Parkinson's Disease Subtypes in the Oxford Parkinson Disease Centre (OPDC) Discovery Cohort

Investigating trial design variability in trials of disease-modifying therapies in Parkinson's disease: a scoping review protocol

INTRODUCTION

Parkinson's disease (PD) is a debilitating neurological disorder for which the identification of disease-modifying interventions represents a major unmet need. Diverse trial designs have attempted to mitigate challenges of population heterogeneity, efficacious symptomatic therapy and lack of outcome measures that are objective and sensitive to change in a disease modification setting. It is not clear whether consensus is emerging regarding trial design choices. Here, we report the protocol of a scoping review that will provide a contemporary update on trial design variability for disease-modifying interventions in PD.

METHODS AND ANALYSIS

The Population, Intervention, Comparator, Outcome and Study design (PICOS) framework will be used to structure the review, inform study selection and analysis. The databases MEDLINE, Web of Science, Cochrane and the trial registry ClinicalTrials.gov will be systematically searched to identify published studies and registry entries in English. Two independent reviewers will screen study titles, abstracts and full text for eligibility, with disagreements being resolved through discussion or by a third reviewer where necessary. Data on general study information, eligibility criteria, outcome measures, trial design, retention and statistically significant findings will be extracted into a standardised form. Extracted data will be presented in a descriptive analysis. We will report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review extension.

ETHICS AND DISSEMINATION

This work will provide an overview of variation and emerging trends in trial design choices for disease-modifying trials of PD. Due to the nature of this study, there are no ethical or safety considerations. We plan to publish our findings in a peer-reviewed journal.

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring.

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

Disease-modifying vs symptomatic treatments: Splitting over lumping

Clinical trials of putative disease-modifying therapies in neurodegeneration have obeyed the century-old principle of convergence, or lumping, whereby any feature of a clinicopathologic disease entity is considered relevant to most of those affected. While this convergent approach has resulted in important successes in trials of symptomatic therapies, largely aimed at correcting common neurotransmitter deficiencies (e.g., cholinergic deficiency in Alzheimer's disease or dopaminergic deficiency in Parkinson's disease), it has been consistently futile in trials of neuroprotective or disease-modifying interventions. As individuals affected by the same neurodegenerative disorder do not share the same biological drivers, splitting such disease into small molecular/biological subtypes, to match people to therapies most likely to benefit them, is vital in the pursuit of disease modification. We here discuss three paths toward the splitting needed for future successes in precision medicine: (1) encourage the development of aging cohorts agnostic to phenotype in order to enact a biology-to-phenotype direction of biomarker development and validate divergence biomarkers (present in some, absent in most); (2) demand bioassay-based recruitment of subjects into disease-modifying trials of putative neuroprotective interventions in order to match the right therapies to the right recipients; and (3) evaluate promising epidemiologic leads of presumed pathogenetic potential using Mendelian randomization studies before designing the corresponding clinical trials. The reconfiguration of disease-modifying efforts for patients with neurodegenerative disorders will require a paradigm shift from lumping to splitting and from proteinopathy to proteinopenia.

Two-year clinical progression in focal and diffuse subtypes of Parkinson's disease

Heterogeneity in Parkinson's disease (PD) presents a barrier to understanding disease mechanisms and developing new treatments. This challenge may be partially overcome by stratifying patients into clinically meaningful subtypes. A recent subtyping scheme classifies de novo PD patients into three subtypes: mild-motor predominant, intermediate, or diffuse-malignant, based on motor impairment, cognitive function, rapid eye movement sleep behavior disorder (RBD) symptoms, and autonomic symptoms. We aimed to validate this approach in a large longitudinal cohort of early-to-moderate PD (n = 499) by assessing the influence of subtyping on clinical characteristics at baseline and on two-year progression. Compared to mild-motor predominant patients (42%), diffuse-malignant patients (12%) showed involvement of more clinical domains, more diffuse hypokinetic-rigid motor symptoms (decreased lateralization and hand/foot focality), and faster two-year progression. These findings extend the classification of diffuse-malignant and mild-motor predominant subtypes to early-to-moderate PD and suggest that different pathophysiological mechanisms (focal versus diffuse cerebral propagation) may underlie distinct subtype classifications.

