REM Sleep Behavior Disorder Screening Research Articles

Parkinson's disease subtypes and their association with probable rapid eye movement sleep behavior disorder severity: a brainstem tractography and machine learning investigation.

Rapid Eye Movement (REM) sleep behavior disorder (RBD) affects nearly half of Parkinson's disease (PD) patients. However, the structural heterogeneity within the brainstem, which regulates REM sleep, remains largely unexplored in PD. Our objective was to identify distinct PD subtypes based on microstructural characteristics in the brainstem and examine their associations with the severity of RBD. Data, including diffusion tensor imaging and REM sleep behavior disorder screening questionnaire (RBDSQ) responses, were obtained from 124 PD patients and 61 healthy controls through the Parkinson's Progression Marker Initiative database. Mean Quantitative Anisotropy (QA) values, representing axonal density, were extracted from 14 brainstem tracts and input into the semi-supervised machine learning algorithm, Heterogeneity through Discriminative Analysis (HYDRA), to cluster subtypes. Applying HYDRA, we identified two distinct PD subtypes (Subtype 1: n = 66, Subtype 2: n = 58). Subtype 2 exhibited reduced QA across assessed brainstem tracts and significantly higher RBDSQ scores than Subtype 1 and healthy controls (p < 0.001). Conversely, Subtype 1, characterized by lower RBDSQ scores, exhibited increased QA, notably in the right medial longitudinal fasciculus, when compared to Subtype 2 and controls (p < 0.001). These findings suggest that heterogeneous axonal damage in brainstem circuits correlates with variations in RBD severity, providing insights into the neurobiological underpinnings of early PD.

Clinical assessment and striatal dopaminergic activity in healthy controls and patients with Parkinson's disease: a Bayesian approach.

We aimed to evaluate correlations between striatal dopamine transporter (DAT) uptake and clinical assessments in both patients with Parkinson's disease (PD) and healthy controls. This study enrolled 193 healthy controls, and 581 patients with PD. They underwent various clinical assessments and 123I-FP-CIT SPECT scans. After reconstruction, attenuation correction, and normalization of SPECT images, counts were measured from the bilateral caudate and putamen, and the occipital cortex for reference. Count densities for each region were extracted and used to calculate striatal binding ratios (SBRs) for each striatal region. SBR is calculated as (target region/reference region)-1. After logarithmic transformation of striatal SBRs, we analyzed the effects of clinical assessments on striatal SBRs using Bayesian hierarchical modeling. MDS-UPDRS total score, part I, part II, part III, Epworth Sleepiness Scale, REM sleep behavior disorder screening questionnaire, SCOPA-AUT total score were negatively associated with striatal SBR in patients with PD. Also, HVLT recognition discrimination was positively associated with striatal SBR in both healthy controls and patients with PD. In healthy control, MDS-UPDRS part II, MOCA, SCOPA-AUT total score were positively associated with striatal SBR. We demonstrated that motor symptom, sleep disturbance, autonomic symptom, and cognition of patients with PD were associated with striatal dopaminergic activity. In healthy controls, motor symptoms, autonomic symptom, and cognition were associated with striatal dopaminergic activity, some of which showing the opposite direction with patients with PD. This result might provide new insight to underlying mechanism of dopamine system with motor and non-motor assessments.

Prediction of Treatment Response and Effect on Clinical Manifestations for Normal Pressure Hydrocephalus of Sleep Related Breathing Disorders

