OBJECTIVEWe sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson ...disease (PD) using a customized smartphone application.
METHODSA total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups.
RESULTSStatistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory.
CONCLUSIONSProdromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinsonʼs Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD.
See Morris and Weil (doi:10.1093/brain/awz014) for a scientific commentary on this article.
In a prospective multicentre study involving 1280 patients with idiopathic RBD, Postuma et al. show that ...approximately 6% of patients each year (>73.5% over 12 years) convert to full neurodegenerative disease. They test the predictive power of 21 prodromal markers of neurodegeneration, providing a template for planning neuroprotective trials.
Abstract
Idiopathic REM sleep behaviour disorder (iRBD) is a powerful early sign of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This provides an unprecedented opportunity to directly observe prodromal neurodegenerative states, and potentially intervene with neuroprotective therapy. For future neuroprotective trials, it is essential to accurately estimate phenoconversion rate and identify potential predictors of phenoconversion. This study assessed the neurodegenerative disease risk and predictors of neurodegeneration in a large multicentre cohort of iRBD. We combined prospective follow-up data from 24 centres of the International RBD Study Group. At baseline, patients with polysomnographically-confirmed iRBD without parkinsonism or dementia underwent sleep, motor, cognitive, autonomic and special sensory testing. Patients were then prospectively followed, during which risk of dementia and parkinsonsim were assessed. The risk of dementia and parkinsonism was estimated with Kaplan-Meier analysis. Predictors of phenoconversion were assessed with Cox proportional hazards analysis, adjusting for age, sex, and centre. Sample size estimates for disease-modifying trials were calculated using a time-to-event analysis. Overall, 1280 patients were recruited. The average age was 66.3 ± 8.4 and 82.5% were male. Average follow-up was 4.6 years (range = 1-19 years). The overall conversion rate from iRBD to an overt neurodegenerative syndrome was 6.3% per year, with 73.5% converting after 12-year follow-up. The rate of phenoconversion was significantly increased with abnormal quantitative motor testing hazard ratio (HR) = 3.16, objective motor examination (HR = 3.03), olfactory deficit (HR = 2.62), mild cognitive impairment (HR = 1.91-2.37), erectile dysfunction (HR = 2.13), motor symptoms (HR = 2.11), an abnormal DAT scan (HR = 1.98), colour vision abnormalities (HR = 1.69), constipation (HR = 1.67), REM atonia loss (HR = 1.54), and age (HR = 1.54). There was no significant predictive value of sex, daytime somnolence, insomnia, restless legs syndrome, sleep apnoea, urinary dysfunction, orthostatic symptoms, depression, anxiety, or hyperechogenicity on substantia nigra ultrasound. Among predictive markers, only cognitive variables were different at baseline between those converting to primary dementia versus parkinsonism. Sample size estimates for definitive neuroprotective trials ranged from 142 to 366 patients per arm. This large multicentre study documents the high phenoconversion rate from iRBD to an overt neurodegenerative syndrome. Our findings provide estimates of the relative predictive value of prodromal markers, which can be used to stratify patients for neuroprotective trials.
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 < 0.000001), constipation, (P < 0.000001) and age over 70 years (P = 0.0002). Combined features obtained from the generalized logistic regression achieved a hazard ratio of 5.71 (95% confidence interval 2.85-11.43). Bayesian classifier suggested that patients with higher Mini-Mental State Examination score and lower caudate SBR asymmetry were more likely to develop parkinsonism, while patients with the opposite pattern were more likely to develop dementia. This study shows that iRBD patients older than 70 with constipation and reduced nigro-putaminal dopaminergic function are at high risk of short-term phenoconversion to an overt synucleinopathy, providing an effective stratification approach for future neuroprotective trials. Moreover, we provide cut-off values for the significant predictors of phenoconversion to be used in single subjects.
Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of ...change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).
•We developed a robust capability for operational monitoring of land surface change.•Landsat ARD and Continuous Change Detection and Classification are foundational.•Landsat's rich time series has substantial variability in observation frequency.•The algorithm was modified reducing variability in results between scene centers and overlap zones.•Classification was modified to improve training data representativeness and reduce artifacts.
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.Methods1601 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.ResultsWe 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%.ConclusionsWe 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.
