1- Background

2- Materials and methods

3- Results

4- Discussion

5- Conclusion

References

Abstract

Background
Adaptive servoventilation (ASV) effectively treats nocturnal respiratory events in patients with heart failure and reduced ejection fraction (HFrEF) and central sleep apnoea (CSA), but increased mortality has been reported. This study investigated changes in sleep architecture during ASV treatment in HFrEF patients.
Methods
A retrospective analysis of polysomnographic datasets for 30 ASV-treated patients with stable HFrEF and moderate-to-severe CSA was performed, including blinded analyses of total sleep time (TST), and percentage of REM and non-REM sleep (stages N1-N3).
Results
Follow-up was 109 ± 32 days; mean device usage was 6.0 ± 1.1 h/day. During ASV there was reduction of N1 (34 ± 20%/TST to 13 ± 5%/TST, p < 0.001) and N3 sleep (4 ± 6%/TST to 1 ± 4%/TST, p = 0.020), and increase of N2 (44 ± 14%/TST to 62 ± 7%/TST, p < 0.001) and REM-sleep (18 ± 8%/TST to 24 ± 6%/TST, p = 0.002).
Conclusions
Disturbances of sympatho-vagal balance during ASV might help explain increased mortality during ASV. Since sympathetic tone is highest in REM-sleep and vagal predominance occurs during N3 sleep, these findings generate new hypotheses for the increased mortality seen in SERVE-HF.

Background

Disturbed sleep architecture and reduced sleep duration is associated with cardiovascular mortality, reduced quality of life and impaired cognitive function (da Silva et al., 2016; Yang et al., 2015). Sleep-disordered breathing (SDB) is one of the most important factors disrupting sleep architecture, is highly prevalent in heart failure (HF) patients, and is the comorbidity most likely to impair sleep and quality of life due to sleep fragmentation caused by nocturnal disordered breathing events (Punjabi, 2008; Arzt et al., 2016). In principle, SDB is classified into two main types: obstructive sleep apnoea (OSA) and central sleep apnoea (CSA). With a prevalence of about 20%, OSA is the most frequent SDB subtype in the general population (Young et al., 2002). The main pathophysiological mechanism of OSA is futile respiratory efforts against collapsed upper airways. As cardiac function deteriorates, the prevalence of SDB increases markedly (Punjabi, 2008; Heinzer et al., 2015; Oldenburg et al., 2007). More than 50% of clinically stable HF patients with reduced left ventricular ejection fraction (HFrEF) present with moderate to severe SDB (Oldenburg et al., 2007). In particular, the proportion of central SDB increases as HF progresses, often manifesting as a periodic breathing pattern, classified as Hunter-Cheyne-Stokes respiration (CSR) (Oldenburg et al., 2007; Oldenburg, 2012). These breathing patterns can show long cyclic respiratory events (Oldenburg, 2012; Linz et al., 2011; Narkiewicz et al., 1999) resulting in phases of hypercapnia followed by arousals (Narkiewicz et al., 1999). The main mechanism is an increase in respiratory gain, leading to hyperventilation interrupted by repetitive sleep apnoeas (Türoff et al., 2017; Somers et al., 1989).