Objectives: Sleep disorders and normal pressure hydrocephalus (NPH) are increasingly important issues observed in neurological disorders. However, the correlation between these diseases has not been sufficiently studied. Thus, we discuss the correlation between sleep disorders and the clinical features of NPH. Methods: Overall, 40 patients with idiopathic NPH were included in the study. To evaluate the sleep disorders and psychiatric problems, surveys were administered, including the 36-Item Short Form Survey, Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISI), Berlin Questionnaire, STOP-Bang Questionnaire, Hospital Anxiety and Depression Scale (HADS), Morningness-Eveningness Questionnaire, and the REM Sleep Behavior Disorder Screening Questionnaire. To evaluate the severity of dementia and ataxia, all of the patients completed the Korean mini-mental state examination; Korea frontal assessment battery score, amd unified Parkinson’s disease rating scale-motor scores before and after cerebrospinal fluid (CSF) drainage. The Pearson’s chi-square test, independent Student’s t-test, Mann–Whitney U test, and linear regression analysis were used to analyze the relationship between sleep disorders and symptom improvement after CSF drainage. Results: Of the 40 patients, 21 patients had poor sleep quality (PSQI&gt;5), 8 patients had insomnia (ISI≥8), 11 patients had daytime sleepiness (ESS≥11), 9 (STOP-Bang≥3) to 13 (Berlin≥2 categories) patients had sleep apnea, 13 patients were anxious (HAS≥8), and 27 patients were depressed (HDS≥8). A linear regression analysis showed that sleep apnea was significantly correlated with cognitive function, and insomnia was correlated with cognitive, motor, and frontal lobe functions. Additionally, patients with severe sleep apnea demonstrated a greater recovery of cognitive function after CSF drainage. Conclusions: Obstructive sleep apnea (OSA) is closely related to the clinical symptoms and treatment effectiveness of NPH. The diagnosis and proper treatment of OSA is expected to improve the prognosis of patients with NPH.

Predictive Modeling Using a Composite Index of Sleep and Cognition in the Alzheimer's Continuum: A Decade-Long Historical Cohort Study.

Sleep disturbances frequently affect Alzheimer's disease (AD), with up to 65% patients reporting sleep-related issues that may manifest up to a decade before AD symptoms. To construct a nomogram that synthesizes sleep quality and cognitive performance for predicting cognitive impairment (CI) conversion outcomes. Using scores from three well-established sleep assessment tools, Pittsburg Sleep Quality Index, REM Sleep Behavior Disorder Screening Questionnaire, and Epworth Sleepiness Scale, we created the Sleep Composite Index (SCI), providing a comprehensive snapshot of an individual's sleep status. Initially, a CI conversion prediction model was formed via COX regression, fine-tuned by bidirectional elimination. Subsequently, an optimized prediction model through COX regression, depicted as a nomogram, offering predictions for CI development in 5, 8, and 12 years among cognitively unimpaired (CU) individuals. After excluding CI patients at baseline, our study included 816 participants with complete baseline and follow-up data. The CU group had a mean age of 66.1±6.7 years, with 36.37% males, while the CI group had an average age of 70.3±9.0 years, with 39.20% males. The final model incorporated glial fibrillary acidic protein, Verbal Fluency Test and SCI, and an AUC of 0.8773 (0.792-0.963). In conclusion, the sleep-cognition nomogram we developed could successfully predict the risk of converting to CI in elderly participants and could potentially guide the design of interventions for rehabilitation and/or cognitive enhancement to improve the living quality for healthy older adults, detect at-risk individuals, and even slow down the progression of AD.

MRI brain structural and functional networks changes in Parkinson disease with REM sleep behavior disorders.

Rapid eye movement sleep behavior disorder (RBD) is common in individuals with Parkinson's disease (PD). In spite of that, the precise mechanism underlying the pathophysiology of RBD among PD remains unclear. The aim of the present study was to analyze gray matter volumes (GMVs) as well as the changes of functional connectivity (FC) among PD patients with RBD (PD-RBD) by employing a combination of voxel-based morphometry (VBM) and FC methods. A total of 65 PD patients and 21 healthy control (HC) subjects were included in this study. VBM analyses were performed on all subjects. Subsequently, regions with significant different GMVs between PD patients with and without RBD (PD-nRBD) were selected for further analysis of FC. Correlations between altered GMVs and FC values with RBD scores were also investigated. Additionally, receiver operating characteristic (ROC) curves were employed for the evaluation of the predictive value of GMVs and FC in identifying RBD in PD. PD-RBD patients exhibited lower GMVs in the left middle temporal gyrus (MTG) and bilateral cuneus. Furthermore, we observed higher FC between the left MTG and the right postcentral gyrus (PoCG), as well as lower FC between the bilateral cuneus (CUN) and the right middle frontal gyrus (MFG) among PD-RBD patients in contrast with PD-nRBD patients. Moreover, the GMVs of MTG (extending to the right PoCG) was positively correlated with RBD severity [as measured by REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) score]. Conversely, the FC value between the bilateral CUN and the right MTG in PD-RBD patients was negatively correlated with RBDSQ score. This study revealed the presence replace with GMV and FC changes among PD-RBD patients, which were closely linked to the severity of RBD symptoms. Furthermore, the combination of basic clinical characteristics, GMVs and FC values effectively predicted RBD for individuals with PD.

