabis use (age 17 or younger) were more likely to have insomnia (β = 0.07, p = 0.024) and insomnia with short sleep on weekdays (β = 0.08, p = 0.003) as young adults. We found significant genetic contributions for the onset of regular cannabis use (a2 = 76%, p < 0.001), insomnia (a2 = 44%, p < 0.001), and insomnia with short sleep on weekdays (a2 = 37%, p < 0.001). We found significant genetic correlations between onset of regular use and both insomnia (rA = 0.20, p = 0.047) and insomnia with short sleep on weekdays (rA = 0.25, p = 0.008) but no significant environmental associations between these traits.ConclusionsWe found common genetic liabilities for early onset of regular cannabis use and insomnia, implying pleiotropic influences of genes on both traits.

Blanken, Tessa F; Borsboom, Denny; Penninx, Brenda Wjh; Van Someren, Eus Jw

doi: 10.1093/sleep/zsz288pmid: 31789381

Study ObjectivesMajor depressive disorder (MDD) is the leading cause of disability worldwide. Its high recurrence rate calls for prevention of first-onset MDD. Although meta-analysis suggested insomnia as the strongest modifiable risk factor, previous studies insufficiently addressed that insomnia might also occur as a residual symptom of unassessed prior depression, or as a comorbid complaint secondary to other depression risks.MethodsIn total, 768 participants from the Netherlands Study of Depression and Anxiety who were free from current and lifetime MDD were followed-up for four repeated assessments, spanning 6 years in total. We performed separate Cox proportional hazard analyses to evaluate whether baseline insomnia severity, short-sleep duration, and individual insomnia complaints prospectively predicted first-onset MDD during follow-up. The novel method of network outcome analysis (NOA) allowed us to sort out whether there is any direct predictive value of individual insomnia complaints among several other complaints that are associated with insomnia.ResultsOver 6-year follow-up, 141 (18.4%) were diagnosed with first-onset MDD. Insomnia severity but not sleep duration predicted first-onset MDD (HR = 1.11, 95% CI: 1.07–1.15), and this was driven solely by the insomnia complaint difficulty initiating sleep (DIS) (HR = 1.10, 95% CI: 1.04–1.16). NOA likewise identified DIS only to directly predict first-onset MDD, independent of four other associated depression complaints.ConclusionsWe showed prospectively that DIS is a risk factor for first-onset MDD. Among the different other insomnia symptoms, the specific treatment of DIS might be the most sensible target to combat the global burden of depression through prevention.

