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Abstract Parkinson’s Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.

Abstract The Neuronal alpha-Synuclein Disease (NSD) biological definition and Integrated Staging System (NSD-ISS) provide a research framework to identify individuals with Lewy body pathology and stage them based on underlying biology and increasing degree of functional impairment. Utilizing data from the PPMI, PASADENA, and SPARK studies, we developed and applied biologic and clinical data-informed definitions for the NSD-ISS across the disease continuum. Individuals enrolled as Parkinson’s disease, Prodromal, or Healthy Controls were defined and staged based on biological, clinical, and functional anchors at baseline. Across the three studies 1741 participants had SAA data and of these 1030 (59%) were S+ consistent with NSD. Among sporadic PD, 683/736 (93%) were NSD, and the distribution for Stages 2B, 3, and 4 was 25%, 63%, and 9%, respectively. Median (95% CI) time to developing a clinically meaningful outcome was 8.3 (6.2, 10.1), 5.9 (4.1, 6.0), and 2.4 (1.0, 4.0) years for baseline stage 2B, 3, and 4, respectively. We propose pilot biologic and clinical anchors for NSD-ISS. Our results highlight the baseline heterogeneity of individuals currently defined as early PD. Baseline stage predicts time to progression to clinically meaningful milestones. Further research on validation of the anchors in longitudinal cohorts is necessary.

Abstract Background Neurodegenerative diseases, such as Parkinson’s disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures. Methods Twenty-eight individuals with Parkinson’s disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person’s body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms. Results The support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively. Conclusion These results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic.

Novel therapies with the ability to delay disease progression are a gap in the care of people living with Parkinson disease (PD) today. Clinical outcomes assessments (COAs) that are sensitive to the earliest clinical changes in PD are deemed essential for a successful therapeutic development. To understand the current landscape of COAs use in clinical trials in PD and define priorities for future research in the field, a stakeholder roundtable meeting was held in November 2022. The current paper 1) proposes the collaborative development of patient-centric COAs that can adequately document the effectiveness of disease modification therapies in PD based on key priorities identified during this initial meeting, 2) summarizes the progress made in the subsequent 12 months, and 3) presents the deliverables expected in the near future. Key priorities include 1) the development of a consensus conceptual model of early PD experiences, 2) the adaptation of existing patient-reported outcomes (PROs), 3) the investigation of the role of observer-reported outcomes in addition to 4) enabling diversity in PD research and advocacy, 5) fostering data sharing, and 6) reaching consensus on a biological staging system for PD to drive the development of appropriate PROs for biologically defined populations.

Abstract We quantified concentrations of three isoforms of the endolysosomal lipid, bis(monoacylglycerol) phosphate (BMP) in the urine of deeply phenotyped cohorts in the Parkinson’s Progression Markers Initiative: LRRK2 G2019S PD (N = 134) and non-manifesting carriers (NMC) (G2019S+ NMC; N = 182), LRRK2 R1441G PD (N = 15) and R1441G+ NMC (N = 15), GBA1 N409S PD (N = 76) and N409S+ NMC (N = 178), sporadic PD (sPD, N = 379) and healthy controls (HC) (N = 190). The effects of each mutation and disease status were analyzed using nonparametric methods. Longitudinal changes in BMP levels were analyzed using linear mixed models. At baseline, all LRRK2 carriers had 3–7× higher BMP levels compared to HC, irrespective of the disease status. GBA1 N409S carriers also showed significant, albeit smaller, elevation (~30–40%) in BMP levels compared to HC. In LRRK2 G2019S PD, urinary BMP levels remained stable over two years. Furthermore, baseline BMP levels did not predict disease progression as measured by striatal DaT imaging, MDS-UPDRS III Off, or MoCA in any of the cohorts. These data support the utility of BMP as a target modulation biomarker in therapeutic trials of genetic and sPD but not as a prognostic or disease progression biomarker.