Background: Insomnia is common during pregnancy but the prevalence and risk factors of insomnia in Chinese women during pregnancy is not well studied. This study aimed to examine the prevalence of insomnia and its risk factors in Chinese women during pregnancy. Methods: In this cross sectional study, 436 Chinese pregnant women with Insomnia Severity Index (ISI) ≥ 8 were clinically assessed using the insomnia criteria based on the combination of DSM-IV (Diagnostic and Statistical Manual-4th Edition) and ICD-10 (International Classification of Dieases, 10th Edition). Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS), Pregnancy Pressure Scale (PPS), Perceived physical discomfort level and number, Epworth Sleepiness Scale (ESS), and a general socio-demographic questionnaire were administered. Results: The results showed that about 20% of the pregnant women met the strict diagnosis criteria of insomnia. Independent-samples t-test revealed that several risk factors were correlated with the group with insomnia (N = 84) compared to the group without insomnia (N = 352). Binary Logistic regression analysis found that more significant bed partner influence (OR = 1.92, 95% CI: 1.03–3.60), depressive symptoms (OR = 1.07, 95% CI: 1.00–1.14), daytime sleepiness (OR = 1.07, 95% CI: 1.01–1.14), subjective somatic discomfort (OR = 2.27, 95% CI: 1.11–4.65), kinds of somatic discomfort (OR = 1.14, 95% CI: 1.03–1.27) and later gestation (OR = 1.05, 95% CI: 1.01–1.09) were significantly associated with insomnia. Conclusion: In this cohort of Chinese pregnant women, about a fifth of women suffered from clinically significant insomnia. Measures to prevent the adverse effects of insomnia should be provided to pregnant women with depressive symptoms, Sleep disturbance of the bed partner, excessive daytime sleepiness and somatic discomfort, especially late in gestation.

Introduction

Insomnia is the most prevalent sleep disturbance experienced in women during pregnancy, affecting 52%–61% of pregnant women [1]. The prevalence of insomnia during pregnancy is high from the start and two thirds of pregnant women suffer from insomnia in the later period of pregnancy [2]. One previous study found that 97% of the women at the end of pregnancy reported middle-of-the-night awakenings, but only less than a third of the women considered sleep disruption as a problem [3]. It has been shown that insomnia is related to a number of potential maternal and infant health risks, such as adverse pregnancy outcomes (hypertension, gestational diabetes mellitus (GDM)) [4–7]. Disturbed sleep in early and late pregnancy may also augment the risk for cardio-metabolic risk factors, which is associated with maternal and infant morbidity [4]. In addition, insomnia is frequently related to a variety of psychiatric disorders, such as depression and anxiety during pregnancy [8,9] and during the postpartum period [9–11]. Insomnia symptoms are also related to BMI increase in post-pregnancy [12]. In addition, sleep loss during pregnancy is related to fetal intrauterine growth retardation, longer duration of delivery, and unplanned cesarean section [13,14]. Pregnant women who slept b6 h per night appear to have longer labor and 4.5 times higher likelihood of a cesarean section [13,15,16].

Introduction

Methods

Results

Discussion

Conclusions

References

Abstract

Background
Ments were examined in the acute period of stroke. 33 (50%) patients had at least one or more new or exacerbated sleep complaiore than half of stroke patients present with a sleep-related breathing disorder including both central and obstructive forms of sleep apnoea. A cerebral infarction in different brain areas can disrupt sleep regulating pathways and cause insomnia, hypersomnia, circadian rhythm disturbances and other sleep disorders. Therefore, there is a need of objective data about various sleep disorders arising after ischemic or haemorrhagic stroke in order to implement practical recommendations how to suspect, diagnose and treat these conditions. Our medical hypothesis is that non-breathing sleep disorders are common among patients with acute ischemic or haemorrhagic stroke.
Objective
To investigate the subjective and objective sleep parameters in the patients with an acute ischemic or haemorrhagic stroke.
Methods
In the acute period (from 3 to 10 days after the first symptoms) of stroke all the patients completed questionnaires about sleep complaints and symptoms experienced before and after stroke, Epworth Sleepiness Scale (ESS), National Institute of Health Stroke Scale, Hospital Anxiety and Depression Scale and Modified Rankin Scale. Patients were included for further polysomnography (PSG) and sleep electroencephalography according to these criteria: (1) patients expressing severe sleep related complaints and/or symptoms that are new or have exacerbated after the stroke; and/or (2) patients having the ESS score equal or >10.
Results
66 patints and/or symptoms, mostly related to obstructive sleep apnoea (OSA) and insomnia. Finally, 13 (19.7% of the whole sample) patients were selected for performing PSG. 12 of 13 patients were diagnosed with sleep disorder: 1 patient got the diagnosis of mild OSA, 1 – central sleep apnoea (CSA), 2 – combination of OSA and CSA, 1 – combination of mild OSA, periodic limb movement disorder (PLMD) and REM sleep behaviour disorder (RBD), 1 – combination of mild OSA and PLMD, 3 – combination of PLMD and insomnia, 3 – insomnia. There were no significant relations between type, location or treatment of stroke and various PSG measures, as well as type of a diagnosed sleep disorder.
Conclusions
Half of our acute stroke patients had at least one or more new or exacerbated sleep complaints and/or symptoms, mainly related to OSA or insomnia. In the selected PSG group almost all patients were diagnosed with a sleep disorder, half of them having non-breathing sleep disorder, such as PLMD, RBD and insomnia.