Cook, Jesse D; Ferry, David G; Tran, Kieulinh M

doi: 10.1093/sleep/zsz304pmid: 31837225

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that culminates in debilitating dementia. In 2011, the National Institute on Aging modified the AD diagnostic guidelines to account for the developing nature of this condition [1–3]. This modification recognizes an AD spectrum that is best captured by three stages: a preclinical stage with no symptoms, a middle stage of mild cognitive impairment, and a final stage with diagnosable dementia [4]. The course of AD is extremely burdensome physically, emotionally, and financially for the patient, their family, and affiliated support network [5]. Additionally, the prevalence, incidence, and associated costs make this condition a global health crisis [6]. As such, improving the understanding of AD’s pathophysiology and etiology is imperative to enhance the risk assessment, disease prevention, and treatment efficacy across the various AD spectrum stages. Much research attention has been put forth to elucidate valid and reliable AD biomarkers that can serve as targets for risk assessment and intervention [7]. Concentrations of amyloid-beta (Aβ) proteins and neurofibrillary tangles of aggregated tau have emerged as viable early-onset biomarkers [7, 8]. Research has shown that patients with AD consistently show higher concentrations of tau and Aβ, specifically the 42-aminoacid form (Aβ42) [7, 9]. However, the causal factors underlying the formation of these biomarkers has not been fully established. Over the past decade there has been an increase in the recognition of the prominent role that sleep plays in the pathogenesis of AD [8, 10, 11]. Sleep has been shown to increase the clearance of Aβ as well as lower Aβ production, in both human and rodent investigations [12–14]. Complimentary, sleep deprivation has been evidenced to increase tau levels, as well as accelerate the spread of tau across neural networks [11]. However, in-laboratory investigations have produced mixed-results across these AD-related biomarkers. For instance, an investigation that evaluated the effects of a single night of total sleep deprivation (TSD) on AD-related biomarkers in a sample of healthy, middle-aged men identified Aβ42 increases without changes in tau [14]. Such an investigation highlights the impetus for research purposed to further clarify the relationship between sleep and AD-related biomarkers. One specific avenue of this relationship warranting further research attention is the cumulative effects of chronic sleep deprivation on AD-related biomarkers, which has been largely unexplored within a laboratory setting. In their recent study published in SLEEP, Olsson and colleagues performed an investigation purposed to address this deficiency in the scientific literature [15]. Primarily, this investigation was designed to assess the cumulative effects of multiple nights of partial sleep deprivation (PSD) on cerebrospinal fluid (CSF) biomarkers of AD, including Aβ42 and tau. The authors hypothesized that repetitive nights of PSD would lead to significant impairment in physiological clearance of CSF biomarkers and that the effect on Aβ42 would be augmented compared with that which was observed after a single night of TSD. Sixteen of the 31 participants that underwent initial screening were enrolled in this randomized, crossover study that evaluated sleep and CSF biomarkers associated with AD across two conditions: PSD and controlled sleep (CS). PSD occurred in-laboratory under polysomnographic (PSG) evaluation for five consecutive nights. Participants arrived at 10:00 p.m., were monitored until a bedtime between 3:00 and 4:00 a.m., and were awoken 4 h after lights out by laboratory personnel. CS was characterized as a time in bed of 8 h. Unlike PSD, only the first-and-last night of the five-night CS condition occurred in-laboratory under PSG evaluation. In addition to in-laboratory assessment, actigraphy (ActiGraph GT3X+) was also collected across the entire experiment, with specific purpose to assess protocol adherence during the CS condition nights that occurred outside of laboratory. Protocol adherence was maintained by at least 420 min of total sleep time during CS. A washout window of 4 weeks was utilized to separate the conditions. CSF samples were collected using lumbar punctures (L3/L4 or L4/L5) on the morning (8:00–9:00 a.m.) immediately following the completion of both PSD and CS. A multitude of assay techniques were used to comprehensively assess concentrations of CSF biomarkers associated with AD. CS values acted as controls in comparison against values obtained during PSD. A predominately male final sample included data from 13 young adults. In terms of sleep, CS, and PSD were comparable in slow-wave sleep (SWS) duration. However, significant reductions were observed for PSD, relative to CS, in the durations of nonrapid eye movement (NREM) stage 1, NREM stage 2, and REM. In terms of CSF concentrations, no differences between conditions were identified except for an expected increase in orexin associated with PSD [16]. Taken together, these results suggest that SWS stability is associated with stability in CSF-derived, AD-related biomarker concentrations. The primary results from this investigation carry clinical import as well as