Background: Parkinson's disease (PD) and metabolic syndrome are separate entities that share common underlying pathophysiology. Management strategy for metabolic syndrome is clinically better characterized and finding a positive clinical correlation between the two could lead to a better understanding of Parkinson's disease progression and prognosis. Purpose: To explore the relationship between progression in PD and metabolic syndrome to characterize the underlying pathophysiology, which could then impact the clinical management of PD. Methods: Using modified NCEP (National Cholesterol Education Program) ATP III (Adult Treatment Plan) criteria, patients enrolled in STEADY-PD III (Safety, Tolerability, and Efficacy Assessment of Isradipine) were classified into one of three categories of metabolic syndrome and compared on PD progression over a period of 3 years. Results: Participants with metabolic syndrome showed a trend for more progression in terms of PD, as measured by the total Unified Parkinson's Disease Rating Scale (UPDRS), the motor EDL (Experiences of daily living) scores of the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS), and the Montreal Cognitive Assessment (MoCA). A significant trend for decline in MoCA was found in the group with metabolic syndrome (−0.78), compared with those without metabolic syndrome (0.14). Greater decline in MoCA signifies worse outcomes. Conclusion: The results demonstrate a trend in more clinical progression in PD in subjects with metabolic syndrome. However, results are limited by the sample size and the limited laboratory measurements available. We hope this study will encourage larger sample studies to explore this relationship further.

IntroductionTo test drugs with the potential to prevent the onset of Parkinson’s disease (PD), it is key to identify individuals in the general population at high risk of developing PD. This is often difficult because most of the clinical markers are non-specific, common in PD but also common in older adults (e.g., sleep problems).ObjectiveWe aimed to identify the clinical markers at high specificity for developing PD by comparing individuals with PD or prodromal PD to healthy controls.MethodsWe investigated motor and non-motor symptoms (Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part 1 and 2 items) in 64 prodromal PD and 422 PD individuals calculating the odds ratios, adjusting for age and gender, for PD and prodromal PD versus 195 healthy controls. Symptoms at high specificity were defined as having an adjusted odds ratio ≥ 6.ResultsConstipation had an adjusted odds ratio, 6.14 [95% CI: 2.94–12.80] showing high specificity for prodromal PD, and speech difficulties had an adjusted odds ratio, 9.61 [95% CI: 7.88–48.81] showing high specificity for PD. The proportion of participants showing these specific markers was moderate (e.g., prevalence of constipation was 43.75% in prodromal PD, and speech difficulties was 33.89% in PD), suggesting these symptoms may make robust predictors of prodromal PD and PD, respectively.DiscussionClinical markers at high specificity for developing PD could be used as tools in the screening of general populations to identify individuals at higher risk of developing PD.

Abstract Background To date, no medication has slowed the progression of Parkinson’s disease (PD). Preclinical, epidemiological, and experimental data on humans all support many benefits of endurance exercise among persons with PD. The key question is whether there is a definitive additional benefit of exercising at high intensity, in terms of slowing disease progression, beyond the well-documented benefit of endurance training on a treadmill for fitness, gait, and functional mobility. This study will determine the efficacy of high-intensity endurance exercise as first-line therapy for persons diagnosed with PD within 3 years, and untreated with symptomatic therapy at baseline. Methods This is a multicenter, randomized, evaluator-blinded study of endurance exercise training. The exercise intervention will be delivered by treadmill at 2 doses over 18 months: moderate intensity (4 days/week for 30 min per session at 60–65% maximum heart rate) and high intensity (4 days/week for 30 min per session at 80–85% maximum heart rate). We will randomize 370 participants and follow them at multiple time points for 24 months. The primary outcome is the Movement Disorders Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor score (Part III) with the primary analysis assessing the change in MDS-UPDRS motor score (Part III) over 12 months, or until initiation of symptomatic antiparkinsonian treatment if before 12 months. Secondary outcomes are striatal dopamine transporter binding, 6-min walk distance, number of daily steps, cognitive function, physical fitness, quality of life, time to initiate dopaminergic medication, circulating levels of C-reactive protein (CRP), and brain-derived neurotrophic factor (BDNF). Tertiary outcomes are walking stride length and turning velocity. Discussion SPARX3 is a Phase 3 clinical trial designed to determine the efficacy of high-intensity, endurance treadmill exercise to slow the progression of PD as measured by the MDS-UPDRS motor score. Establishing whether high-intensity endurance treadmill exercise can slow the progression of PD would mark a significant breakthrough in treating PD. It would have a meaningful impact on the quality of life of people with PD, their caregivers and public health. Trial registration ClinicalTrials.gov NCT04284436 . Registered on February 25, 2020.

