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OBJECTIVE: We recently demonstrated that 998 features derived from a simple 7-minute smartphone test could distinguish between controls, people with Parkinson's and people with idiopathic Rapid Eye Movement sleep behavior disorder, with mean sensitivity/specificity values of 84.6-91.9%. Here, we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson's. METHODS: A total of 237 participants with Parkinson's (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest, and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18-month follow-up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10-fold and subject-wise cross validation schemes. RESULTS: Baseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment, and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10-fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained. INTERPRETATION: We demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualized predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome. ispartof: ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY vol:6 issue:8 pages:1498-1509 ispartof: location:United States status: published

Objective: We recently demonstrated that 998 features derived from a simple 7-minute smartphone test could distinguish between controls, people with Parkinson’s and people with idiopathic Rapid Eye Movement sleep behaviour disorder, with mean sensitivity/specificity values of 84.6-91.9%. Here we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson’s.Methods: 237 participants with Parkinson’s (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18-month follow up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10-fold and subject-wise cross validation schemes. Results: Baseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10-fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained.Interpretation: We demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualised predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome.

ObjectivesTo use a data-driven approach to determine the existence and natural history of subtypes of Parkinson’s disease (PD) using two large independent cohorts of patients newly diagnosed with this condition.Methods 1601 and 944 patients with idiopathic PD, from Tracking Parkinson’s and Discovery cohorts, respectively, were evaluated in motor, cognitive and non-motor domains at the baseline assessment. Patients were recently diagnosed at entry (within 3.5 years of diagnosis) and were followed up every 18 months. We used a factor analysis followed by a k-means cluster analysis, while prognosis was measured using random slope and intercept models.Results We identified four clusters: (1) fast motor progression with symmetrical motor disease, poor olfaction, cognition and postural hypotension; (2) mild motor and non-motor disease with intermediate motor progression; (3) severe motor disease, poor psychological well-being and poor sleep with an intermediate motor progression; (4) slow motor progression with tremor-dominant, unilateral disease. Clusters were moderately to substantially stable across the two cohorts (kappa 0.58). Cluster 1 had the fastest motor progression in Tracking Parkinson’s at 3.2 (95% CI 2.8 to 3.6) UPDRS III points per year while cluster 4 had the slowest at 0.6 (0.1–1.1). In Tracking Parkinson’s, cluster 2 had the largest response to levodopa 36.3% and cluster 4 the lowest 28.8%.Conclusions We have found four novel clusters that replicated well across two independent early PD cohorts and were associated with levodopa response and motor progression rates. This has potential implications for better understanding disease pathophysiology and the relevance of patient stratification in future clinical trials.

Apathy is a common and under-recognized disorder that often emerges in the prodromal phase of Parkinsonian diseases. The mechanism by which this occurs is not known, but recent evidence from patients with established Parkinson’s disease suggests that serotonergic dysfunction may play a role. The integrity of the raphe serotonergic system can be assessed alongside dopaminergic basal ganglia imaging using the radioligand 123I-ioflupane, which binds both serotonin and dopamine transporters. To investigate the relative roles of these neurotransmitters in prodromal parkinsonism, we imaged patients with idiopathic rapid eye movement sleep behaviour disorder, the majority of whom will develop a parkinsonian disorder in future. Forty-three patients underwent brain imaging with 123I-ioflupane single photon emission computed tomography and structural MRI. Apathy was quantified using the Lille Apathy Rating Scale. Other clinical parkinsonian features were assessed using standard measures. A negative correlation was observed between apathy severity and serotonergic 123I-ioflupane signal in the dorsal raphe nucleus (r = −0.55, P < 0.001). There was no significant correlation between apathy severity and basal ganglia dopaminergic signal, nor between dorsal raphe signal and other neuropsychiatric scores. This specific association between apathy and raphe 123I-ioflupane signal suggests that the serotonergic system might represent a target for the treatment of apathy.

BACKGROUND: Apathy is an important neuropsychiatric feature of Parkinson's disease (PD), which often emerges before the onset of motor symptoms. Patients with rapid eye movement sleep behaviour disorder (RBD) have a high probability of developing Parkinson's disease in future. Neuropsychiatric problems are common in RBD, but apathy has not previously been detailed in this key prodromal population.METHODS: 88 patients with polysomnographically proven RBD, 65 patients with PD and 33 controls were assessed for apathy using the Lille Apathy Rating Scale (LARS). Cognition and depression were also quantified. The sensitivity of the Unified Parkinson's Disease Rating Scale screening questions for apathy and depression was calculated.RESULTS: 46% of Patients with RBD were apathetic, compared with 31% of Parkinson's patients in our sample. Most RBD patients with depression were apathetic but more than half of apathetic patients were not depressed. The sensitivity of the single UPDRS screening question was only 33% for mild apathy and 50% for severe apathy.CONCLUSIONS: Apathy is common in RBD and is underestimated by a single self-report question. Recognition of apathy as a distinct neuropsychiatric feature in RBD could aid targeted treatment interventions and might contribute to understanding of prodromal Parkinson's disease. This article is protected by copyright. All rights reserved.

