Longitudinal assessment of excessive daytime sleepiness in early Parkinson's disease.

BACKGROUND: Excessive daytime sleepiness (EDS) is common and disabling in Parkinson's disease (PD). Predictors of EDS are unclear, and data on biological correlates of EDS in PD are limited. We investigated clinical, imaging and biological variables associated with longitudinal changes in sleepiness in early PD. METHODS: The Parkinson's Progression Markers Initiative is a prospective cohort study evaluating progression markers in participants with PD who are unmedicated at baseline (n=423) and healthy controls (HC; n=196). EDS was measured with the Epworth Sleepiness Scale (ESS). Clinical, biological and imaging variables were assessed for associations with EDS for up to 3 years. A machine learning approach (random survival forests) was used to investigate baseline predictors of incident EDS. RESULTS: ESS increased in PD from baseline to year 3 (mean±SD 5.8±3.5 to 7.55±4.6, p<0.0001), with no change in HC. Longitudinally, EDS in PD was associated with non-tremor dominant phenotype, autonomic dysfunction, depression, anxiety and probable behaviour disorder, but not cognitive dysfunction or motor severity. Dopaminergic therapy was associated with EDS at years 2 and 3, as dose increased. EDS was also associated with presynaptic dopaminergic dysfunction, whereas biofluid markers at year 1 showed no significant associations with EDS. A predictive index for EDS was generated, which included seven baseline characteristics, including non-motor symptoms and cerebrospinal fluid phosphorylated-tau/total-tau ratio. CONCLUSIONS: In early PD, EDS increases significantly over time and is associated with several clinical variables. The influence of dopaminergic therapy on EDS is dose dependent. Further longitudinal analyses will better characterise associations with imaging and biomarkers.

Application of the movement disorder society prodromal Parkinson's disease research criteria in 2 independent prospective cohorts.

BACKGROUND: The research criteria for prodromal PD of the MDS propose a new approach for the assessment of the individual probability of prodromal PD. These criteria require a testing of their reliability in different prospective cohorts. OBJECTIVES: The objective was to evaluate the MDS prodromal PD criteria in 2 independent prospective studies. METHODS: Prodromal PD probabilities of the Tübingen Evaluation of Risk Factors for Early Detection of Neurodegeneration cohort (TREND study, n = 650, recruited by the presence of probable rapid eye movement sleep behavior disorder, depression, and/or hyposmia or none of these at baseline and 2-, 4-, and 6-year follow-up) and the population-based Prospective Evaluation of Risk Factors for Idiopathic Parkinson's Syndrome cohort (PRIPS Tübingen subsample; n = 715, baseline and 3- and 5-year follow-up) were calculated. Baseline posttest probabilities, time to PD diagnosis, marker constellations, and longitudinal changes of prodromal PD probabilities were analyzed. RESULTS: Incident PD cases (TREND, n = 10; PRIPS = 7) showed significantly higher likelihood ratios of risk and prodromal markers at baseline when compared with nonconverters. Only 2 of 17 incident PD cases met the criteria for probable prodromal PD (ie, posttest probability > 80%) and 5 had possible prodromal PD (ie, > 50%) 1.4 to 3.8 years before diagnosis. The criteria showed high specificity and negative predictive values (>98%), but low sensitivity (TREND, 30%; PRIPS, 14%) and positive predictive values (TREND, 19%, PRIPS, 50%). The individual risk for prodromal PD in incident PD cases showed an inverse correlation with the time to conversion (Spearman rho = .80, P = .006) and unlike in nonconverters, increased during follow-up. CONCLUSION: The MDS prodromal criteria provide a practical framework for the calculation of prodromal PD risk. Although specificity of the criteria is high, most patients will not meet the criteria before diagnosis unless testing is thoroughly performed with numerous and highly specific markers objectively assessed. © 2017 International Parkinson and Movement Disorder Society.

Correlates of excessive daytime sleepiness in de novo Parkinson's disease: A case control study.

OBJECTIVE: This study was undertaken to determine the frequency and correlates of excessive daytime sleepiness in de novo, untreated Parkinson's disease (PD) patients compared with the matched healthy controls. METHODS: Data were obtained from the Parkinson's Progression Markers Initiative, an international study of de novo, untreated PD patients and healthy controls. At baseline, participants were assessed with a wide range of motor and nonmotor scales, including the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Excessive daytime sleepiness was assessed based on the Epworth Sleepiness scale (ESS), with a cutoff of 10. RESULTS: Four hundred twenty-three PD subjects and 196 healthy controls were recruited into the study. Mean ESS (min, max) score was 5.8 (0, 20) for the PD subjects and 5.6 (0, 19) for healthy controls (P = 0.54). Sixty-six (15.6%) PD subjects and 24 (12%) healthy controls had ESS of at least 10 (P = 0.28). No difference was seen in demographic characteristics, age of onset, disease duration, PD subtype, cognitive status, or utilization of sedatives between the PD sleepiness-positive versus the negative group. The sleepiness-positive group had higher MDS-UPDRS Part I and II but not III scores, and higher depression and autonomic dysfunction scores. Sleepiness was associated with a marginal reduction of A-beta (P = 0.05) but not alpha-synuclein spinal fluid levels in PD. CONCLUSIONS: This largest case control study demonstrates no difference in prevalence of excessive sleepiness in subjects with de novo untreated PD compared with healthy controls. The only clinical correlates of sleepiness were mood and autonomic dysfunction. Ongoing longitudinal analyses will be essential to further examine clinical and biological correlates of sleepiness in PD and specifically the role of dopaminergic therapy.