Introduction

Sleep is recognized to be considerable for health and well-being in all persons, but increasingly so in those recovering from brain damage [1]. It is known that sleep disorders are a frequent complication in patients with acute ischemic or haemorrhagic stroke, approximately 20–40% of stroke patients experience various sleep-wake disorders, such as insomnia, hypersomnia, REM sleep behavior disorder (RBD), periodic limb movement disorder (PLMD). Even more, about 50–70% stroke patients present with a sleep-related breathing disorder including both central and obstructive forms of sleep apnoea [2,3]. Despite that, the majority of physicians do not screen stroke patients for sleep-disordered breathing [4]. Patients with stroke and OSA have a higher risk for repeated stroke and significantly increased mortality in one year after the stroke in comparison with the patients without apnoea [5,6]. Studies showed a significant reduction in stroke recurrence and mortality as well as improved neurological recovery among the patients treated with continuous positive airway pressure therapy (CPAP), consequently, CPAP treatment may reduce the risk of stroke in patients with OSA [7]. A cerebral infarction in subcortex, thalamus or mesencephalon can fully disrupt sleep and wakefulness cycle, leading to insomnia and agitation during the night, and severe sleepiness during the day [8]. Insufficient sleep after stroke can be followed by chronic fatigue, cognitive and emotional dysfunction, worse neurological outcome and life quality. Therefore, there is a need of objective data about various sleep disorders arising after ischemic or haemorrhagic stroke in order to implement practical recommendations how to suspect, diagnose and treat these conditions. Our medical hypothesis is that non-breathing sleep disorders are common among patients with acute ischemic or haemorrhagic stroke. The aim of the study presented in this article was to investigate the subjective and objective sleep parameters in the patients with an acute ischemic or haemorrhagic stroke.

Abstract

Sleep alterations associated with adulthood ADHD are poorly understood. Here, we conducted the first meta-analysis of sleep studies in adults with ADHD. Based on a pre-registered protocol (PROSPERO-CRD42017065407), we searched Pubmed, Ovid and Web of Knowledge databases through August 3rd, 2017, with no language or publication type restrictions, and contacted study authors for unpublished data/information. From a pool of 8812 references, we retained 13 studies. Random-effects models were performed and study quality was rated using the Newcastle-Ottawa Scale. Compared to adults without ADHD, those with ADHD significantly differed in seven out of nine subjective parameters (Standardized Mean Difference, SMD, ranging from 0.56 to 1.55) and two out of five actigraphic parameters [SMD (95% CI): sleep onset latency: 0.80 (0.46–1.14); sleep efficiency: −0.68 (−1.03, −0.34)]. No significant differences were detected for polysomnographic parameters. We conclude that, whereas subjectively reported sleep problems are significantly associated with ADHD in adults and should be systematically screened during the clinical interview, additional research is needed to understand if they are underpinned by objective sleep alterations.

Introduction

With a worldwide estimated prevalence of about 5% (Polanczyk et al., 2014), Attention-Deficit/Hyperactivity Disorder (ADHD) is the most commonly diagnosed neurodevelopmental disorder in childhood. Impairing symptoms of ADHD persist into adulthood in about 65% of cases (Faraone et al., 2006), with a pooled prevalence of adulthood ADHD around 2.5% (Simon et al., 2009).