Research-ready data: the C-Surv data model

Research-ready data (data curated to a defined standard) increase scientific opportunity and rigour by integrating the data environment. The development of research platforms has highlighted the value of research-ready data, particularly for multi-cohort analyses. Following stakeholder consultation, a standard data model (C-Surv) optimised for data discovery, was developed using data from 5 population and clinical cohort studies. The model uses a four-tier nested structure based on 18 data themes selected according to user behaviour or technology. Standard variable naming conventions are applied to uniquely identify variables within the context of longitudinal studies. The data model was used to develop a harmonised dataset for 11 cohorts. This dataset populated the Cohort Explorer data discovery tool for assessing the feasibility of an analysis prior to making a data access request. Data preparation times were compared between cohort specific data models and C-Surv.It was concluded that adopting a common data model as a data standard for the discovery and analysis of research cohort data offers multiple benefits.

Comprehensive Evaluation of Psychotic Features and Their Clinical Correlates in Early Parkinson's Disease

BACKGROUND

Some reports suggest that psychotic features may occur in the early stages of Parkinson's disease (PD), but sensitive tools have not been utilized.

OBJECTIVE

The aim was to evaluate the presence of psychotic symptoms using detailed scales and to assess the association with clinical characteristics.

METHODS

Healthy controls and patients within three years of PD onset were recruited. Participants were examined for psychotic symptoms using two different instruments: the Comprehensive Assessment of At-Risk Mental States (CAARMS) and a 10 question PD specific psychosis severity scale (10PDQ). In the PD group, medication use, motor and non-motor symptoms were documented.

RESULTS

Based on CAARMS and 10PDQ scales, psychotic features were present in 39% (27/70) of patients and 4% (3/74) of controls. The prevalence of passage hallucinations and illusions was significantly higher in PD compared to the control group. The presence of PD-associated psychotic features was not significantly affected by medication, motor severity or global cognitive status. Higher prevalence of overall non-motor manifestations, REM sleep behavior disorder (RBD) and depressive symptoms was significantly associated with the manifestation of psychotic features in PD [(adjusted OR:1.3; 95% CI:1.1-1.6; p = 0.003), (adjusted OR:1.3; 95% CI:1.0-1.6; p = 0.023), and (adjusted OR:1.2; 95% CI:1.0-1.4;p = 0.026)].

CONCLUSIONS

Psychotic phenomena mainly of minor nature are highly common in early PD. Cumulative non-motor symptoms, RBD and depressive features are associated with the presence of psychotic symptoms in this non-demented, early-stage PD population. More studies are needed to clarify the mechanisms that contribute to the onset of psychotic features in early PD.

An International Multi-Stakeholder Delphi Survey Study on the Design of Disease Modifying Parkinson's Disease Trials

BACKGROUND

Design of disease modification (DM) trials for Parkinson's disease (PD) is challenging. Successful delivery requires a shared understanding of priorities and practicalities.

OBJECTIVE

To seek stakeholder consensus on phase 3 trials' overall goals and structure, inclusion criteria, outcome measures, and trial delivery and understand where perspectives differ.

METHODS

An international expert panel comprising people with Parkinson's (PwP), care partners (CP), clinical scientists, representatives from industry, funders and regulators participated in a survey-based Delphi study. Survey items were informed by a scoping review of DM trials and PwP input. Respondents scored item agreement over 3 rounds. Scores and reasoning were summarized by participant group each round until consensus, defined as≥70% of at least 3 participant groups falling within the same 3-point region of a 9-point Likert scale.