Validity and reliability of REM sleep behaviour disorder screening questionnaire (RBDSQ) Indonesian version among Indonesian Parkinson’s disease patients: a pilot study

Validity and reliability of REM sleep behaviour disorder screening questionnaire (RBDSQ) Indonesian version among Indonesian Parkinson’s disease patients: a pilot study

Tasmanian ISLAND Sleep Study: prodromal neurodegenerative disease (iRBD) is associated with subjective reports of daytime sleepiness and poor sleep quality

AbstractBackgroundIsolated REM sleep behaviour disorder (iRBD) is a prodrome to dementia with Lewy bodies (DLB) and other neurodegenerative disease (NDD) such that over 90% of people with iRBD (pwRBD) will develop a NDD within 10 years of diagnosis. Disrupted sleep and dream‐enactment episodes are frequently observed by bed partners of pwRBD but it is unclear whether pwRBD perceive reduced sleep quality themselves, as previous studies show inconsistent results. This research aims to investigate sleep quality in iRBD and we hypothesise that people with probable iRBD (pwpRBD) will report poorer sleep quality, compared to healthy controls.MethodAdults aged 50+ years in the Tasmanian ISLAND Sleep Study, Australia, completed the single REM Sleep Behaviour Disorder Screening Question (RBD1Q) and the Pittsburgh Sleep Quality Index (PSQI) online. The PSQI includes a ‘total sleep quality’ score as well as 7 subscales: duration of sleep, sleep disturbance, sleep latency, day dysfunction due to sleepiness, sleep efficiency, overall sleep quality, and need meds to sleep.Result2,905 participants were recruited (mean (SD) age 64 (7.7) years; 26% male) and 273 (9.4%) screened positive for pRBD (45% male). In the pRBD group, 66% had poor total sleep quality, compared to 57% of controls (p = 0.001). pRBD was associated with a greater prevalence of sleep disturbance (67% vs 54%) and daytime dysfunction due to sleepiness (24% vs 11%). There was no difference in sleep duration, sleep latency, or sleep efficiency between pRBD and controls.ConclusionWe found that pwpRBD report more sleep disturbance, daytime sleepiness and overall poorer sleep quality compared to those without pRBD. This suggests that pwpRBD may suffer from greater sleepiness than has previously been reported in the literature and highlights the importance of sleep enquiries in those with suspected prodromal NDD who drive or operate machinery.

Isolated REM sleep behaviour disorder in the Tasmanian ISLAND Sleep Study: prevalence and profiles of prodromal dementia

AbstractBackgroundIdentifying people with isolated REM sleep behaviour disorder (iRBD) provides important opportunities for targeted dementia prevention as iRBD is a prodrome of Lewy Body dementia (LBD) and other neurodegenerative diseases (NDD). There have been no prevalence studies of iRBD in Australia and little is known about whether associated motor, cognitive, olfactory and circadian features influence the progression to different NDD. The Island Study Linking Ageing and Neurodegenerative Disease (ISLAND) Sleep Study aims to identify the iRBD prevalence in Tasmania and determine how different iRBD profiles predict progression to dementia and other NDD.MethodAdults aged 50+ years in Tasmania completed online questionnaires including the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ), RBD single question screen (RBD1Q), Pittsburgh Sleep Quality Index (PSQI), and questions regarding dreaming, autonomic, motor s, and pain symptoms.Result2,905 participants have been recruited (mean (SD) age 64 (7.7) years; 26% male). 273 (9.4%) have ‘probable’ iRBD (pRBD) based on the RBDQ1. Compared to those without pRBD, the pRBD group have poorer sleep quality (66% vs 57%), greater autonomic dysfunction (median score 18 vs 13), more pain (particularly stabbing and itching pain), higher risk for sleep apnoea (30% vs 13%), more intense dreams (21% vs 8%), and more motor symptoms, including shaky limbs (16% vs 6%) and slurred speech (12% vs 6%).Conclusion2905 participants have been recruited (mean (SD) age 64 (7.7) years; 26% male). 273 (9.4%) have ‘probable’ iRBD (pRBD) based on the RBDQ1. Compared to those without pRBD, the pRBD group have poorer sleep quality (66% vs 57%), greater autonomic dysfunction (median score 18 vs 13), more pain (particularly stabbing and itching pain), higher risk for sleep apnoea (30% vs 13%), more intense dreams (21% vs 8%), and more motor symptoms, including shaky limbs (16% vs 6%) and slurred speech (12% vs 6%).