Abstract The Movement Disorder Society revised version of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS) parts 2 and 3 reflect patient‐reported functional impact and clinician‐reported severity of motor signs of Parkinson’s disease (PD), respectively. Total scores are common clinical outcomes but may obscure important time‐based changes in items. We aim to analyze longitudinal disease progression based on MDS‐UPRDS parts 2 and 3 item‐level responses over time and as functions of Hoehn & Yahr (H&Y) stages 1 and 2 for subjects with early PD. The longitudinal item response theory (IRT) modeling is a novel statistical method addressing limitations in traditional linear regression approaches, such as ignoring varying item sensitivities and the sum score balancing out improvements and declines. We utilized a harmonized dataset consisting of six studies with 3573 subjects with early PD and 14,904 visits, and mean follow‐up time of 2.5 years (±1.57). We applied both a unidimensional (each part separately) and multidimensional (both parts combined) longitudinal IRT models. We assessed the progression rates for both parts, anchored to baseline H&Y stages 1 and 2. Both the uni‐ and multidimensional longitudinal IRT models indicate significant worsening time effects in both parts 2 and 3. Baseline H&Y stage 2 was associated with significantly higher baseline severities, but slower progression rates in both parts, as compared with stage 1. Patients with baseline H&Y stage 1 demonstrated slower progression in part 2 severity compared to part 3, whereas patients with baseline H&Y stage 2 progressed faster in part 2 than part 3. The multidimensional model had a superior fit compared to the unidimensional models and it had excellent model performance.

Abstract We examined 2-year longitudinal change in clinical features and biomarkers in LRRK2 non-manifesting carriers (NMCs) versus healthy controls (HCs) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). We analyzed 2-year longitudinal data from 176 LRRK2 G2019S NMCs and 185 HCs. All participants were assessed annually with comprehensive motor and non-motor scales, dopamine transporter (DAT) imaging, and biofluid biomarkers. The latter included cerebrospinal fluid (CSF) Abeta, total tau and phospho-tau; serum urate and neurofilament light chain (NfL); and urine bis(monoacylglycerol) phosphate (BMP). At baseline, LRRK2 G2019S NMCs had a mean (SD) age of 62 (7.7) years and were 56% female. 13% had DAT deficit (defined as

Abstract Objectives Isradipine is a dihydropyridine calcium channel inhibitor that has demonstrated concentration‐dependent neuroprotective effects in animal models of Parkinson’s disease (PD) but failed to show efficacy in a phase 3 clinical trial. The objectives of this study were to model the plasma pharmacokinetics of isradipine in study participants from the phase 3 trial; and, to investigate associations between drug exposure and longitudinal clinical outcome measures of PD progression. Methods Plasma samples from nearly all study participants randomized to immediate‐release isradipine 5‐mg twice daily (166 of 170) were collected for population pharmacokinetic modeling. Estimates of isradipine exposure included apparent oral clearance and area under the concentration‐time curve. Isradipine exposure parameters were tested for correlations with 36‐month changes in disease severity clinical assessment scores, and time‐to‐event analyses for initiation of antiparkinson therapy. Results Isradipine exposures did not correlate with the primary clinical outcome, changes in the antiparkinson therapy‐adjusted Unified Parkinson’s Disease Rating Scale parts I–III score over 36 months (Spearman rank correlation coefficient, rs: 0.09, P = 0.23). Cumulative levodopa equivalent dose at month 36 was weakly correlated with isradipine plasma clearance (rs: 0.18, P = 0.035). This correlation was sex dependent and significant in males, but not females. Those with higher isradipine exposure had decreased risk of needing antiparkinson treatment over 36 months compared with placebo (hazard ratio: 0.87, 95% CI: 0.78–0.98, P = 0.02). Interpretation In this clinical trial, higher isradipine plasma exposure did not affect clinical assessment measures of PD severity but modestly decreased cumulative levodopa equivalent dose and the time needed for antiparkinson treatment initiation. Trial Registration ClinicalTrials.gov NCT02168842.

Abstract Objective The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site‐centric therapeutic trials in Parkinson’s disease and generate novel insights. The value of this approach has never been more evident than during the current COVID‐19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow‐up of clinical trial participants, (2) compare changes in smartphone‐based assessments, online patient‐reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson’s disease disability and progression. Methods Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele‐Health Outcomes in Multiyear Extensions of Parkinson’s Disease trials (AT‐HOME PD), a two‐year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone‐based assessments of motor function and socialization, and patient‐reported outcomes online. Results From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study’s smartphone application and 161 (71%) have completed patient‐reported outcomes on the online platform. Interpretation It is feasible to conduct a large‐scale, international virtual observational study following the completion of participation in brick‐and‐mortar clinical trials in Parkinson’s disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow‐up of clinical trial participants and design of future clinical trials.