RESULTS

92/121 individuals from 13 countries (46/69 PwP, 13/18 CP, 20/20 clinical scientists, representatives from 8/8 companies, 4/5 funders, and 1/1 regulator) completed the study. Consensus was reached on 14/31 survey items: 5/8 overall goals and structure, 1/8 Eligibility criteria, 7/13 outcome measures, and 1/2 trial delivery items. Extent of stakeholder endorsement for 428 reasons for scores was collated across items.

CONCLUSIONS

This is the first systematic multi-stakeholder consultation generating a unique repository of perspectives on pivotal aspects of DM trial design including those of PwP and CP. The panel endorsed outcomes that holistically measure PD and the importance of inclusive trials with hybrid delivery models. Areas of disagreement will inform mitigating strategies of researchers to ensure successful delivery of future trials.

Precision medicine for Parkinson's disease: The subtyping challenge

Despite many pharmacological and surgical treatments addressing the symptoms of Parkinson's disease, there are no approved treatments that slow disease progression. Genetic discoveries in the last 20 years have increased our understanding of the molecular contributors to Parkinson's pathophysiology, uncovered many druggable targets and pathways, and increased investment in treatments that might slow or stop the disease process. Longitudinal, observational studies are dissecting Parkinson's disease heterogeneity and illuminating the importance of molecularly defined subtypes more likely to respond to targeted interventions. Indeed, clinical and pathological differences seen within and across carriers of PD-associated gene mutations suggest the existence of greater biological complexity than previously appreciated and increase the likelihood that targeted interventions based on molecular characteristics will be beneficial. This article offers our current perspective on the promise and current challenges in subtype identification and precision medicine approaches in Parkinson's disease.

Universal clinical Parkinson's disease axes identify a major influence of neuroinflammation

BACKGROUND

There is large individual variation in both clinical presentation and progression between Parkinson's disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting.

METHODS

Replicating across three large Parkinson's cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson's study (n = 1807) and Parkinson's Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5-10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors ("phenotypic axes").

RESULTS

When applied to disease severity at diagnosis, the most influential of three phenotypic axes "Axis 1" was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer's disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer's disease genetic risk, and in contrast to Parkinson's disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer's disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson's disease genetics did not.

CONCLUSIONS

We identify universal axes of Parkinson's disease phenotypic variation which reveal that Parkinson's patients with high concomitant genetic risk for Alzheimer's disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia.

Exploration of whether socioeconomic factors affect the results of priority setting partnerships: updating the top 10 research priorities for the management of Parkinson's in an international setting

OBJECTIVES

Explore whether socioeconomic differences of patients affect the prioritisation of pre-existing research questions and explore the agreement between healthcare professionals (HCP) and patients in priority setting partnerships (PSPs).

DESIGN AND SETTING

Prospective, three centre survey across UK (400 participants), Tuebingen (176 participants) and Luxembourg (303 participants). People with Parkinson's (PwP), research participants, relatives and HCP associated with three Parkinson's cohort studies were invited to participate, along with linked centres (clinical care settings, research groups, charities). Responders were encouraged to pass on the survey to friends/families/carers.

METHODS

The survey involved rating the importance of research questions on a Likert scale, allowing for the generation of one new question participants felt was particularly important. Collection of demographic information allowed for comparisons of priorities across a range of socioeconomic variables; the top 10 research priorities for each group were then compared. Questions added by participants were subject to a thematic analysis.

RESULTS

879 participants completed the survey (58% PwP, 22% family/friends, 13% HCP, 4% carers). Finding the best form of physiotherapy for PwP was the number one priority across the majority of analyses. HCP were the only subgroup not to place physiotherapy in the top 10. Factors most likely to affect prioritisation in PwP included educational level, presence of carer support and disease duration. There was little difference between other socioeconomic categories.

CONCLUSIONS

Socioeconomic factors modestly influenced some research priority ratings but did not significantly affect the top priority in most comparisons. Future studies must ensure patients from a range of socioeconomic backgrounds are recruited, ensuring results generalisable to the public while also identifying any key disparities in prioritisation. PSP should also take care that HCP do not skew results during prioritisation of questions, as in this study the most important priority to patients was not identified by professionals.