Parkinson’s Disease Non-Motor Subtypes Classification in a Group of Slovenian Patients: Actuarial vs. Data-Driven Approach

Background and purpose: The aim of this study was to examine the risk factors, prodromal symptoms, non-motor symptoms (NMS), and motor symptoms (MS) in different Parkinson’s disease (PD) non-motor subtypes, classified using newly established criteria and a data-driven approach. Methods: A total of 168 patients with idiopathic PD underwent comprehensive NMS and MS examinations. NMS were assessed by the Non-Motor Symptom Scale (NMSS), Montreal Cognitive Assessment (MoCA), Hamilton Depression Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Epworth Sleepiness Scale (ESS), Starkstein Apathy Scale (SAS) and Fatigue Severity Scale (FSS). Motor subtypes were classified based on Stebbins’ method. Patients were classified into groups of three NMS subtypes (cortical, limbic, and brainstem) based on the newly designed inclusion criteria. Further, data-driven clustering was performed as an alternative, statistical learning-based classification approach. The two classification approaches were compared for consistency. Results: We identified 38 (22.6%) patients with the cortical subtype, 48 (28.6%) with the limbic, and 82 (48.8%) patients with the brainstem NMS PD subtype. Using a data-driven approach, we identified five different clusters. Three corresponded to the cortical, limbic, and brainstem subtypes, while the two additional clusters may have represented patients with early and advanced PD. Pearson chi-square test of independence revealed that a priori classification and cluster membership were significantly related to one another with a large effect size (χ2(8) = 175.001, p &lt; 0.001, Cramer’s V = 0.722). The demographic and clinical profiles differed between NMS subtypes and clusters. Conclusion: Using the actuarial and clustering approach, marked differences between individual NMS subtypes were found. The newly established criteria have potential as a simplified tool for future clinical research of NMS subtypes of Parkinson’s disease.

The Genetic Basis of Probable REM Sleep Behavior Disorder in Parkinson's Disease.

Patients with Parkinson's Disease (PD) experience REM sleep behavior disorder (RBD) more frequently than healthy controls. RBD is associated with torpid disease evolution. To test the hypothesis that differential genetic signatures might contribute to the torpid disease evolution in PD patients with RBD we compared the rate of genetic mutations in PD patients with or without probable RBD. Patients with a clinical diagnosis of PD in the Parkinson's Progression Markers Initiative (PPMI) database entered the study. We excluded those with missing data, dementia, psychiatric conditions, or a diagnosis change over the first five years from the initial PD diagnosis. Probable RBD (pRBD) was confirmed by a REM Sleep Behavior Disorder Screening Questionnaire score > 5 points. Logistic regression and Machine Learning (ML) algorithms were used to relate Single Nucleotide Polymorphism (SNPs) in PD-related genes with pRBD. We included 330 PD patients fulfilling all inclusion and exclusion criteria. The final logistic multivariate model revealed that the following SNPs increased the risk of pRBD: GBA_N370S_rs76763715 (OR, 95% CI: 3.38, 1.45-7.93), SNCA_A53T_rs104893877 (8.21, 2.26-36.34), ANK2. CAMK2D_rs78738012 (2.12, 1.08-4.10), and ZNF184_rs9468199 (1.89, 1.08-3.33). Conversely, SNP COQ7. SYT17_rs11343 reduced pRBD risk (0.36, 0.15-0.78). The ML algorithms led to similar results. The predictive models were highly specific (95-99%) but lacked sensitivity (9-39%). We found a distinctive genetic signature for pRBD in PD. The high specificity and low sensitivity of the predictive models suggest that genetic mutations are necessary but not sufficient to develop pRBD in PD. Additional investigations are needed.