provide direction for future research. Due to the unique design nature of repetitive PSD, which leads to a physiological emphasis for rebounding SWS [17], it is not surprising for CS and PSD conditions to be comparable in SWS yet significantly differ in REM duration. However, given the absence of significant differences across all assessed Aβ proteins and other AD-related biomarkers, such as tau, these PSG macrostructure results provide further support for the unique import of SWS in AD pathogenesis. A previously conducted investigation assessed the effect of SWS disruption on AD-related biomarkers demonstrated a significant relationship between change in slow wave/delta power (0.5–4 Hz) and Aβ levels [18]. Specifically, greater suppression of delta power resulted in statistically significant increases of Aβ levels [18]. Notably, this relationship did not present for non-REM or REM duration, nor extended to tau [18]. Although these results do align with results derived from Olsson and colleagues, it is paramount to note that these relationships were specific to Aβ-40. Additionally, these authors did not appear to evaluate the relationship between SWS duration and Aβ levels. As such, further research is necessary to clarify the relationships between SWS duration, slow wave/delta power, and AD-related biomarkers. Nevertheless, the growing body of evidence highlighting the importance of SWS on Aβ levels must be acknowledged. Although a full comparison of Aβ-40 and Aβ-42 is beyond the scope of this commentary, it is important to acknowledge potential differences between these two major Aβ isoforms on AD pathology. Despite greater concentrations of Aβ-40 in CSF, Aβ-42 has been evidenced as the major component of amyloid plaques in AD [19]. A recently conducted evaluation of these Aβ isoforms highlighted slight differences in morphology and suggested that differential rates of aggregation are likely to explain the dissimilar influence on AD pathology, whereby Aβ-42’s faster rate of aggregation accounts for its primary role in AD-related amyloid plaque formulation [19]. However, further research is needed to corroborate this aggregation rate finding. Moreover, some argue that the Aβ-42/Aβ-40 ratio is a better clinical indicator of AD, rather than Aβ-42 alone [20]. As such, it is imperative that the field determine a universal, Aβ outcome of focus to better evaluate results across investigations, as well as optimize clinical assessment. From a clinical perspective, the identified relationship between SWS and Aβ levels may have benefit for both AD risk assessment and disease intervention. Deficiencies in SWS could be included in a multifactorial risk profile for AD, along with other prominent factors that have been indicated to heighten disease likelihood [21]. This overall risk profile could then be utilized to steer disease prevention or treatment in an approach personalized for the patient. At present, nonpharmacologic techniques such as transcranial direct-current stimulation, transcranial magnetic stimulation, acoustic stimulation, and exercise have been evidenced to enhance SWS [22–24]. Presently, a body of evidence has emerged implicating acoustic stimulation as a viable and reliable technique for SWS enhancement that is associated with beneficial cognitive effects [25–27]. However, these effects have largely been determined acutely and its long-term efficacy remains unknown [28]. Multiple pharmacological agents have been indicated to enhance SWS [24–29], with some evidenced to be influencing SWS by increasing GABA synaptic levels (e.g. tiagabine) [24, 30]. However, these medications, and their associated SWS enhancement, have displayed inconsistent memory and cognitive effects [24, 30]. Additionally, they have been associated with increased dementia risk, which has been hypothesized to be a product of their ancillary suppression of sleep spindles [28]. Given the potential of preventing and treating AD through SWS enhancement, it is imperative that future research be purposed to determine effective, reliable, and sustainable pharmacologic and nonpharmacologic SWS enhancers in the context of AD biomarker formation. An important subsequent step toward the clinical integration of SWS assessment and treatment in AD is to elucidate the cortical locations whereby these sleep-specific deficiencies are occurring. Although standard six-channel electroencephalography (EEG) can be useful in determining PSG macrostructure changes, this measurement technique is spatially limited. As such, expanding on the SWS findings using high-density EEG (hdEEG) is necessary. This approach could be further enhanced through the coregistration of hdEEG with magnetic resonance imaging, which would allow for more precise and accurate understanding of the cortical regions involved in these SWS findings. After this, SWS enhancement techniques targeting these cortical regions could be evaluated, which would help clarify the clinical utility of this intervention. However, at present, our narrow understanding of the underlying cortical regions and mechanisms associated with these SWS findings limits clinical applicability. Although Olsson and colleagues’ findings suggest a less prominent role of REM in the formation of AD-related CSF biomarkers, future research is necessary to further substantiate