The ALSFRS-R Summit: a global call to action on the use of the ALSFRS-R in ALS clinical trials.

The Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) was developed more than 25 years ago as an instrument to monitor functional change over time in patients with ALS. It has since been revised and extended to meet the needs of high data quality in ALS trials (ALSFRS-R), however a full re-validation of the scale was not completed. Despite this, the scale has remained a primary outcome measure in clinical trials. We convened a group of clinical trialists to discuss and explore opportunities to improve the scale and propose alternative measures. In this meeting report, we present a call to action on the use of the ALSFRS-Revised scale in clinical trials, focusing on the need for (1) harmonization of the ALSFRS-R administration globally, (2) alignment on a set of recommendations for clinical trial design and statistical analysis plans (SAPs), and (3) use of additional outcome measures.

Cinpanemab in Early Parkinson Disease: Evaluation of Biomarker Results From the Phase 2 SPARK Clinical Trial.

BACKGROUND AND OBJECTIVES: Sensitive, reliable, and scalable biomarkers are needed to accelerate the development of therapies for Parkinson disease (PD). In this study, we evaluate the biomarkers of early PD diagnosis, disease progression, and treatment effect collected in the SPARK. METHODS: Cinpanemab is a human-derived monoclonal antibody binding preferentially to aggregated forms of extracellular α-synuclein. SPARK was a randomized, double-blind, placebo-controlled, phase 2 multicenter trial evaluating 3 cinpanemab doses administered intravenously every 4 weeks for 52 weeks with an active treatment dose-blind extension period for up to 112 weeks. SPARK enrolled 357 participants diagnosed with PD within 3 years, aged 40-80 years, ≤2.5 on the modified Hoehn and Yahr scale, and with evidence of striatal dopaminergic deficit. The primary outcome was change from baseline in the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale total score. Secondary and exploratory biomarker outcomes evaluated change from baseline at week 52 relative to placebo. Dopamine transporter SPECT and MRI were used to quantify changes in the nigrostriatal dopamine pathway and regional atrophy. CSF and plasma samples were used to assess change in total α-synuclein levels, α-synuclein seeding, and neurofilament light chain levels. SPARK was conducted from January 2018 to April 2021 and terminated due to lack of efficacy. RESULTS: Approximately 3.8% (15/398) of SPECT-imaged participants did not have evidence of dopaminergic deficit and were screen-failed. Binary classification of α-synuclein seeding designated 93% (110/118) of the enrolled CSF subgroup as positive for α-synuclein seeds at baseline. Clinical disease progression was observed, with no statistically significant difference in cinpanemab groups compared with that in placebo. Ninety-nine percent of participants with positive α-synuclein seeding remained positive through week 52. No statistically significant changes from baseline were observed between treatment groups and placebo across biomarker measures. Broadly, there was minimal annual change with high interindividual variability across biomarkers-with striatal binding ratios of the ipsilateral putamen showing the greatest mean change/SD over time. DISCUSSION: Biomarker results indicated enrollment of the intended population with early PD, but there was no significant correlation with disease progression or clear evidence of a cinpanemab treatment effect on biomarker measures. Suitable biomarkers for evaluating disease severity and progression in early PD trials are still needed. TRIAL REGISTRATION INFORMATION: NCT03318523 (clinicaltrials.gov/ct2/show/NCT03318523); Submitted October 24, 2017; First patient enrolled January 2018.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease.

BACKGROUND: Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. OBJECTIVES: The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. METHODS: Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. RESULTS: Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]). CONCLUSIONS: Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Relative Meaningfulness and Impacts of Symptoms in People with Early-Stage Parkinson's Disease.

BACKGROUND: Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies. OBJECTIVE: To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important. METHODS: Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why. Individual symptom maps were coded for types, frequencies, and bothersomeness of symptoms and their impacts, with thematic analysis of narratives to explore perceptions. RESULTS: The three most bothersome and important symptoms were tremor, fine motor difficulties, and slow movements. Symptoms had the greatest impact on sleep, job functioning, exercise, communication, relationships, and self-concept- commonly expressed as a sense of being limited by PD. Thematically, most bothersome symptoms were those that were personally limiting with broadest negative impact on well-being and activities. However, symptoms could be important to patients even when not present or limiting (e.g., speech, cognition). CONCLUSION: Meaningful symptoms of early PD can include symptoms that are present or anticipated future symptoms that are important to the individual. Systematic assessment of meaningful symptoms should aim to assess the extent to which symptoms are personally important, present, bothersome, and limiting.