Olfactory Impairment Is the Main Predictor of Higher Scores at REM Sleep Behavior Disorder (RBD) Screening Questionnaire in Parkinson's Disease Patients.

Olfactory impairment and REM sleep behavior disorder (RBD) are common non-motor symptoms in Parkinson's disease (PD) patients, often preceding the onset of the specific motor symptoms and, thus, crucial for strategies directed to anticipate PD diagnosis. In this context, the specific interaction between olfactory impairment and RBD has not been clearly defined. The aim of this study was to determine the possible role of olfactory impairment and other clinical characteristics as possible predictors of higher scores at RBD screening questionnaire (RBDSQ) in a large population of PD patients. In this study, 590 PD patients were included from the Parkinson's Progression Markers Initiative. Demographic and clinical features were registered. All participants completed motor and non-motor evaluations at the baseline visit. For motor assessments, the disease severity was evaluated by the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) pars III. Regarding non-motor symptoms assessment, Montreal Cognitive Assessments (MoCA), University of Pennsylvania Smell Identification Test (UPSIT) and RBD screening questionnaire (RBDSQ) were registered. Among 590 PD patients included in this study, 111 patients with possible RBD were found (18.8%). RBD was less frequent in female PD patients (p ≤ 0.011). Among patients with or without possible RBD diagnosis, statistically significant differences in MDS-UPDRS III (23.3 ± 11.4 vs. 19.7 ± 9.1, respectively, p ≤ 0.002) and in UPSIT score (19.7 ± 8.3 vs. 22.6 ± 8.0, respectively, p ≤ 0.001) were found. Moreover, significant correlations between RBDSQ versus UPDRS III score and versus UPSIT score were observed. Multivariate linear regression analysis showed that UPSIT was the most significant predictor of higher scores at RBDSQ, while the other significant predictors were UPDRS III and age. The severity of olfactory impairment appears tightly correlated to RBD symptoms, highlighting the role of these biomarkers for PD patients. Additionally, according to this large study, our data confirmed that RBD in PD patients exhibits peculiar gender differences.

Motor and non-motor features in Parkinson's Disease patients carrying GBA gene mutations.

Mutations of the Glucocerebrosidase (GBA) gene are the most common genetic risk factor yet discovered for Parkinson's Disease (PD), being found in about 5-14% of Caucasian patients. We aimed to assess motor and non-motor symptoms (NMS) in patients with GBA-related PD (GBA-PD) in comparison with idiopathic PD (iPD) subjects using standardized and validated scales. Eleven (4M, 7 F) patients with GBA-PD and 22 iPD patients, selected from the same cohort and matched for gender, age, and disease duration, were enrolled. The disease severity was assessed by Unified Parkinson's Disease Rating Scale-section III, gait disorder and falls by Freezing of Gait Questionnaire, and motor fluctuations by Wearing off questionnaire. NMS were evaluated using the following scales: Mini-Mental State Examination and extended neuropsychological battery, if required, Non-Motor Symptoms Scale, SCOPA-AUT Questionnaire, Apathy Evaluation Scale, Beck Depression Inventory, Epworth Sleepiness Scale, Restless Legs Syndrome Rating Scale, REM Sleep Behavior Disorder Screening Questionnaire, and Questionnaire for Impulsive-Compulsive Disorders in Parkinson's disease. GBA-PD patients showed a more severe and rapidly progressive disease, and more frequent positive family history for PD, akinetic-rigid phenotype, postural instability, dementia, and psychosis in comparison to iPD. Two of three subjects carrying L444P mutation presented with early dementia. We also found a higher occurrence of fatigue, diurnal sleepiness, and intolerance to heat/cold in the carriers group. Our results confirm that NMS and a more severe and faster disease course more frequently occur among GBA-PD patients in comparison to iPD.

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study.

BackgroundActive ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson’s disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD.MethodsActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques.DiscussionWe hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.