Premotor and nonmotor features of Parkinson's disease.

PURPOSE OF REVIEW: This review highlights recent advances in premotor and nonmotor features in Parkinson's disease, focusing on these issues in the context of prodromal and early-stage Parkinson's disease. RECENT FINDINGS: Although Parkinson's disease patients experience a wide range of nonmotor symptoms throughout the disease course, studies demonstrate that nonmotor features are not solely a late manifestation. Indeed, disturbances of smell, sleep, mood, and gastrointestinal function may herald Parkinson's disease or related synucleinopathies and precede these neurodegenerative conditions by 5 or more years. In addition, other nonmotor symptoms such as cognitive impairment are now recognized in incident or de-novo Parkinson's disease cohorts. Many of these nonmotor features reflect disturbances in nondopaminergic systems and early involvement of peripheral and central nervous systems, including olfactory, enteric, and brainstem neurons as in Braak's proposed pathological staging of Parkinson's disease. Current research focuses on identifying potential biomarkers that may detect persons at risk for Parkinson's disease and permit early intervention with neuroprotective or disease-modifying therapeutics. SUMMARY: Recent studies provide new insights into the frequency, pathophysiology, and importance of nonmotor features in Parkinson's disease as well as the recognition that these nonmotor symptoms occur in premotor, early, and later phases of Parkinson's disease.

Suggested immobilization test for diagnosis of restless legs syndrome in Parkinson's disease.

Diagnosis of restless leg syndrome (RLS) in Parkinson's disease (PD) is difficult because of clinical confounds. The suggested immobilization test (SIT) is validated for diagnosis of primary RLS. This study evaluated the usefulness of the SIT for diagnosis of RLS in PD. We compared SIT scores, as well as polysomnography measures in 50 patients with PD (25 with RLS, 25 without), 25 patients with primary RLS, and 25 age/sex matched controls. Mean leg discomfort score was increased in patients with PD and RLS compared to PD without RLS, and also in patients with primary RLS compared to controls. Leg discomfort was significantly higher at the end of the test in patients with RLS compared to patients without RLS. Intensity of leg discomfort was similar between patients with RLS, with or without PD. Using a mean leg discomfort cutoff of 11, we showed sensitivity of 91% and specificity of 72% for RLS diagnosis in PD during symptomatic time intervals. Periodic leg movements index during the SIT did not differ between groups. Periodic leg movements index during sleep and wakefulness was increased in patients with primary RLS compared to controls, but did not differ between patients with PD, with and without RLS. The sensory SIT is a simple test that may help diagnose RLS in patients with PD.

Dream enactment behavior: review for the clinician.

NONE: Dream enactment behavior commonly occurs on occasion in normal children and adults. Disruptive and frequent dream enactment behavior may come to the attention of the clinician either as the primary reason for consultation or as a prominent characteristic of a patient with other sleep disorders. Questioning patients with chronic neurologic and psychiatric disorders may also reveal previously unrecognized behavior. In the absence of sleep pathology, process of dream enactment likely begins with active, often emotionally charged dream content that may occasionally break through the normal REM sleep motor suppressive activity. Disrupted sleep resulting from many possible causes, such as circadian disruption, sleep apnea, or medications, may also disrupt at least temporarily the motor-suppressive activity in REM sleep, allowing dream enactment to occur. Finally, pathological neurological damage in the context of degenerative, autoimmune, and infectious neurological disorders may lead to chronic recurrent and severe dream enactment behavior. Evaluating the context, frequency, and severity of dream enactment behavior is guided first and foremost by a structured approach to the sleep history. Physical exam and selected testing support the clinical diagnosis. Understanding the context and the likely cause is essential to effective therapy.

Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder.

