Excessive Daytime Sleepiness and REM Sleep Behavior Disorders in Parkinson's Disease: A Narrative Review on Early Intervention With Implications to Neuroprotection

AbstractBackgroundCognitive impairments are common in Parkinson’s disease (PD). Despite its clinical importance, the development of dementia is still difficult to predict. In this study, we investigated the possible associations between non-motor symptoms and the risk of developing dementia within a 2-year observation period in PD.MethodsA total of 80 patients with PD participated in this study. Nonmotor symptoms (the Nonmotor Symptoms Questionnaire), PD status (Unified Parkinson’s Disease Rating Scale), depression (Geriatric d Depression Scale or Montgomery-Asberg Depression Scale), stereopsis and severity of nonmotor symptoms (Non-motor symptoms scale) were assessed. Global cognitive function (Mini-Mental State Examination) were evaluated at baseline and 2 years later.ResultsPresence of depression, vivid dreaming, REM sleep behavior disorders, hyposmia, abnormal stereopsis, non-smoking and postural instability/ gait disturbance phenotype were associated with a significantly more rapid decline of Mini-Mental State Examination. Logistic regression analyses demonstrated that depression (odds ratio=13.895), abnormal stereopsis (odds ratio=10.729), vivid dreaming (odds ratio=4.16), REM sleep behavior disorders (odds ratio=5.353) and hyposmia (odds ratio=4.911) were significant independent predictors of dementia risk within 2 years. Postural instability/ gait disturbance phenotype and age >62 years were also independent predictors of dementia risk (odd ratio=38.333, odds ratio=10.625).ConclusionWe suggest that depression, vivid dreaming, REM sleep behavior disorders, hyposmia and abnormal stereopsis are closely associated with cognitive decline, and that presence of these nonmotor symptoms predict the subsequent development of Parkinson’s disease dementia.

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White matter changes in drug-naïve Parkinson's disease patients with impulse control & probable REM sleep behavior disorders

Journal of the Neurological Sciences ◽

10.1016/j.jns.2021.120032 ◽

2021 ◽

pp. 120032

Author(s):

Mahsa Dolatshahi ◽

Amir Ashraf-Ganjouei ◽

I-Wei Wu ◽

Yu Zhang ◽

Mohammad Hadi Aarabi ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

White Matter ◽

Rem Sleep ◽

Behavior Disorders ◽

Impulse Control ◽

White Matter Changes ◽

Sleep Behavior ◽

Drug Naïve

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Investigation of Nonmotor Symptoms in First-Degree Relatives of Patients with Different Clinical Types of Parkinson’s Disease

Parkinson s Disease ◽

10.1155/2019/1654161 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Jiang-bing Liu ◽

Jun-ling Leng ◽

Ying-ge Wang ◽

Yu Zhang ◽

Tie-yu Tang ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Excessive Daytime Sleepiness ◽

Daytime Sleepiness ◽

Late Onset ◽

Severe Depression ◽

Nonmotor Symptoms ◽

Sleep Behavior ◽

First Degree Relatives

Background. Nonmotor symptoms (NMS) are prodromal characteristics of Parkinson’s disease (PD). The first-degree relatives (FDR) of PD patients had a higher risk of PD and also had more NMS. Objective. To delineate NMS in FDR of patients with different clinical types of PD. Methods. A total of 98 PD probands were recruited; 256 siblings of them were enrolled in the FDR group. Various scales were used to assess NMS, including depression, anxiety, cognitive impairment, insomnia, constipation, excessive daytime sleepiness, rapid eye movement sleep behavior disorder (RBD), and restless legs syndrome (RLS). The incidences of NMS were further compared between the FDR groups of PD with different types. Results. The FDR of early-onset PD (EOP) showed a higher incidence of moderate to severe depression (OR = 4.08; 95% CI: 1.12–14.92; P=0.033), anxiety (OR = 4.22; 95% CI: 1.87–9.52; P=0.001), and excessive daytime sleepiness (OR = 3.40; 95% CI: 1.00–11.48; P=0.049) than the FDR of late-onset PD (LOP). It was also found that RBD (OR = 11.65; 95% CI: 3.82–35.54; P<0.001), constipation (OR = 4.94; 95% CI: 1.85–13.21; P=0.001), sleep disorders (OR = 4.51; 95% CI: 1.73–11.78; P=0.002), cognitive impairment (OR = 3.55; 95% CI: 1.62–7.77; P=0.002), and anxiety (OR = 2.49; 95% CI: 1.32–4.71; P=0.005) were more frequent in FDR of tremor-dominant PD (TDP) than in FDR of non-tremor-dominant PD (NTDP). Conclusions. The siblings of patients with EOP and TDP have more NMS, presuming that they have a higher risk in the PD prodromal stage. Whether they have a greater possibility to progress into PD requires further investigation.