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

Abstract Objective Reduction in glucocerebrosidase (GCase; encoded by GBA) enzymatic activity has been linked to Parkinson’s disease (PD). Here, we correlated GCase activity and PD phenotype in the Parkinson’s Progression Markers Initiative (PPMI) cohort. Methods We measured GCase activity in dried blood spots from 1559 samples of participants in the inception PPMI cohort, collected in four annual visits (from baseline visit to Year‐3). Participants (PD, n = 392; controls, n = 175) were fully sequenced for GBA variants by means of genome‐wide genotyping arrays, whole‐exome sequencing, whole‐genome sequencing, Sanger sequencing, and RNA‐sequencing. Results Fifty‐two PD participants (13.4%) and 13 (7.4%) controls carried a GBA variant. GBA status was strongly associated with GCase activity. Among noncarriers, GCase activity was similar between PD and controls. Among GBA p.E326K carriers (PD, n = 20; controls, n = 5), activity was significantly lower in PD carriers than control carriers (9.53 µmol/L/h vs. 11.68 µmol/L/h, P = 0.035). Glucocerebrosidase activity was moderately (r = 0.45) associated with white blood cell (WBC) count. Next, we divided the noncarriers with PD to tertiles based on WBC count‐corrected enzymatic activity. Members of the lower tertile had higher MDS‐Unified Parkinson’s Disease Rating Scale motor score in the “off” medication examination at year‐III exam. Longitudinal analyses demonstrated slight reduction of activity in samples collected earlier on in the study, likely because of longer storage time. Interpretation GCase activity is associated with GBA genotype, WBC count, and among p.E326K variant carriers, with PD status. Reduced activity may also be associated with worse phenotype but longer follow up is required to confirm this observation.

Abstract Objective To determine the evolution of numerous neuropsychiatric symptoms and cognitive abilities in Parkinson disease from disease onset. Methods Prospectively collected, longitudinal (untreated, disease onset to year 5), observational data from Parkinson's Progression Markers Initiative annual visits was used to evaluate prevalence, correlates, and treatment of 10 neuropsychiatric symptoms and cognitive impairment in Parkinson disease participants and matched healthy controls. Results Of 423 Parkinson disease participants evaluated at baseline, 315 (74.5%) were assessed at year 5. Eight neuropsychiatric symptoms studied increased in absolute prevalence by 6.2–20.9% at year 5 relative to baseline, and cognitive impairment increased by 2.7–6.2%. In comparison, the frequency of neuropsychiatric symptoms in healthy controls remained stable or declined over time. Antidepressant and anxiolytic/hypnotic use in Parkinson disease were common at baseline and increased over time (18% to 27% for the former; 13% to 24% for the latter); antipsychotic and cognitive‐enhancing medication use was uncommon throughout (2% and 5% of patients at year 5); and potentially harmful anticholinergic medication use was common and increased over time. At year 5 the cross‐sectional prevalence for having three or more neuropsychiatric disorders/cognitive impairment was 56% for Parkinson disease participants versus 13% for healthy controls, and by then seven of the examined disorders had either occurred or been treated at some time point in the majority of Parkinson disease patients. Principal component analysis suggested an affective disorder subtype only. Interpretation Neuropsychiatric features in Parkinson disease are common from the onset, increase over time, are frequently comorbid, and fluctuate in severity.

Abstract Background Parkinson’s disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set. Conclusions Our findings demonstrate the ability to simplify measurement characteristics from body-worn sensors while maintaining performance in PD symptom detection. Understanding the trade-off between model performance and data resolution is crucial to design efficient, accurate wearable sensing systems. This approach may improve the feasibility of long-term, continuous, and real-time monitoring of PD symptoms by reducing computational burden on wearable devices.