Mapping Relevance of Digital Measures to Meaningful Symptoms and Impacts in Early Parkinson's Disease.

BACKGROUND: Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease. OBJECTIVE: This study evaluated of relevance of WATCH-PD digital measures to monitoring meaningful symptoms and impacts of early Parkinson's disease from the patient perspective. METHODS: Participants with early Parkinson's disease (N = 40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data. RESULTS: Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant. CONCLUSION: Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.

The Influence of GBA and LRRK2 on Mood Disorders in Parkinson's Disease.

Background: Mood disorders have emerged as major non-motor comorbidities in Parkinson's disease (PD) even at the prodromal stage of the disease. Mutations in the LRRK2 and GBA genes are common among Ashkenazi Jews, with more severe phenotype reported for GBA-PD. Objective: To explore the association between genetic status and mood related disorders before and after diagnosis of PD and the association between mood-related medications, phenotype, and genetic status. Methods: Participants were genotyped for mutations in the LRRK2 and GBA genes. State of depression, anxiety and non-motor features were evaluated using validated questionnaires. History of mood disorders prior to diagnosis of PD and use of mood-related medications were assessed. Results: The study included 105 idiopathic PD (iPD), 55 LRRK2-PD and 94 GBA-PD. Scores on mood related questionnaires and frequency of depression and anxiety before diagnosis were similar between the groups (p>0.05). However, more GBA-PD patients used mood related medications before PD diagnosis than LRRK2-PD and iPD (16.5% vs 7.1% and 8.2%, p=0.044). LRRK2-PD and GBA-PD receiving mood-related medications at time of assessment had worse motor and non-motor phenotype compared to those that did not (p<0.05). LRRK2-PD receiving mood related-medications at time of assessment, scored higher on mood-related questionnaires compared to LRRK2-PD not receiving such medications (p<0.04). Conclusions: Prodromal GBA-PD are more frequently treated with mood related-medications despite equal rates of reported mood-related disorders, while LRRK2-PD with mood-related disorders experience high rates of anxiety and depression despite treatment, attesting to the need of more precise assessment and treatment of these genetic subgroups.

A qualitative evaluation of the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R) by the patient community: a web-based cross-sectional survey.

OBJECTIVE: The revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) is the most commonly used outcome measure in ALS studies. The aim of this study was to identify potential limitations of the ALSFRS-R from the perspective of people living with ALS and their caregivers. METHODS: A web-based survey was developed by investigators, people living with ALS, and their caregivers, and shared across social media. For each item, participants were asked, "Can you think of a situation where you might not be able to answer this item accurately or that your answer might not reflect your abilities?" Responses were divided into two categories: criticisms that could be addressed in a manual or issues with the items/responses that would require measure modification. RESULTS: 57 participants (72% participants with ALS, 28% caregivers) responded to at least one item question, of which 71.9% expressed concern about at least one item. The most frequently identified items were speech, walking, and cutting food. Common criticisms were: language used is of a medical literacy level too high; item is situational; difficult to distinguish the difference between response choices; and the structure and/or underlying assumptions of the item makes it difficult to answer. CONCLUSIONS: Several items of the ALSFRS-R were considered to inaccurately reflect the abilities of patients with ALS. The ALSFRS-R may need a revision to address these issues, preferably in co-development with people living with ALS and their caregivers, and/or alternate outcome measures should be considered for patients with ALS.

Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson's disease.

MRI was suggested as a promising method for the diagnosis and assessment of Parkinson's Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T2* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T2* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T2* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can't be performed. Combining neuromelanin and T2*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.

Trial of Cinpanemab in Early Parkinson's Disease.