this finding. Presently, a body of research exists highlighting REM’s prominent role in healthy aging and cognitive functioning [31, 32]. Additionally, the orexinergic system, which influences REM [33], has been acknowledged as a mechanistic driver of neurodegeneration [34–37]. Thus, it seems likely that REM has a relevant role in the pathogenesis of AD. To improve the field’s understanding of REM’s role in AD pathogenesis, an investigation that assesses CSF associated biomarkers of AD during REM deprivation is warranted. If CSF biomarkers associated with AD are undisturbed from the absence of REM, then there would be cumulative support for Olsson and colleagues’ finding of REM playing a less prominent role than SWS in AD pathogenesis. However, at this time, not enough evidence exists to truly legitimize the hierarchical recognition of SWS over REM in the pathogenesis of AD. Mechanistically, glymphatic system functionality must be considered when considering these findings and AD pathogenesis. The glymphatic system removes interstitial metabolic biproducts, such as Aβ and tau, and has been shown to down-regulate during wake and up-regulate during sleep [12, 38, 39]. Recently, evidence has emerged suggesting an intimate relationship between NREM sleep, and particularly Delta EEG activity, and glymphatic system activation [39, 40]. Olsson and colleagues’ findings align with this relationship, as AD-related biomarkers were equivalent across conditions that were indistinguishable based on SWS, which suggests that the metabolite clearance was comparable with SWS maintenance. However, much of the literature surrounding the relationship between sleep and the glymphatic system has been derived from animal models. As such, future research in humans aimed at clarifying the specifics that underly sleep’s regulatory role of the glymphatic system is an important next step for a better understanding of AD pathogenesis. Although Olsson and colleagues appropriately discussed some limitations of this investigation (e.g. small sample size and potential of diurnal variations in CSF Aβ concentrations), we believe there is one more potentially limiting characteristics worth acknowledging. It is unclear based on the described methods whether participants were screened for psychiatric illness, which is important given that psychiatric illness is often associated with abnormal PSG architecture [41]. Overall, this characteristic and limiting sample size provide reason to interpret the results from this investigation with appropriate caution. Although a larger sample size is challenging due to constraints from time, cost, and study resources, replicating these findings using a substantially larger sample would be of great benefit to the field. Nevertheless, this was a well-performed study with meriting characteristics such as the ability to collect multiple nights of in-laboratory PSG across both conditions, which is often not available. Another notable characteristic of the investigation was their utilization of lumbar punctures to access CSF and assess AD-related biomarkers. Multiple techniques exist to assess AD-related biomarkers, with each having its own unique set of advantages and disadvantages [42]. Much of the existing research surrounding AD-related biomarkers has relied upon blood-based sampling to assess Aβ and tau concentrations [7, 9], due to being less invasive and cost-and-time effective [42]. However, CSF-derived assays provide more accurate representations of AD-related biomarker concentrations [42]. As such, it is recognized as a more advanced assessment approach that should be prioritized when possible [42]. Importantly, this technique is not immune to error as many factors (e.g. storage temperature) can influence quality of the CSF sample [20, 43]. Going forward, it is critical that a standardized protocol be utilized for CSF sampling, especially in the clinical domain, in order to improve measurement accuracy [43]. Implementing a standardized approach for CSF extraction, storage, and analysis will also have great benefit for research as it will provide the ability to better compare results across investigations. In summary, this investigation was the first to directly assess the effects of repetitive nights of PSD on CSF biomarkers associated with AD, within a laboratory setting. Primarily, this investigation did not identify significant changes in CSF biomarkers associated with AD, including Aβ42 and tau, from PSD. However, the absence of these changes in the context of observed PSG macrostructure variables provides further support for the unique import of SWS in the pathogenesis of AD. The growing body of evidence surrounding this relationship suggests viability of SWS as a clinical tool for both disease risk assessment and intervention. However, future research is necessary to improve the field’s understanding of the cortical regions associated with the SWS findings. 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Fjell, Anders M; Sørensen, Øystein; Amlien, Inge K; Bartrés-Faz, David; Bros, Didac Maciá; Buchmann, Nikolaus; Demuth, Ilja; Drevon, Christian A; Düzel, Sandra; Ebmeier, Klaus P; Idland, Ane-Victoria; Kietzmann, Tim C; Kievit, Rogier; Kühn, Simone; Lindenberger, Ulman; Mowinckel, Athanasia M; Nyberg, Lars; Price, Darren; Sexton, Claire E;