IMPORTANCE: Nonmotor symptoms of Parkinson disease (PD) often predate the movement disorder by decades. Currently, there is no blood biomarker to define this prodromal phase. OBJECTIVE: To ...investigate whether α-synuclein in neuronally derived serum-extracellular vesicles identifies individuals at risk of developing PD and related dementia. DESIGN, SETTING, AND PARTICIPANTS: This retrospective, cross-sectional multicenter study of serum samples included the Oxford Discovery, Marburg, Cologne, and Parkinson’s Progression Markers Initiative cohorts. Participants were recruited from July 2013 through August 2023 and samples were analyzed from April 2022 through September 2023. The derivation group (n = 170) included participants with isolated rapid eye movement sleep behavior disorder (iRBD) and controls. Two validation groups were used: the first (n = 122) included participants with iRBD and controls and the second (n = 263) included nonmanifest GBA1N409S gene carriers, participants with iRBD or hyposmia, and available dopamine transporter single-photon emission computed tomography, healthy controls, and patients with sporadic PD. Overall the study included 199 participants with iRBD, 20 hyposmic participants with available dopamine transporter single-photon emission computed tomography, 146 nonmanifest GBA1N409S gene carriers, 21 GBA1N409S gene carrier patients with PD, 50 patients with sporadic PD, and 140 healthy controls. In the derivation group and validation group 1, participants with polysomnographically confirmed iRBD were included. In the validation group 2, at-risk participants with available Movement Disorder Society prodromal markers and serum samples were included. Among 580 potential participants, 4 were excluded due to alternative diagnoses. EXPOSURES: Clinical assessments, imaging, and serum collection. MAIN OUTCOME AND MEASURES: L1CAM-positive extracellular vesicles (L1EV) were immunocaptured from serum. α-Synuclein and syntenin-1 were measured by electrochemiluminescence. Area under the receiver operating characteristic (ROC) curve (AUC) with 95% CIs evaluated biomarker performance. Probable prodromal PD was determined using the updated Movement Disorder Society research criteria. Multiple linear regression models assessed the association between L1EV α-synuclein and prodromal markers. RESULTS: Among 576 participants included, the mean (SD) age was 64.30 (8.27) years, 394 were male (68.4%), and 182 were female (31.6%). A derived threshold of serum L1EV α-synuclein distinguished participants with iRBD from controls (AUC = 0.91; 95% CI, 0.86-0.96) and those with more than 80% probability of having prodromal PD from participants with less than 5% probability (AUC = 0.80; 95% CI, 0.71-0.89). Subgroup analyses revealed that specific combinations of prodromal markers were associated with increased L1EV α-synuclein levels. Across all cohorts, L1EV α-synuclein differentiated participants with more than 80% probability of having prodromal PD from current and historic healthy control populations (AUC = 0.90; 95% CI, 0.87-0.93), irrespective of initial diagnosis. L1EV α-synuclein was increased in at-risk participants with a positive cerebrospinal fluid seed amplification assay and was above the identified threshold in 80% of cases (n = 40) that phenoconverted to PD or related dementia. CONCLUSIONS AND RELEVANCE: L1EV α-synuclein in combination with prodromal markers should be considered in the stratification of those at high risk of developing PD and related Lewy body diseases.
A field spray drift experiment using florpyrauxifen-benzyl was conducted to measure drift from commercial ground and aerial applications, evaluate soybean Glycine max (L.) Merr. impacts, and compare ...to United States Environmental Protection Agency (US EPA) drift models. Collected field data were consistent with US EPA model predictions. Generally, with both systems applying a Coarse spray in a 13-kph average wind speed, the aerial application had a 5.0- to 8.6-fold increase in drift compared to the ground application, and subsequently, a 1.7- to 3.6-fold increase in downwind soybean injury. Soybean reproductive structures were severely reduced following herbicide exposure, potentially negatively impacting pollinator foraging sources. Approximately a 25% reduction of reproductive structures up to 30.5-m downwind and nearly a 100% reduction at 61-m downwind were observed for ground and aerial applications, respectively. Aerial applications would require three to five swath width adjustments upwind to reduce drift potential similar to ground applications.
The process of neurodegeneration in Parkinson's disease begins long before the onset of clinical motor symptoms, resulting in substantial cell loss by the time a diagnosis can be made. The period ...between the onset of neurodegeneration and the development of motoric disease would be the ideal time to intervene with disease modifying therapies. This pre-motor phase can last many years, but the lack of a specific clinical phenotype means that objective biomarkers are needed to reliably detect prodromal disease. In recent years, recognition that patients with REM sleep behaviour disorder (RBD) are at particularly high risk of future parkinsonism has enabled the development of large prodromal cohorts in which to investigate novel biomarkers, and neuroimaging has generated some of the most promising results to date. Here we review investigations undertaken in RBD and other pre-clinical cohorts, including modalities that are well established in clinical Parkinson's as well as novel imaging methods. Techniques such as high resolution MRI of the substantia nigra and functional imaging of Parkinsonian brain networks have great potential to facilitate early diagnosis. Further longitudinal studies will establish their true value in quantifying prodromal neurodegeneration and predicting future Parkinson's.
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