BACKGROUND: Aggregated α-synuclein plays an important role in Parkinson's disease pathogenesis. Cinpanemab, a human-derived monoclonal antibody that binds to α-synuclein, is being evaluated as a disease-modifying treatment for Parkinson's disease. METHODS: In a 52-week, multicenter, double-blind, phase 2 trial, we randomly assigned, in a 2:1:2:2 ratio, participants with early Parkinson's disease to receive intravenous infusions of placebo (control) or cinpanemab at a dose of 250 mg, 1250 mg, or 3500 mg every 4 weeks, followed by an active-treatment dose-blinded extension period for up to 112 weeks. The primary end points were the changes from baseline in the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score (range, 0 to 236, with higher scores indicating worse performance) at weeks 52 and 72. Secondary end points included MDS-UPDRS subscale scores and striatal binding as assessed on dopamine transporter single-photon-emission computed tomography (DaT-SPECT). RESULTS: Of the 357 enrolled participants, 100 were assigned to the control group, 55 to the 250-mg cinpanemab group, 102 to the 1250-mg group, and 100 to the 3500-mg group. The trial was stopped after the week 72 interim analysis owing to lack of efficacy. The change to week 52 in the MDS-UPDRS score was 10.8 points in the control group, 10.5 points in the 250-mg group, 11.3 points in the 1250-mg group, and 10.9 points in the 3500-mg group (adjusted mean difference vs. control, -0.3 points [95% confidence interval {CI}, -4.9 to 4.3], P = 0.90; 0.5 points [95% CI, -3.3 to 4.3], P = 0.80; and 0.1 point [95% CI, -3.8 to 4.0], P = 0.97, respectively). The adjusted mean difference at 72 weeks between participants who received cinpanemab through 72 weeks and the pooled group of those who started cinpanemab at 52 weeks was -0.9 points (95% CI, -5.6 to 3.8) for the 250-mg dose, 0.6 points (95% CI, -3.3 to 4.4) for the 1250-mg dose, and -0.8 points (95% CI, -4.6 to 3.0) for the 3500-mg dose. Results for secondary end points were similar to those for the primary end points. DaT-SPECT imaging at week 52 showed no differences between the control group and any cinpanemab group. The most common adverse events with cinpanemab were headache, nasopharyngitis, and falls. CONCLUSIONS: In participants with early Parkinson's disease, the effects of cinpanemab on clinical measures of disease progression and changes in DaT-SPECT imaging did not differ from those of placebo over a 52-week period. (Funded by Biogen; SPARK ClinicalTrials.gov number, NCT03318523.).

Application of longitudinal item response theory models to modeling Parkinson's disease progression.

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.

Tracking Emergence of New Motor and Non-Motor Symptoms Using the MDS-UPDRS: A Novel Outcome Measure for Early Parkinson's Disease?

BACKGROUND: Summary scores of current clinical rating scales do not appear sensitive enough to quantify changes in disease progression in early Parkinson's disease (PD) clinical trials. An alternate approach might be to track the appearance of new or emergent symptoms (ES) over time as a measure of disease progression. OBJECTIVE: Explore the potential utility of patient reported ES as an outcome measure during the early phase of PD. METHODS: We analyzed data from the MDS-UPDRS Parts IB (non-motor) and II (motor) Experiences of Daily Living scales over two years in the STEADY-PD3 study. We assessed the number of ES reported in each part of the scale in both participants who started symptomatic treatment and those who did not (STx-yes/no) in two periods: between 0 and 12-months (Year 1), and 13 and 24-months (Year 2). RESULTS: Of 331 participants, 87% developed ES, and 55% started STx in Year 1. The median number of Part IB ES did not significantly differ between STx groups, but ES in Part II were significantly more frequent in the STx-yes group. Of 148 participants who remained STx-no into Year 2, 77% developed ES, and 42% started STx. Again, Part II, but not Part IB ES were more frequent the STx-yes group. Using these results, a sample size of ∼90 per group would be required to detect a 30% reduction in combined Part IB and II ES over 12 months. CONCLUSION: Assessing ES of patient-reported experiences of daily living may provide a useful marker for tracking PD progression.

Aberrant dopamine transporter and functional connectivity patterns in LRRK2 and GBA mutation carriers.