Cubillos-Zapata, Carolina; Almendros, Isaac; Díaz-García, Elena; Toledano, Victor; Casitas, Raquel; Galera, Raúl; López-Collazo, Eduardo; Farre, Ramón; Gozal, David; García-Rio, Francisco

doi: 10.1093/sleep/zsz285pmid:

McKenzie, Marna B; Jones, Michelle-Lee; O’Carroll, Aoife; Serletis, Demitre; Shafer, Leigh Anne; Ng, Marcus C

doi: 10.1093/sleep/zsz281pmid: 31768558

Study ObjectivesRapid eye movement sleep (REM) usually suppresses interictal epileptiform discharges (IED) and seizures. However, breakthrough IEDs in REM sometimes continue. We aimed to determine if the amount of IED and seizures in REM, or REM duration, is associated with clinical trajectories.MethodsContinuous electroencephalogram (EEG) recordings from the epilepsy monitoring unit (EMU) were clipped to at least 3 h of concatenated salient findings per day including all identified REM. Concatenated EEG files were analyzed for nightly REM duration and the “REM spike burden” (RSB), defined as the proportion of REM occupied by IED or seizures. Patient charts were reviewed for clinical data, including patient-reported peak seizure frequency. Logistic and linear regressions were performed, as appropriate, to explore associations between two explanatory measures (duration of REM and RSB) and six indicators of seizure activity (clinical trajectory outcomes).ResultsThe median duration of REM sleep was 43.3 (IQR 20.9–73.2) min per patient per night. 59/63 (93.7%) patients achieved REM during EMU admission. 39/59 (66.1%) patients had breakthrough IEDs or seizures in REM with the median RSB at 0.7% (IQR 0%–8.4%). Every 1% increase in RSB was associated with 1.69 (95% CI = 0.47–2.92) more seizures per month during the peak seizure period of one’s epilepsy (p = 0.007).ConclusionsIncreased epileptiform activity during REM is associated with increased peak seizure frequency, suggesting an overall poorer epilepsy trajectory. Our findings suggest that RSB in the EMU is a useful biomarker to help guide about what to expect over the course of one’s epilepsy.

Exarchos, Ioannis; Rogers, Anna A; Aiani, Lauren M; Gross, Robert E; Clifford, Gari D; Pedersen, Nigel P; Willie, Jon T

doi: 10.1093/sleep/zsz272pmid: 31693157

Despite commercial availability of software to facilitate sleep–wake scoring of electroencephalography (EEG) and electromyography (EMG) in animals, automated scoring of rodent models of abnormal sleep, such as narcolepsy with cataplexy, has remained elusive. We optimize two machine-learning approaches, supervised and unsupervised, for automated scoring of behavioral states in orexin/ataxin-3 transgenic mice, a validated model of narcolepsy type 1, and additionally test them on wild-type mice. The supervised learning approach uses previously labeled data to facilitate training of a classifier for sleep states, whereas the unsupervised approach aims to discover latent structure and similarities in unlabeled data from which sleep stages are inferred. For the supervised approach, we employ a deep convolutional neural network architecture that is trained on expert-labeled segments of wake, non-REM sleep, and REM sleep in EEG/EMG time series data. The resulting trained classifier is then used to infer on the labels of previously unseen data. For the unsupervised approach, we leverage data dimensionality reduction and clustering techniques. Both approaches successfully score EEG/EMG data, achieving mean accuracies of 95% and 91%, respectively, in narcoleptic mice, and accuracies of 93% and 89%, respectively, in wild-type mice. Notably, the supervised approach generalized well on previously unseen data from the same animals on which it was trained but exhibited lower performance on animals not present in the training data due to inter-subject variability. Cataplexy is scored with a sensitivity of 85% and 57% using the supervised and unsupervised approaches, respectively, when compared to manual scoring, and the specificity exceeds 99% in both cases.

Swift, Kevin M; Keus, Karina; Echeverria, Christy Gonzalez; Cabrera, Yesenia; Jimenez, Janelly; Holloway, Jasmine; Clawson, Brittany C; Poe, Gina R