Insomnia is the most common sleep disorder, having become one of the foremost health concerns for workers internationally. Over the previous 30 years, a large number of epidemiological studies have investigated and reported on the correlation between insomnia or sleep disturbance and stressors in the workplace. Knudsen [16] investigated 1715 full-time employees, identifying a close relationship between work overload and insomnia, while role conflict, as well as job latitude, was correlated with difficulty in maintaining sleep. A further investigation [25] involving 1786 workers from 8 electrical appliance factories, applying both DCS and ERI on the same population, illustrated the significant association between job stress and insomnia symptoms. Comparable conclusions have also been drawn in other research [13, 14, 26, 30, 31]. Nevertheless, the distinctions between these findings were readily apparent, while in certain studies, the estimated confidence intervals of effects were too wide to draw meaningful conclusions. Moreover, an array of instruments was adopted for measuring exposure and results among the literature, with the majority of studies being of a cross-sectional nature, where a sole study is believed to lack strength in terms of validity and dependability. In this research, we undertook a comprehensive search and analysis of the literature concerned with the correlation between insomnia symptoms measured by scales and job stress variables, as a means of producing evidence with greater reliability. Additionally, we used meta-analyses to summarize the varied literature from Europe and Asia that was both prospective and cross-sectional by design, accounting for the deficiencies in the breadth of evidence within previous research.

Obstructive sleep apnea (OSA) is a common disorder associated with signifcant cardiovascular morbidities [1, 2]. OSA has been identifed as a risk factor for the induction and progression of signifcant cardiac rhythm disturbance including atrial fbrillation [3].Te diagnosis and treatment of OSA may decrease the recurrence and/or severity of cardiac arrhythmia [3]. Te gold-standard method for the diagnosis of OSA is an overnight in-laboratory polysomnography (PSG) [4]. Te severity of OSA is classifed according to the apneahypopnea-index (AHI), which is the number of obstructive events per hour of sleep. OSA is considered mild with an AHI between 5 and 15/hour; moderate with an AHI of 15 and 30/hour; and severe with an AHI ≥ 30/hour [5]. Other measures of the disease activity include the degree of hypoxemia and the arousal index. For some, the presence of the clinical sequelae of apnea is considered when evaluating the clinical severity of OSA. Tese may include excessive daytime sleepiness, hypertension, and depression. Te overnight in-laboratory PSG involves the concurrent recording of physiologic signals during sleep, including the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG). For identifcation of obstructive events, an oronasal airfow sensor is used to monitor the airfow. For monitoring of respiratory efort, dual thoracoabdominal belts are used. Typically, oxygen saturation is measured using a pulse oximeter attached to the patient’s fnger. In addition, a nasal pressure transducer is used for snoring detection. Te in-laboratory PSG has several challenges including signifcant fnancial, human, and healthcare costs [6]. Polysomnographic studies are typically conducted in a sleep laboratory which is an unfamiliar environment for patients and is associated with the discomfort of being connected to a recording device with several wires. Tese environmental changes create a phenomenon known as the “frst night efect” [7], which implicates changes in sleep quality and efciency resulting in inaccurate and unrepresentative results. Moreover, there are diferences in the recording techniques among diferent sleep laboratories, and until recently a lack of awareness of the implication of rapid eye movement (REM) and nonrapid eye movement (NREM) related apneas [8]. In addition, the inconsistency of the results related to diferent criteria, such as the threshold of oxygen desaturation, exists between diferent laboratories. Some variability in sleep data may result due to night-to-night variability or the “frst night efect” [7]. Further, in-laboratory PSG requires monitoring by an attending sleep technician. Another challenge is the scarcity and distribution of sleep laboratories compared to the high number of patients who may need a sleep assessment such as arrhythmia patients. Finally, a sleep study may need to be repeated if inadequate sleep was recorded as in such case inadequate data may not be reliable for obtaining a diagnosis. Te previous reasons suggest that in-laboratory PSG increases the healthcare burden and could be a contributing factor to the underrecognition of OSA.