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Application of Machine Learning Technique to Distinguish Parkinson’s Disease Dementia and Alzheimer’s Dementia: Predictive Power of Parkinson’s Disease-Related Non-Motor Symptoms and Neuropsychological Profile

Journal of Personalized Medicine ◽

10.3390/jpm10020031 ◽

2020 ◽

Vol 10 (2) ◽

pp. 31 ◽

Cited By ~ 1

Author(s):

Haewon Byeon

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rem Sleep ◽

Behavior Disorders ◽

Predictive Power ◽

Screening Tests ◽

Motor Symptoms ◽

Sleep Behavior ◽

Non Motor Symptoms

In order to develop a predictive model that can distinguish Parkinson’s disease dementia (PDD) from other dementia types, such as Alzheimer’s dementia (AD), it is necessary to evaluate and identify the predictive accuracy of the cognitive profile while considering the non-motor symptoms, such as depression and rapid eye movement (REM) sleep behavior disorders. This study compared Parkinson’s disease (PD)’s non-motor symptoms and the diagnostic predictive power of cognitive profiles that distinguish AD and PD using machine learning. This study analyzed 118 patients with AD and 110 patients with PDD, and all subjects were 60 years or older. In order to develop the PDD prediction model, the dataset was divided into training data (70%) and test data (30%). The prediction accuracy of the model was calculated by the recognition rate. The results of this study show that Parkinson-related non-motor symptoms, such as REM sleep behavior disorders, and cognitive screening tests, such as Korean version of Montreal Cognitive Assessment, were highly accurate factors for predicting PDD. It is required to develop customized screening tests that can detect PDD in the early stage based on these results. Furthermore, it is believed that including biomarkers such as brain images or cerebrospinal fluid as input variables will be more useful for developing PDD prediction models in the future.

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Visual hallucinations as REM sleep behavior disorders in patients with Parkinson's disease

Movement Disorders ◽

10.1002/mds.10439 ◽

2003 ◽

Vol 18 (7) ◽

pp. 812-817 ◽

Cited By ~ 49

Author(s):

Takashi Nomura ◽

Yuichi Inoue ◽

Hidetaka Mitani ◽

Ryuzo Kawahara ◽

Masahiro Miyake ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Rem Sleep ◽

Behavior Disorders ◽

Visual Hallucinations ◽

Sleep Behavior

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Early Daytime Sleepiness and Nighttime Slow-wave Sleep Disorganization in Progressive Macaque Model of Parkinson’s Disease

10.21203/rs.3.rs-1216338/v1 ◽

2022 ◽

Author(s):

Aurélie Davin ◽

Stéphan Chabardès ◽

Hayat Belaid ◽

Daniel Fagret ◽

Loic Djaileb ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Excessive Daytime Sleepiness ◽

Daytime Sleepiness ◽

Early Stage ◽

Clinical Signs ◽

Human Condition ◽

Sleep Behavior ◽

Behavior Disturbances ◽

Nighttime Sleep

Abstract Parkinsonian patients often experience wake/sleep behavior disturbances, which can appear at an early stage of the disease in a way that is still not fully described. We aimed here at reproducing and characterizing these clinical signs in a progressive non-human primate model of the Parkinson’s disease to better understand the underlying physiopathology and to identify biomarkers of the disease. Three adult non-human primates (macaca fascicularis) were equipped with a polysomnographic telemetry system allowing the characterization of the wake/sleep behavior by long-term neurophysiological recordings and a modified multiple sleep latency test. Experiments were first performed in healthy animals and then during the progressive induction of a parkinsonian syndrome by chronic intramuscular injections of low doses of MPTP. We observed a significant early onset of wake/sleep behavior disturbances, before any motor symptoms, resulting in (i) a disorganization of nighttime sleep with more deep sleep and (ii) a disorganization of daytime naps with an excessive daytime sleepiness characterized by longer duration of naps, which occurred faster. These observations persisted and worsened in stable symptomatic state. In that latter state, we observed persistent excessive daytime sleepiness and more disorganized nighttime sleep architecture and continuity. Interpolating to the human condition, the present study suggests that nighttime and daytime sleep disorders may appear in early stage of the disease. They could thus be used as biomarkers of the disease for early stratification of patients who are at risk of developing Parkinson’s disease.

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