Prevalence and features of non-motor symptoms in Wilson’s disease

IntroductionWilson's Disease (WD) is an autosomal recessive disorder caused by excessive copper deposition in liver, brain and other organs. The clinical picture is characterized by hepatic, psychiatric and neurological dysfunction. Movement disorders are the core neurological features, although non-motor symptoms (NMS), as cognitive/affective, autonomic and sleep disorders, may occur over time. We aimed to assess the frequency of NMS in WD patients compared with healthy subjects. MethodsTwenty-seven patients affected with genetically proven WD (12 F, 15 M) and 35 healthy controls (Ctrl; 17 F, 18 M), comparable for age and education, were enrolled. Eighteen patients presented with the neurological form of the disease (NV) and nine with the non-neurological variant (NNV). NMS were assessed in all subjects by the following clinical scales: Mini-Mental State Examination (MMSE), Non-Motor Symptoms Scale (NMSS), SCOPA-AUT Questionnaire, Apathy Evaluation Scale (AES), Beck Depression Inventory (BDI), Epworth Sleepiness Scale (ESS), Restless Legs Syndrome Rating Scale (RLSRS), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Questionnaire for Impulsive-Compulsive Disorders in Parkinson's disease (QUIP-RS). ResultsWe found that the patients showed more severe and frequent NMS and daytime sleepiness, and lower MMSE than Ctrl. In comparison to healthy subjects, NV subjects showed statistically significant higher ESS, NMSS, and RLSRS scores, and a lower MMSE score. Subtle and subclinical extrapyramidal/pyramidal signs and brain MRI signal abnormalities were detected in patients considered as asymptomatic for neurological disturbances. ConclusionsNMS are common among WD patient, in particular those with NV, likely due to the widespread pathological changes throughout the central nervous system.

Development and validation of a nomogram for freezing of gait in patients with Parkinson's Disease.

Freezing of gait (FOG) is a common and complex disabling episodic gait disturbance in patients with Parkinson's disease (PD). Currently, the treatment of FOG remains a challenge for clinicians. The aim of our study was to develop a nomogram for FOG risk based on data collected from Chinese patients with PD. A total of 379 PD patients (197 with FOG) from Kunming Medical University were recruited as a training cohort. Additionally, 339 PD patients (166 with FOG) were recruited from West China Hospital of Sichuan University, to serve as the validation cohort. The least absolute shrinkage and selection operator regression model was used to select clinical and demographic characteristics as well as blood markers, which were incorporated into a predictive model using multivariate logistic regression to predict the risk of developing FOG. The model was validated using the validation dataset, and model performance was evaluated using the C-index, calibration plot, and decision curve analyses. The final predictive model included the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) score, Parkinson's Disease Questionnaire (PDQ39), H-Y stage, and visuospatial function. The model showed good calibration and good discrimination, with a C-index value of 0.772 against the training cohort and 0.766 against the validation cohort. Decision curve analysis demonstrated the clinical utility of the nomogram. A nomogram incorporating RBDSQ, PDQ39, H-Y stage, and visuospatial function may reliably predict the risk of FOG in PD patients.

Reply to: Correlation of Antidepressant Use and Symptom Time Period in Dream Enactment Behaviors.