Advanced diffusion imaging which accounts for complex tissue properties, such as crossing fibers and extracellular fluid, may detect longitudinal changes in widespread pathology in atypical Parkinsonian syndromes. We implemented fixel-based analysis, Neurite Orientation and Density Imaging (NODDI), and free-water imaging in Parkinson’s disease (PD), multiple system atrophy (MSAp), progressive supranuclear palsy (PSP), and controls longitudinally over one year. Further, we used these three advanced diffusion imaging techniques to investigate longitudinal progression-related effects in key white matter tracts and gray matter regions in PD and two common atypical Parkinsonian disorders. Fixel-based analysis and free-water imaging revealed longitudinal declines in a greater number of descending sensorimotor tracts in MSAp and PSP compared to PD. In contrast, only the primary motor descending sensorimotor tract had progressive decline over one year, measured by fiber density (FD), in PD compared to that in controls. PSP was characterized by longitudinal impairment in multiple transcallosal tracts (primary motor, dorsal and ventral premotor, pre-supplementary motor, and supplementary motor area) as measured by FD, whereas there were no transcallosal tracts with longitudinal FD impairment in MSAp and PD. In addition, free-water (FW) and FW-corrected fractional anisotropy (FAt) in gray matter regions showed longitudinal changes over one year in regions that have previously shown cross-sectional impairment in MSAp (putamen) and PSP (substantia nigra, putamen, subthalamic nucleus, red nucleus, and pedunculopontine nucleus). NODDI did not detect any longitudinal white matter tract progression effects and there were few effects in gray matter regions across Parkinsonian disorders. All three imaging methods were associated with change in clinical disease severity across all three Parkinsonian syndromes. These results identify novel extra-nigral and extra-striatal longitudinal progression effects in atypical Parkinsonian disorders through the application of multiple diffusion methods that are related to clinical disease progression. Moreover, the findings suggest that fixel-based analysis and free-water imaging are both particularly sensitive to these longitudinal changes in atypical Parkinsonian disorders.

Currently, no treatments available for Parkinson’s disease (PD) can slow PD progression. At the early stage of the disease, only a subset of individuals with PD progress quickly, while the majority have a slowly progressive form of the disease. In developing treatments that aim to slow PD progression, clinical trials aim to include individuals who are likely to progress faster, such that a treatment effect, if one exists, can be identified easier and earlier. The aim of the present study was to identify baseline predictors of clinical progression in early PD. We analyzed 12-month data acquired from the PASADENA trial Part 1 (NCT03100149, n = 76 participants who were allocated to the placebo arm and did not start symptomatic therapy) and the Parkinson’s Progression Markers Initiative (PPMI) study (n = 139 demographically and clinically matched participants). By using ridge regression models including clinical characteristics, imaging, and non-imaging biomarkers, we found that Hoehn and Yahr stage and dopamine transporter single-photon emission computed tomography specific binding ratios (Dat-SPECT SBR) in putamen ipsilateral to the side of motor symptom onset predicted PD progression at the early stage of the disease. Further studies are needed to confirm the validity of these predictors to identify with high accuracy individuals with early PD with a faster progression phenotype.