Sleep quality has been identified as an essential characteristic that travelers look for when perusing online hotel reviews to find the perfect hotel (Beyer and Melendez, 2015). To that end, several high-end hotels have started working with sleep consultants and hotel designers to create a better environment to enhance travelers’ sleep experience (Beyer and Melendez, 2015; Prunty, 2015). For instance, JW Marriott launched the “Nightly Refresh Program” in 2013 to feature sensory relaxation, such as a mixture of aromatic and detoxifying essential oils in bathroom amenities, a late-night snack, and herbal tea that help travelers recover from stress (The Luxury Travel Magazine, 2013). Similarly, Westin Hotels and Resorts’ (n.d.) “Well-Being Movement” has focused on its “Sleep Well program” by offering high quality mattresses and bedding. A restful night’s sleep not only gives travelers’ repose to recover their physical and psychological condition, but also promotes a healthy lifestyle for travelers. Hotels can build their reputation and obtain customers’ loyalty by enhancing customers’ experience and satisfaction of sleep quality (Hotelzone, 2015). Therefore, hotels are seeking solutions that may help hotel guests rejuvenate while traveling. Business travelers are one of the most important market segments in the hospitality industry (McCleary et al., 1994; Callan and Kyndt, 2001). Global business travel expenditure was $1.2 trillion in 2015 and is expected to show continuous 5 percent growth until 2021 (BusinessWire, 2017; Deloitte, 2016). Business travelers stay in hotels to rest, refresh, and prepare themselves for the next day of business or the next destination. Because a business trip can often be an exhausting journey, it is important for business travelers to find comfortable accommodation and welcoming hospitality services while away from home. However, a hotel room is not always a comfort zone for travelers. Hotel guests may experience uncomfortable beds, poor quality pillows, a loud air conditioner, and noise from the hallway (Valtonen and Veijola, 2011). These issues may affect the hotel guests’ sleep quality, which, in turn, may influence their overall experience in the hotel and ultimately their work productivity. Providing an environment for travelers to rest peacefully is an essential function of hotels. However, limited research scrutinizes business travelers’ experience in terms of sleep quality while staying in hotels (Pallesen et al., 2016). Given the importance of the business travel market, it is important to understand business travelers’ sleep quality at hotels and how hotel attributes influence their experience. The purpose of this study was to measure business travelers’ sleep quality and overall satisfaction in hotels. Although previous studies of business travel focused on travelers’ preferences regarding hotel attributes, the questions of how hotel attributes and hotel star levels would impact travelers’ sleep quality had not been investigated. Thus, this study addresses the following questions: what hotel attributes are related to business travelers’ sleep quality in hotels; which business travelers’ demographic characteristics, such as the frequency of business travel, personal sleep assessment, and age, influence their sleep quality in hotels; and is there a significant difference in factors that influence business travelers’ overall sleep satisfaction in upscale hotels (4 + stars) vs mid-scale hotels (2.5-3.5 stars)?