Firstly, I would like to thank Sumi et al.1 for showing interest in my recently published study2 and providing the opportunity to expand the discussion on this fascinating topic, that has both clinical and theoretical relevance. Sumi et al. had concerns about possible confounding due to medication use. Although all participants in the Parkinson's Progression Markers Initiative (PPMI) database were untreated for Parkinson's disease (PD) at baseline, which is important to highlight, since dopaminergic medications have previously been associated with dream alterations3; I do nevertheless agree that given the frequent comorbidity of depression and REM sleep behavior disorder in people with PD, that the effects of antidepressants and clonazepam used to treat these, ought to be considered. In fact, a previous study identified that 41.2% of PD participants in PPMI reported using antidepressants during the course of the study and 14.2% reported having used clonazepam.4 However, Sumi et al.'s statement that neither PPMI nor the study evaluated the presence of depression, is not entirely correct. In the study, total number of depressive symptoms, assessed using the extensively validated Geriatric Depression Scale,5 was included as a covariate in one of the sensitivity analyses.2 Crucially, the inclusion of depressive symptoms as a covariate did not alter the association between aggressive dream content and either motor or cognitive decline.2 Nonetheless, to ensure that the results were not confounded by antidepressant use specifically, I have now conducted two further analyses. First, I repeated the primary analyses after adjusting for antidepressant use at any time from baseline to the most recent study visit. Second, I repeated the analyses after including self-reported past or present depression (obtained from item 10 of the REM sleep behavior disorder screening questionnaire), as an additional covariate. Neither antidepressant use nor a history of depression attenuated the association between dream content and motor or cognitive decline (P's < 0.05). Similarly, after adjusting for clonazepam use, the associations between aggressive dream content and both outcomes remained significant (P's < 0.05). Finally, with respect to Sumi et al.'s third concern regarding fluctuation of aggressive dreams over time, it is pertinent to emphasize that these participants were all newly diagnosed at baseline, with a mean duration of 6.3 months since diagnosis. As such, it is unlikely that sufficient time had passed for there to be a significant group for whom “aggressive dreams [had] disappeared” during the course of PD. In any case, if such a group were to exist, it could actually have led to an underestimation of the association, since individuals who once had aggressive dreams would have been included in the non-aggressive dream group. Therefore, whilst the insightful concerns raised by Sumi et al are important to consider, I do not believe they challenge the core conclusions of this study, namely, that people with newly diagnosed, untreated PD who report having frequent aggressive dreams, may be at higher risk of early motor and cognitive decline than those who do not report having them. Data used in the preparation of this article were obtained from the Parkinson's Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org. Data were downloaded from the PPMI database on December 22, 2020. 1. Research Project: Conception, Organization, Execution; 2. Statistical Analysis: Design, Execution; 3. Manuscript Preparation AIO: 1,2,3. Each Parkinson's Progression Markers Initiative site received approval from a local ethical committee on human experimentation before commencement of the study. Written informed consent was obtained from all individuals who participated in the study. I confirm that I have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. No specific funding was received for this work. I declare that there are no conflicts of interest relevant to this work. The author is funded by a National Institute for Health Research academic clinical fellowship.

Sleep Disturbances and Associated Factors in Drug-Naïve Patients with Parkinson's Disease.

PurposeSleep disturbance is one of the common symptoms in Parkinson’s disease (PD). The study of sleep disturbance used to concentrate on treated PD. This study aimed to investigate the factors that are associated with the sleep quality of drug-naïve patients with PD.Patients and MethodsAll participants were interviewed using a standard questionnaire to collect basic information. PD severity, depression symptoms, anxiety symptoms, sleep quality, cognitive status, life quality, and the presence of rapid eye movement (REM) sleep behavior disorder (RBD) and minor hallucination were assessed using corresponding rating scales. The patients with a Pittsburgh Sleep Quality Index score ≤6 fell into the poor sleep group, and those with REM Sleep Behavior Disorder Screening Questionnaire score ≥5 were considered to have probable RBD.ResultsSeventy drug-naive patients with PD and 30 healthy controls matched for age, sex, and education were recruited. Up to 41.4% of the patients suffered from sleep disturbance, and 24.3% of the patients had RBD. Poor sleepers were more likely to have left-side predominant motor symptoms. Compared with good sleepers, poor sleepers, particularly female patients, had more burden in the aspect of anxiety and depression. RBD was associated with more nonmotor symptoms, poor sleep quality, bad performance in cognition orientation domain, anxiety, depression, presence of minor hallucination, and poor life quality.ConclusionSleep disturbances are common in drug-naïve PD and require wide attention. Motor symptom laterality and gender difference in mood are associated with sleep quality. Depression, anxiety, and RBD are highly related to sleep disturbance. RBD has many comorbidities, which can influence the cognitive function and life quality of the patients.

Dream Content Predicts Motor and Cognitive Decline in Parkinson's Disease.