Background: Currently available treatments for Parkinson's disease (PD) do not slow clinical progression nor target alpha-synuclein, a key protein associated with the disease.Objective: The study objective was to evaluate the efficacy and safety of prasinezumab, a humanized monoclonal antibody that binds aggregated alpha-synuclein, in individuals with early PD.Methods: The PASADENA study is a multicenter, randomized, double-blind, placebo-controlled treatment study. Individuals with early PD, recruited across the US and Europe, received monthly intravenous doses of prasinezumab (1,500 or 4,500 mg) or placebo for a 52-week period (Part 1), followed by a 52-week extension (Part 2) in which all participants received active treatment. Key inclusion criteria were: aged 40–80 years; Hoehn & Yahr (H&Y) Stage I or II; time from diagnosis ≤2 years; having bradykinesia plus one other cardinal sign of PD (e.g., resting tremor, rigidity); DAT-SPECT imaging consistent with PD; and either treatment naïve or on a stable monoamine oxidase B (MAO-B) inhibitor dose. Study design assumptions for sample size and study duration were built using a patient cohort from the Parkinson's Progression Marker Initiative (PPMI). In this report, baseline characteristics are compared between the treatment-naïve and MAO-B inhibitor-treated PASADENA cohorts and between the PASADENA and PPMI populations.Results: Of the 443 patients screened, 316 were enrolled into the PASADENA study between June 2017 and November 2018, with an average age of 59.9 years and 67.4% being male. Mean time from diagnosis at baseline was 10.11 months, with 75.3% in H&Y Stage II. Baseline motor and non-motor symptoms (assessed using Movement Disorder Society—Unified Parkinson's Disease Rating Scale [MDS-UPDRS]) were similar in severity between the MAO-B inhibitor-treated and treatment-naïve PASADENA cohorts (MDS-UPDRS sum of Parts I + II + III [standard deviation (SD)]; 30.21 [11.96], 32.10 [13.20], respectively). The overall PASADENA population (63.6% treatment naïve and 36.4% on MAO-B inhibitor) showed a similar severity in MDS-UPDRS scores (e.g., MDS-UPDRS sum of Parts I + II + III [SD]; 31.41 [12.78], 32.63 [13.04], respectively) to the PPMI cohort (all treatment naïve).Conclusions: The PASADENA study population is suitable to investigate the potential of prasinezumab to slow disease progression in individuals with early PD.Trial Registration: NCT03100149.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these “walk-like” events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Objective: To describe the characteristics of people with Parkinson disease and movement disorders (PDMDs) referred by neurologist to a physiatrist-led interdisciplinary rehabilitation screening program. Design: Retrospective data analysis of electronic health records (EMRs). Setting: Outpatient PDMD neurology clinic and an interdisciplinary rehabilitation hospital’s PDMD screening program. Participants: People with PDMDs referred by neurologists to the interdisciplinary rehabilitation screening program from 2009-2017 (n=934), with early referrals from 2009-2015 (n=449) and recently referred from 2015-2017 (n=485), and patients who had new interdisciplinary rehabilitation screening program evaluations from 2015-2017 (n=183). Intervention: Participation in the physiatrist-led PDMD screening clinic. Main Outcome Measures: Demographics, disease-related features, timed Up and Go, conversational voice volume, recommended therapy services, and number of therapies completed 90 days following interdisciplinary rehabilitation screening program. Results: People referred from the neurologists to the interdisciplinary rehabilitation screening program from 2009-2017 were 72±12.9 years old, male (56%), white (65%), and with 1 or more comorbidities (62%). Compared with early referrals from 2009-2015, more recently referred participants from 2015-2017 were younger (P

Summary: Background: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach. Methods: We did an international study at 17 MRI centres in Austria, Germany, and the USA. We used diffusion-weighted MRI from 1002 patients and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions. Findings: In the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease vs atypical parkinsonism had an AUC 0·962; multiple system atrophy vs progressive supranuclear palsy AUC 0·897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease vs atypical parkinsonism AUC 0·955; multiple system atrophy vs progressive supranuclear palsy AUC 0·926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease vs atypical parkinsonism 0·775; multiple system atrophy vs progressive supranuclear palsy 0·582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods. Interpretations: This study provides an objective, validated, and generalisable imaging approach to distinguish different forms of parkinsonian syndromes using multisite diffusion-weighted MRI cohorts. The diffusion-weighted MRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 min across 3T scanners worldwide. The use of this test could positively affect the clinical care of patients with Parkinson's disease and parkinsonism and reduce the number of misdiagnosed cases in clinical trials. Funding: National Institutes of Health and Parkinson's Foundation.

INTRODUCTION:Several characteristics associated with increased risk for Parkinson's disease (PD) have been identified, including specific genotypes and various non-motor symptoms. Characterizing non-motor features, such as cognitive abilities, among individuals considered at-risk for PD is essential to improving prediction of future neurodegeneration. METHODS:Participants belonging to the following cohorts of the Parkinson Progression Markers Initiative (PPMI) study were included: de novo PD with dopamine transporter binding deficit (n = 423), idiopathic REM sleep behavior disorder (RBD, n = 39), hyposmia (n = 26) and non-PD mutation carrier (NMC; Leucine-rich repeat kinase 2 (LRRK2) G2019S (n = 88) and glucocerebrosidase (GBA) gene (n = 38) mutations)). Inclusion criteria enriched the RBD and hyposmia cohorts, but not the NMC cohort, with individuals with dopamine transporter binding deficit. Baseline neuropsychological performance was compared, and analyses were adjusted for age, sex, education, and depression. RESULTS:The RBD cohort performed significantly worse than the hyposmia and NMC cohorts on Symbol Digit Modality Test (mean (SD) 32.4 (9.16) vs. 41.8 (9.98), p = 0.002 and vs. 45.2 (10.9), p