Non-manifesting carriers (NMCs) of Parkinson's disease (PD)-related mutations such as LRRK2 and GBA are at an increased risk for developing PD. Dopamine transporter (DaT)-spectral positron emission computed tomography is widely used for capturing functional nigrostriatal dopaminergic activity. However, it does not reflect other ongoing neuronal processes; especially in the prodromal stages of the disease. Resting-state fMRI (rs-fMRI) has been proposed as a mode for assessing functional alterations associated with PD, but its relation to dopaminergic deficiency remains unclear. We aimed to study the association between presynaptic striatal dopamine uptake and functional connectivity (FC) patterns among healthy first-degree relatives of PD patients with mutations in LRRK2 and GBA genes. N = 85 healthy first-degree subjects were enrolled and genotyped. All participants underwent DaT and rs-fMRI scans, as well as a comprehensive clinical assessment battery. Between-group differences in FC within striatal regions were investigated and compared with striatal binding ratios (SBR). N = 26 GBA-NMCs, N = 25 LRRK2-NMCs, and N = 34 age-matched nonmanifesting noncarriers (NM-NCs) were included in each study group based on genetic status. While genetically-defined groups were similar across clinical measures, LRRK2-NMCs demonstrated lower SBR in the right putamen compared with NM-NCs, and higher right putamen FC compared to GBA-NMCs. In this group, higher striatal FC was associated with increased risk for PD. The observed differential SBR and FC patterns among LRRK2-NMCs and GBA-NMCs indicate that DaTscan and FC assessments might offer a more sensitive prediction of the risk for PD in the pre-clinical stages of the disease.

Considerations to address missing data when deriving clinical trial endpoints from digital health technologies.

Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspectives in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.

Glucocerebrosidase Activity is not Associated with Parkinson's Disease Risk or Severity.

BACKGROUND: Mutations in the GBA gene, which encodes the lysosomal enzyme glucocerebrosidase (GCase), are risk factors for Parkinson's disease (PD). OBJECTIVE: To explore the association between GCase activity, PD phenotype, and probability for prodromal PD among carriers of mutations in the GBA and LRRK2 genes. METHODS: Participants were genotyped for the G2019S-LRRK2 and nine GBA mutations common in Ashkenazi Jews. Performance-based measures enabling the calculation of the Movement Disorder Society (MDS) prodromal probability score were collected. RESULTS: One hundred and seventy PD patients (102 GBA-PD, 38 LRRK2-PD, and 30 idiopathic PD) and 221 non-manifesting carriers (NMC) (129 GBA-NMC, 45 LRRK2-NMC, 15 GBA-LRRK2-NMC, and 32 healthy controls) participated in this study. GCase activity was lower among GBA-PD (3.15 ± 0.85 µmol/L/h), GBA-NMC (3.23 ± 0.91 µmol/L/h), and GBA-LRRK2-NMC (3.20 ± 0.93 µmol/L/h) compared to the other groups of participants, with no correlation to clinical phenotype. CONCLUSIONS: Low GCase activity does not explain the clinical phenotype or risk for prodromal PD in this cohort. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Evaluating dopamine transporter imaging as an enrichment biomarker in a phase 2 Parkinson's disease trial.

BACKGROUND: Dopamine transporter single-photon emission computed tomography (DaT-SPECT) can quantify the functional integrity of the dopaminergic nerve terminals and has been suggested as an imaging modality to verify the clinical diagnosis of Parkinson's disease (PD). Depending on the stage of progression, approximately 5-15% of participants clinically diagnosed with idiopathic PD have been observed in previous studies to have normal DaT-SPECT patterns. However, the utility of DaT-SPECT in enhancing early PD participant selection in a global, multicenter clinical trial of a potentially disease-modifying therapy is not well understood. METHODS: The SPARK clinical trial was a phase 2 trial of cinpanemab, a monoclonal antibody against alpha-synuclein, in participants with early PD. DaT-SPECT was performed at screening to select participants with DaT-SPECT patterns consistent with degenerative parkinsonism. Acquisition was harmonised across 82 sites. Images were reconstructed and qualitatively read at a central laboratory by blinded neuroradiologists for inclusion prior to automated quantitative analysis. RESULTS: In total, 482 unique participants were screened between January 2018 and May 2019; 3.8% (15/398) of imaged participants were excluded owing to negative DaT-SPECT findings (i.e., scans without evidence of dopaminergic deficit [SWEDD]). CONCLUSION: A smaller proportion of SPARK participants were excluded owing to SWEDD status upon DaT-SPECT screening than has been reported in prior studies. Further research is needed to understand the reasons for the low SWEDD rate in this study and whether these results are generalisable to future studies. If supported, the radiation risks, imaging costs, and operational burden of DaT-SPECT for enrichment may be mitigated by clinical assessment and other study design aspects. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03318523 . Date submitted: October 19, 2017. First Posted: October 24, 2017.

Seeking progress in disease modification in Parkinson disease.