Expectations have a profound influence on human perception. In the waking state, placebo analgesia is an example of cognitive modulation driven by expectations of a positive treatment outcome (e.g., [1;8;16;42;52;64]). Although classical conditioning appears sufficient to generate placebo responses involving unconscious physiological functions, the modulation of pain perception by a placebo generally involves a conscious anticipation of relief ([9;15]). Sleep is a state of reduced awareness in which higher-order processing of external stimuli is generally thought to be largely suppressed. Given that conscious processing of placebo cues and associated outcomes may be necessary for placebo responses to occur, placebo effects would be expected to be suppressed during sleep. However, once learning has occurred, expectancy may affect later outcomes through unconscious processes ([28;31;43]). Learned placebo analgesia responses might then be expressed without explicit processing of pain-related cues or conscious evocation of relief expectancy. The brain may process external stimuli during both rapid-eye-movement sleep (REM) and slow-wave sleep (SWS), and the reduced engagement of frontal cortices in response to such inputs is generally interpreted as a sleep-protecting function that prevents waking ([14;18;56;63]). Brain activity during REM sleep has also been associated with cognitive processes ([19;25;38;49]), including offline reprocessing of recent memories ([36;59]). We previously suggested that REM sleep may be involved in the reprocessing of relief expectations generated prior to sleep ([33;34]). Placebo mechanisms may involve sleep-related processing that results from unconscious reactivation of learned expectations that may alter responses to noxious stimuli. In the present study, healthy participants underwent placebo conditioning on the evening before sleep and were subjected to noxious thermal stimuli while  sleeping the following night. We tested the hypothesis that placebo conditioning and relief expectations induced prior to sleep would reduce nocturnal pain, anxiety, and sleep disturbance reported the following morning. Polysomnography was used to assess brain arousals in response to nociceptive stimuli across sleep stages. We expected a reduction in stimulus-induced arousals during sleep, and especially during REM sleep, in response to a possible reactivation of relief expectations.

Sleep, in contrast to wakefulness, is characterized by reduced awareness and responsiveness. A basic model of sleep homeostasis is based on the concept of sleep-wake transition [1]. Conventionally, sleep stages in humans are classified as wake, rapid eye movement (REM) sleep, and an approximate continuum of depth during non-REM (NREM) sleep based on electroencephalographic patterns, which comprises about 80% of the entire sleep [2]. This cycling model of wake/NREM/REM sleep switches has been the primary focus of sleep research for decades. However, this reductionist type of approach is over-simplified and has limitation in understanding pathophysiological mechanisms in sleep disorders. Sleep is not simply a succession of human invented stages, but a delicate and sophisticated nonlinear symphony played by the brain in a democratic and mutual interaction with the rest of the body [2]. Real sleep stages are dynamic transitions between multiple physiological states swinging between the dual condition of stability and instability to warrant environmental adaptations and achieve physical and mental restoration [3]. Quantification of sleep stages via the analysis of electroencephalography (EEG) signal has been a challenge for years. Conventional visual sleep stage scoring is arbitrary and does not fully capture intrinsic EEG activity [4]. Fourier-based spectral analysis can quantify frequency compositions in EEG signals and is the most commonly used EEG analysis; however, it has intrinsic limitations to capturing underlying dynamics of the brain oscillations. First, fast Fourier transform (FFT)-based analysis assumes that complex oscillations embedded in the EEG signal are comprised of sine waves with different frequencies [5]. In this context, EEG signal can be decomposed into frequency components such as beta, alpha, theta, or delta frequency bands. However, it has long been known that brain oscillation is not a linear combination of these arbitrary frequency components, a property called “nonlinearity” [6]. Second, FFT-based spectral analysis assumes that none of these frequency components change in amplitude or shape as time evolves, which is clearly against what has been observed in complex brain oscillations, a property called “nonstationarity” [5]. It has long been observed that physiologic output of human body is nonstationary and nonlinear. Controls of physiological systems and outputs such as heartbeat, respiration, and brain wave oscillations are extraordinary complex [7]. Such complexity is believed to arise from nonlinear interactions among multiple control nodes in different physiological body systems that operate at multiple time scales. It has been hypothesized that the complexity of a biological system should be related to the system’s capacity to adapt and function in an ever changing environment [7]. Conventionally, scientists employ a reductionist approach to disassemble the complex system into constituent pieces, examine each component, and, finally, reassemble them to recreate the original entity. However, this approach is often unrealistic. In most circumstances, we can observe only the macroscopic output of physiological functions, such as an EEG, heart rate, or respiration. In the language of complex systems, the composite behavior cannot be fully understood by “adding up” the components. Instead, one needs new approaches to measuring a system’s integrative behavior. Thus, the understanding of the complex dynamics of the physiologic output, such as changes in EEG dynamics observed across different sleep stages, will be improved by applying nonlinear dynamical approaches to the analysis of EEG signal.