REM Behavior Disorder (RBD) is now recognized as the prodromal stage of α-synucleinopathies such as Parkinson's disease (PD). In this paper, we describe deep learning models for diagnosis/prognosis derived from a few minutes of eyes-closed resting electroencephalography data (EEG) collected at baseline from idiopathic RBD patients (n = 121) and healthy controls (HC, n = 91). A few years after the EEG acquisition (4±2 years), a subset of the RBD patients were eventually diagnosed with either PD (n = 14) or Dementia with Lewy bodies (DLB, n = 13), while the rest remained idiopathic RBD. We describe first a simple deep convolutional neural network (DCNN) with a five-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG spectrograms from idiopathic patients and healthy controls. We treat the data as in audio or image classification problems where deep networks have proven successful by exploiting invariances and compositional features in the data. For comparison, we study a simple deep recurrent neural network (RNN) model using three stacked Long Short Term Memory network (LSTM) cells or gated-recurrent unit (GRU) cells-with very similar results. The performance of these networks typically reaches 80% (±1%) classification accuracy in the balanced HC vs. PD-conversion classification problem. In particular, using data from the best single EEG channel, we obtain an area under the curve (AUC) of 87% (±1%)-while avoiding spectral feature selection. The trained classifier can also be used to generate synthetic spectrograms using the DeepDream algorithm to study what time-frequency features are relevant for classification. We find these to be bursts in the theta band together with a decrease of bursting in the alpha band in future RBD converters (i.e., converting to PD or DLB in the follow up) relative to HCs. From this first study, we conclude that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, offering physiological insights and enabling the identification of clinically relevant biomarkers.

Analysis of the PINK1 gene in a large cohort of cases with Parkinson disease.

BACKGROUND: Mutations in the PTEN-induced kinase (PINK1) gene located within the PARK6 locus on chromosome 1p35-p36 have recently been identified in patients with recessive early-onset Parkinson disease. OBJECTIVE: To assess the prevalence of PINK1 mutations within a series of early- and late-onset Parkinson disease patients living in North America. DESIGN: All coding exons of the PINK1 gene were sequenced in a series of 289 Parkinson disease patients and 80 neurologically normal control subjects; the mutation frequencies were evaluated in additional controls (100 white and 50 Filipino subjects). RESULTS: We identified 27 variants, including the first reported compound heterozygous mutation (Glu240Lys and Leu489Pro) and a homozygous Leu347Pro mutation in 2 unrelated young-onset Parkinson disease patients. CONCLUSION: Autosomal recessive mutations in PINK1 are a rare cause of young-onset Parkinson disease.

Abnormal metabolic network activity in REM sleep behavior disorder.

OBJECTIVE: To determine whether the Parkinson disease-related covariance pattern (PDRP) expression is abnormally increased in idiopathic REM sleep behavior disorder (RBD) and whether increased baseline activity is associated with greater individual risk of subsequent phenoconversion. METHODS: For this cohort study, we recruited 2 groups of RBD and control subjects. Cohort 1 comprised 10 subjects with RBD (63.5 ± 9.4 years old) and 10 healthy volunteers (62.7 ± 8.6 years old) who underwent resting-state metabolic brain imaging with (18)F-fluorodeoxyglucose PET. Cohort 2 comprised 17 subjects with RBD (68.9 ± 4.8 years old) and 17 healthy volunteers (66.6 ± 6.0 years old) who underwent resting brain perfusion imaging with ethylcysteinate dimer SPECT. The latter group was followed clinically for 4.6 ± 2.5 years by investigators blinded to the imaging results. PDRP expression was measured in both RBD groups and compared with corresponding control values. RESULTS: PDRP expression was elevated in both groups of subjects with RBD (cohort 1: p < 0.04; cohort 2: p < 0.005). Of the 17 subjects with long-term follow-up, 8 were diagnosed with Parkinson disease or dementia with Lewy bodies; the others did not phenoconvert. For individual subjects with RBD, final phenoconversion status was predicted using a logistical regression model based on PDRP expression and subject age at the time of imaging (r(2) = 0.64, p < 0.0001). CONCLUSIONS: Latent network abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome.

Morbidities in rapid eye movement sleep behavior disorder.

Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD, RBD without any obvious comorbid major neurological disease), is strongly associated with numerous comorbid conditions. The most prominent is that with neurodegenerative disorders, especially synuclein-mediated disorders, above all Parkinson disease (PD). Idiopathic RBD is an important risk factor for the development of synucleinopathies. Comorbidity studies suggest that iRBD is associated with a number of other potential pre-motor manifestations of synucleinopathies such as, cognitive and olfactory impairment, reduced autonomic function, neuropsychiatric manifestations and sleep complaints. Furthermore, patients with PD and RBD may have worse prognosis in terms of impaired cognitive function and overall morbidity/mortality; in dementia, the presence of RBD is strongly associated with clinical hallmarks and pathological findings of dementia with Lewy bodies. These findings underline the progressive disease process, suggesting involvement of more brain regions in patients with a more advanced disease stage. RBD is also associated with narcolepsy, and it is likely that RBD associated with narcolepsy is a distinct subtype associated with different comorbidities. RBD is also associated with antidepressant medications, autoimmune conditions, and, in rare cases, brainstem lesions.