OBJECTIVE: Disease modification in Parkinson disease (PD) has remained an elusive goal, in spite of large investments over several decades. Following a large meeting of experts, this review article discusses the state of the science, possible reasons for past PD trials' failures to demonstrate disease-modifying benefit, and potential solutions. METHODS: The National Institute of Neurological Disorders and Stroke (NINDS) convened a meeting including leaders in the field and representatives of key stakeholder groups to discuss drug therapy with the goal of disease modification in PD. RESULTS: Important lessons can be learned from previous attempts, as well as from other fields. The selection process for therapeutic targets and agents differs among various organizations committed to therapeutic development. The areas identified as critical to target in future research include the development of relevant biomarkers, refinements of the targeted patient populations, considerations of novel trial designs, and improving collaborations between all stakeholders. CONCLUSIONS: We identify potential barriers to progress in disease modification for Parkinson's and propose a set of research priorities that may improve the likelihood of success.

Quantitative digital clock drawing test as a sensitive tool to detect subtle cognitive impairments in early stage Parkinson's disease.

INTRODUCTION: The prevalence of subtle cognitive decline in the early stages of Parkinson's Disease (PD) is common and is thought to be even greater in patients carrying genetic mutations in the GBA gene. Current cognitive tests often lack sensitivity to identify subtle impairments. Technological advancements may offer greater precision. We explored the utility of a digitized cognitive clock-drawing test to assess cognition in patients with PD compared to healthy controls (HC) and its sensitivity compared to that of standardized neuropsychological tests. Further, we investigated the existence of a cognitive profile based on genotype. METHODS: The study included 75 early stage PD patients (24 with GBA-PD, 23 LRRK2-PD, 28 idiopathic PD cases) and 59 HC. Participants underwent a cognitive assessment which included the Montreal Cognitive Assessment (MoCA), the Color Trails Test (CTT) and a digital clock drawing test (DCTclock). RESULTS: Patients with PD presented lower scores than HC on all cognitive tests. The DCTclock best discriminated PD from HC (AUC: 0.807) compared to the MoCA (0.590) and CTT (0.636 and 0.717 for CTT-1 and CTT-2 respectively). In-depth quantitative analysis of the DCTclock revealed that LRRK2-PD showed better performance than other PD sub-groups. CONCLUSION: The use of quantitative digital cognitive assessment showed greater sensitivity in identifying subtle cognitive decline than the current standardized tests. Differences in cognitive profiles were observed based on genotype. The identification of early cognitive decline may improve the clinical management of PD patients and be useful for cognitive related clinical trials.

Mutations in GBA and LRRK2 Are Not Associated with Increased Inflammatory Markers.

BACKGROUND: Inflammation is an integral part of neurodegeneration including in Parkinson's disease (PD). Ashkenazi Jews have high rates of genetic PD with divergent phenotypes among GBA-PD and LRRK2-PD. The role of inflammation in the prodromal phase of PD and the association with disease phenotype has yet to be elucidated. OBJECTIVE: To assess central and peripheral cytokines among PD patients with mutations in the LRRK2 and GBA genes and among non-manifesting carriers (NMC) of these mutations in order to determine the role of inflammation in genetic PD. METHODS: The following cytokines were assessed from peripheral blood and cerebrospinal fluid (CSF): TNF-α, IL-1ß, IL-2, IL-4, IL-6, IL-8, IL-10 and INF- γ. A comprehensive intake including general medical conditions, use of anti-inflammatory treatments, motor and cognitive assessments and additional laboratory measures were recorded, enabling the construction of the MDS probable prodromal score. RESULTS: Data from 362 participants was collected: 31 idiopathic PD (iPD), 30 LRRK2-PD, 77 GBA-PD, 3 homozygote GBA-PD, 3 GBA-LRRK2-PD, 67 LRRK2-NMC, 105 GBA-NMC, 14 LRRK2-GBA-NMC, and 32 healthy controls. No between-group differences in peripheral or CSF cytokines were detected. No correlation between disease characteristics or risk for prodromal PD could be associated with any inflammatory measure. CONCLUSION: In this study, we could not detect any evidence on dysregulated immune response among GBA and LRRK2 PD patients and non-manifesting mutation carriers.

Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning.

BACKGROUND: It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). OBJECTIVE: To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. METHODS: Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. RESULTS: High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages. CONCLUSIONS: Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.