Following traumatic experiences, sleep disturbances are frequently reported. The most common symptoms are insomnia and trauma related nightmares (TRN) [1]. The nightmares that trauma survivors develop are noted to be distinctly different than the nightmares which characterize idiopathic nightmare disorder [2,3]. Trauma related nightmares tend to be more severe and distressing to the individual, and in many (but not all) cases, associated with posttraumatic stress disorder (PTSD). Disruptive nocturnal behaviors (DNB) to include dream enactment behavior (DEB) and sympathetic activation are often reported in the context of TRN. Nocturnal movements with TRN can range from gross body movements to frank combative behaviors. These symptoms, which further distinguish TRN from idiopathic nightmares, have remained relatively poorly characterized. This is in part due to the frequent association of TRN with PTSD as a variant of idiopathic nightmares, as well as the rare capture of nightmares and DNB in monitored settings [4]. Prior to the proposal of trauma associated sleep disorder (TSD) as a distinct parasomnia [5], these nocturnal phenomena was best described by Raskind: “The physiology of PTSD trauma nightmares differs from that of ‘normal’ dreams. Trauma nightmares are largely expressed during light sleep and disrupted rapid eye movement (REM) sleep, and often are accompanied by motor activity. Normal dreams, whether pleasant or unpleasant, most often arise from REM sleep that is characterized by a relative paralysis of large muscle movement” [6]. This description highlights the characteristics which distinguish TSD from nightmare disorder, most notably the presence of motor activity, but also that the nightmares potentially emanate from both non-rapid eye movement (NREM) and REM sleep. Additionally, this constellation of findings occurs exclusively while asleep, thus differentiating TSD from the diagnosis of PTSD. In this article, we report on the historical findings consistent with TSD and develop the clinical and potential neurobiological mechanisms of TSD. As there is a paucity of articles on nightmares and DNB emanating solely from traumatic experiences in the absence of PTSD, we review the scientific literature which has evaluated patients with PTSD and in some cases, REM sleep behavior disorder (RBD). In a portion of the literature evaluated, the patient’s symptoms and overall clinical picture is, in our opinion, more consistent with the proposed criteria for TSD.

As the world’s population ages, age-related disorders such as Parkinson disease (PD) threaten to become a significant financial and social burden (1), on top of the personal burden carried by the affected individuals and their families. PD is the most common neurodegenerative movement disorder, affecting 1-2% of the population older than 60 years (2-4), and the projected number of PD patients in 2030 in 15 of the most populous nations is estimated to be 8.67 million patients (5). PD is pathologically characterized by degeneration of the substantia nigra (SN) and the presence of Lewy bodies, which are aggregates of various proteins, primarily α-synuclein (6). As such, it is part of a family of disorders collectively termed synucleinopathies, mainly including PD, dementia with Lewy bodies (DLB), and multiple system atrophy (MSA) (7). Other diseases may also have α-synuclein depositions, and while this is a shared feature, it does not imply that α-synuclein is the sole cause for these disorders.

The pathogenic process leading to PD begins many years before diagnosis, which is traditionally based on classical motor symptoms; therefore, the course of PD can be divided accordingly, to preclinical PD, prodromal PD (non-motor PD) and clinical PD, when motor symptoms appear (8). This recent division aims to distinguish between the different phases of PD, first, when neurodegeneration started but no symptoms are present (preclinical PD), then when early symptoms appear but PD is not diagnosed yet (prodromal PD), and lastly when motor symptoms present (clinical PD) (8). Clinical PD occurs when about half of the dopaminergic neurons in the SN had already been irreversibly degenerated and died, and the typical motor symptoms appear (9). One of the main challenges in PD research is identifying individuals during the prodromal phase of PD; when neuroprotective therapy is available, early diagnosis of PD will be crucial to slow or stop the degenerative process prior to motor parkinsonism.