doi: 10.1093/sleep/zsz289pmid: 31784755

Sleep impacts diverse physiological and neural processes and is itself affected by the menstrual cycle; however, few studies have examined the effects of the estrous cycle on sleep in rodents. Studies of disease mechanisms in females therefore lack critical information regarding estrous cycle influences on relevant sleep characteristics. We recorded electroencephalographic (EEG) activity from multiple brain regions to assess sleep states as well as sleep traits such as spectral power and interregional spectral coherence in freely cycling females across the estrous cycle and compared with males. Our findings show that the high hormone phase of proestrus decreases the amount of nonrapid eye movement (NREM) sleep and rapid eye movement (REM) sleep and increases the amount of time spent awake compared with other estrous phases and to males. This spontaneous sleep deprivation of proestrus was followed by a sleep rebound in estrus which increased NREM and REM sleep. In proestrus, spectral power increased in the delta (0.5–4 Hz) and the gamma (30–60 Hz) ranges during NREM sleep, and increased in the theta range (5–9 Hz) during REM sleep during both proestrus and estrus. Slow-wave activity (SWA) and cortical sleep spindle density also increased in NREM sleep during proestrus. Finally, interregional NREM and REM spectral coherence increased during proestrus. This work demonstrates that the estrous cycle affects more facets of sleep than previously thought and reveals both sex differences in features of the sleep–wake cycle related to estrous phase that likely impact the myriad physiological processes influenced by sleep.

Redhead, Karen; Walsh, Jennifer; Galbally, Megan; Newnham, John P; Watson, Stuart J; Eastwood, Peter

doi: 10.1093/sleep/zsz270pmid: 31782959

Study ObjectivesIn pregnancy, the prevalence of both obstructive sleep apnea (OSA) and depression increases. Research reveals an association in the general population with up to 45% of patients diagnosed with OSA having depressive symptoms. Therefore, this study aimed to investigate the relationship between OSA and depression in pregnant women.MethodsOne hundred and eighty-nine women ≥26 weeks pregnant were recruited from a tertiary perinatal hospital. This cross-sectional study measured OSA (Apnea Hypopnea Index, AHI, using an ApneaLink device) and symptoms of depression (Edinburgh Postnatal Depression Scale, EPDS). Data were collected from medical records including participant age, ethnicity, parity, BMI, smoking status, history of depression, and use of antidepressants.ResultsOf the consenting women, data from 124 were suitable for analysis. Twenty women (16.1%) had OSA (AHI ≥ 5 events/h) and 11 (8.8%) had depressive symptoms (EPDS > 12). Women with OSA were more likely to have depressive symptoms after adjusting for covariates, odds ratio = 8.36, 95% CI [1.57, 44.46]. OSA was also related to higher EPDS scores and these were greater in women with a history of depression.ConclusionsDuring late pregnancy women with OSA had eight times the odds of having depressive symptoms. Furthermore, an interaction was found between OSA and history of depression. Specifically, in women with no history of depression, OSA increases depressive symptoms. In women with a history of depression, OSA has an even stronger effect on depressive symptomology. This suggests screening for OSA in pregnancy may identify women prone to future depressive episodes and allow for targeted interventions.

Herberger, Sebastian; Kräuchi, Kurt; Glos, Martin; Lederer, Katharina; Assmus, Lisa; Hein, Julia; Penzel, Thomas; Fietze, Ingo

doi: 10.1093/sleep/zsz271pmid: 31679018

Study ObjectivesThis study deals with the question whether a slow (non-disturbing) reduction of core body temperature (CBT) during sleep increases sleep stage N3 and EEG slow wave energy (SWE) and leads to a slowing of heart rate in humans.ParticipantsThirty-two healthy male subjects with a mean ± SD age 46 ± 4 years and body mass index 25.2 ± 1.8 kg/m2.MethodsA high-heat capacity mattress (HM) was used to lower body temperatures in sleep and was compared to a conventional low-heat capacity mattress (LM) in a double-blinded fashion. Polysomnography was performed accompanied by measurements of skin-, core body- and mattress surface-temperatures, and heart rate. EEG power spectral analyses were carried out using Fast Fourier Transform. Interbeat intervals were derived from the electrocardiogram.ResultsThe HM led to a larger decline in CBT, mediated through higher heat conduction from the core via the proximal back skin onto the mattress together with reduced heart rate. These effects occurred together with a significant increase in sleep stage N3 and standardized slow wave energy (sSWE, 0.791–4.297 Hz) accumulated in NREM sleep. In the 2nd half of the night sSWE increase was significantly correlated with body temperature changes, for example with CBT decline in the same phase.ConclusionsA HM subtly decreases CBT, leading to an increased amount of sleep stage N3 and of sSWE, as well as a slowing of heart rate.