Dream content alterations in Parkinson's disease (PD) are associated with motor and cognitive dysfunction cross-sectionally. Although recent studies suggest abnormal dream content in PD might also predict cognitive decline, the relationship between dream content and motor decline in PD remains unknown. To investigate whether abnormal dream content in PD predicts both motor and cognitive decline. Data were obtained from the Parkinson's Progression Markers Initiative cohort study. Patients were evaluated at baseline and at the 60-month follow-up, with validated clinical scales, including the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Montreal Cognitive Assessment (MoCA), and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Patients were dichotomized using RBDSQ item 2, which inquires whether they frequently experience aggression in their dreams. Regression analyses were used to assess whether frequent aggressive dreams at baseline predicted longitudinal changes in MDS-UPDRS III and MoCA scores as well as progression to Hoehn and Yahr stage 3 (H&Y ≥ 3) and cognitive impairment. Of the patients, 58/224 (25.9%) reported frequent aggressive dreams at baseline. Aggressive dreams predicted a faster increase in MDS-UPDRS III scores (β=4.64; P= 0.007) and a faster decrease in MoCA scores (β=-1.49; P= 0.001). Furthermore, they conferred a 6-fold and 2-fold risk for progressing to H&Y ≥ 3 (odds ratio [OR]=5.82; P= 0.005) and cognitive impairment (OR,2.35; P= 0.023) within 60 months. These associations remained robust when adjusting for potential confounders. This study demonstrates for the first time that frequent aggressive dreams in newly diagnosed PD may independently predict early motor and cognitive decline.

Fatigue prevalence and associated factors in patients with multiple system atrophy.

Fatigue was reported a determinant of poor quality of life in multiple system atrophy (MSA) patients. This study aimed to determine fatigue prevalence and associated demographic, motor, and non-motor symptoms in MSA patients. A total of 174 MSA patients met "Probable" diagnostic criteria were included in this cross-sectional study. Fatigue Severity Scale (FSS) was used to measure fatigue prevalence. Unified MSA Rating Scale (UMSARS), Non-Motor Symptoms Scale (NMSS), Hamilton Depression Rating Scale-17 (HDRS-17), Hamilton Anxiety Scale (HAMA), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), and Mini-Mental State Exam (MMSE) were used for comprehensive clinical assessments. Nonparametric Mann-Whitney or Pearson's chi-square test was used to compare the patient score with or without fatigue (defined as a mean FSS score≥4). Binary logistic regression analysis was performed to determine features independently associated with the presence of fatigue. Fifty (28.7%) patients enrolled reported fatigue. Results of multivariate analysis revealed that anxiety (OR=3.01, 95% CI=1.43-6.31), excessive daytime sleepiness (OR=2.70, 95% CI=1.23-5.90), and use of sleep medicine (OR=3.58, 95% CI=1.39-9.24) were significantly associated with fatigue in MSA patients. Fatigue is common in our MSA patients. Anxiety, excessive daytime sleepiness, and current sleep medicine use may be associated with an increased risk of fatigue. However, the severity of motor symptoms may not be associated with fatigue. Our findings highlight the need to identify, investigate, and treat fatigue in MSA.

Modifiable Comorbidities Associated with Cognitive Decline in Parkinson's Disease.

Cognitive impairment (CI) is one of the most feared and debilitating complications of PD. No therapy has been shown to slow or prevent CI in PD. To determine associations between modifiable comorbidities, including cardiovascular disease risk factors, mood disorders, and sleep characteristics, and rate of cognitive decline in Parkinson's disease (PD). Data from the Parkinson's Progression Markers Initiative (PPMI) cohort was queried for baseline cardiovascular disease risk factors, mood disorders, and sleep characteristics. Linear mixed- effects models (LME) were used to examine the association between baseline factors and change in cognition, evaluated by the Montreal Cognitive Assessment (MoCA) over time. Baseline comorbidities found to affect MoCA decline were assessed for an association with focal cognitive domains using LME. Higher Body Mass Index (BMI) (β = -0.009, P = 0.039), State Trait Anxiety Inventory (STAI) (β = -0.005, P < 0.001), Geriatric Depression Scale (GDS) (β = -0.034, P < 0.001), Epworth Sleepiness Scale (ESS) (β = -0.017, P = 0.003), and REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) (β = -0.037, P < 0.001) were associated with faster rates of MoCA decline. Using established cut-offs for clinically significant symptoms, being overweight, or the presence of depression, excessive day time sleepiness (EDS), and possible REM sleep behavior disorder (pRBD), were all associated with faster rate of cognitive decline. Several modifiable baseline comorbidities are associated with faster rate of CI over time in patients with PD. These associations identify potential opportunities for early intervention that could influence CI in PD.