ObjectivesRapid eye movement (REM) sleep behavior disorder (RBD) is the most specific marker of prodromal alpha-synucleinopathies. We sought to delineate the baseline clinical characteristics of RBD and evaluate risk stratification models.MethodsClinical assessments were performed in 171 RBD, 296 control, and 119 untreated Parkinson’s (PD) participants. Putative risk measures were assessed as predictors of prodromal neurodegeneration, and Movement Disorders Society (MDS) criteria for prodromal PD were applied. Participants were screened for common leucine-rich repeat kinase 2 (LRRK2)/glucocerebrosidase gene (GBA) gene mutations.ResultsCompared to controls, participants with RBD had higher rates of solvent exposure, head injury, smoking, obesity, and antidepressant use. GBA mutations were more common in RBD, but no LRRK2 mutations were found. RBD participants performed significantly worse than controls on Unified Parkinson’s Disease Rating Scale (UPDRS)-III, timed “get-up-and-go”, Flamingo test, Sniffin Sticks, and cognitive tests and had worse measures of constipation, quality of life (QOL), and orthostatic hypotension. For all these measures except UPDRS-III, RBD and PD participants were equally impaired. Depression, anxiety, and apathy were worse in RBD compared to PD participants. Stratification of people with RBD according to antidepressant use, obesity, and age altered the odds ratio (OR) of hyposmia compared to controls from 3.4 to 45.5. 74% (95% confidence interval [CI] 66%, 80%) of RBD participants met the MDS criteria for probable prodromal Parkinson’s compared to 0.3% (95% CI 0.009%, 2%) of controls.ConclusionsRBD are impaired across a range of clinical measures consistent with prodromal PD and suggestive of a more severe nonmotor subtype. Clinical risk stratification has the potential to select higher risk patients for neuroprotective interventions.

The present study aimed to examine whether the APOE ε4 allele, associated with dementia with Lewy bodies (DLB), and possibly with dementia in Parkinson's disease (PD), is also associated with idiopathic rapid eye movement sleep behavior disorder (RBD). Two single nucleotide polymorphisms, rs429358 and rs7412, were genotyped in RBD patients (n = 480) and in controls (n = 823). APOE ε4 allele frequency was 0.14 among RBD patients and 0.13 among controls (OR = 1.11, 95% CI: 0.88–1.40, p = 0.41). APOE ε4 allele frequencies were similar in those who converted to DLB (0.14) and those who converted to Parkinson's disease (0.12) or multiple system atrophy (0.14, p = 1.0). The APOE ε4 allele is neither a risk factor for RBD nor it is associated with conversion from RBD to DLB or other synucleinopathies.

International audience; The present study aimed to examine whether the APOE ε4 allele, associated with dementia with Lewy bodies (DLB), and possibly with dementia in Parkinson's disease (PD), is also associated with idiopathic rapid eye movement sleep behavior disorder (RBD). Two single nucleotide polymorphisms, rs429358 and rs7412, were genotyped in RBD patients (n = 480) and in controls (n = 823). APOE ε4 allele frequency was 0.14 among RBD patients and 0.13 among controls (OR = 1.11, 95% CI: 0.88-1.40, p = 0.41). APOE ε4 allele frequencies were similar in those who converted to DLB (0.14) and those who converted to Parkinson's disease (0.12) or multiple system atrophy (0.14, p = 1.0). The APOE ε4 allele is neither a risk factor for RBD nor it is associated with conversion from RBD to DLB or other synucleinopathies.

This is an international multicentre study aimed at evaluating the combined value of dopaminergic neuroimaging and clinical features in predicting future phenoconversion of idiopathic REM sleep behaviour (iRBD) subjects to overt synucleinopathy. Nine centres sent 123I-FP-CIT-SPECT data of 344 iRBD patients and 256 controls for centralized analysis. 123I-FP-CIT-SPECT images were semiquantified using DaTQUANTTM, obtaining putamen and caudate specific to non-displaceable binding ratios (SBRs). The following clinical variables were also analysed: (i) Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale, motor section score; (ii) Mini-Mental State Examination score; (iii) constipation; and (iv) hyposmia. Kaplan-Meier survival analysis was performed to estimate conversion risk. Hazard ratios for each variable were calculated with Cox regression. A generalized logistic regression model was applied to identify the best combination of risk factors. Bayesian classifier was used to identify the baseline features predicting phenoconversion to parkinsonism or dementia. After quality check of the data, 263 iRBD patients (67.6 ± 7.3 years, 229 males) and 243 control subjects (67.2 ± 10.1 years, 110 males) were analysed. Fifty-two (20%) patients developed a synucleinopathy after average follow-up of 2 years. The best combination of risk factors was putamen dopaminergic dysfunction of the most affected hemisphere on imaging, defined as the lower value between either putamina (P