Genetics of validated Parkinson's disease subtypes in the Oxford Discovery and Tracking Parkinson's cohorts

OBJECTIVES

To explore the genetics of four Parkinson's disease (PD) subtypes that have been previously described in two large cohorts of patients with recently diagnosed PD. These subtypes came from a data-driven cluster analysis of phenotypic variables.

METHODS

We looked at the frequency of genetic mutations in glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 against our subtypes. Then we calculated Genetic Risk Scores (GRS) for PD, multiple system atrophy, progressive supranuclear palsy, Lewy body dementia, and Alzheimer's disease. These GRSs were regressed against the probability of belonging to a subtype in the two independent cohorts and we calculated q-values as an adjustment for multiple testing across four subtypes. We also carried out a Genome-Wide Association Study (GWAS) of belonging to a subtype.

RESULTS

A severe disease subtype had the highest rates of patients carrying GBA mutations while the mild disease subtype had the lowest rates (p=0.009). Using the GRS, we found a severe disease subtype had a reduced genetic risk of PD (p=0.004 and q=0.015). In our GWAS no individual variants met genome wide significance (<5×10e-8) although four variants require further follow-up, meeting a threshold of <1×10e-6.

CONCLUSIONS

We have found that four previously defined PD subtypes have different genetic determinants which will help to inform future studies looking at underlying disease mechanisms and pathogenesis in these different subtypes of disease.

Deep phenotyping for precision medicine in Parkinson's disease

A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.

Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering

The second most common progressive neurodegenerative disorder, Parkinson’s disease (PD), is characterized by a broad spectrum of symptoms that are associated with its progression. Several studies have attempted to classify PD according to its clinical manifestations and establish objective biomarkers for early diagnosis and for predicting the prognosis of the disease. Recent comprehensive research on the classification of PD using clinical phenotypes has included factors such as dominance, severity, and prognosis of motor and non-motor symptoms and biomarkers. Additionally, neuroimaging studies have attempted to reveal the pathological substrate for motor symptoms. Genetic and transcriptomic studies have contributed to our understanding of the underlying molecular pathogenic mechanisms and provided a basis for classifying PD. Moreover, an understanding of the heterogeneity of clinical manifestations in PD is required for a personalized medicine approach. Herein, we discuss the possible subtypes of PD based on clinical features, neuroimaging, and biomarkers for developing personalized medicine for PD. In addition, we conduct a preliminary clustering using gait features for subtyping PD. We believe that subtyping may facilitate the development of therapeutic strategies for PD.

A Systematic Review of Parkinson's Disease Cluster Analysis Research

One way to understand the Parkinson’s disease (PD) population is to investigate the similarities and differences among patients through cluster analysis, which may lead to defined, patient subgroups for diagnosis, progression tracking and treatment planning. This paper provides a systematic review of PD patient clustering research, evaluating the variables included in clustering, the cluster methods applied, the resulting patient subgroups, and evaluation metrics. A search was conducted from 1999 to 2021 on the PubMed database, using various search terms including: Parkinson’s disease, cluster, and analysis. The majority of studies included a variety of clinical scale scores for clustering, of which many provide a numerical, but ordinal, categorical value. Even though the scale scores are ordinal, these were treated as numerical values with numerical and continuous values being the focus of the clustering, with limited attention to categorical variables, such as gender and family history, which may also provide useful insights into disease diagnosis, progression, and treatment. The results pointed to two to five patient clusters, with similarities among the age of onset and disease duration. The studies lacked the use of existing clustering evaluation metrics which points to a need for a thorough, analysis framework, and consensus on the appropriate variables to include in cluster analysis. Accurate cluster analysis may assist with determining if PD patients’ symptoms can be treated based on a subgroup of features, if personalized care is required, or if a mix of individualized and group-based care is the best approach.

Clinical classification systems and long-term outcome in mid- and late-stage Parkinson's disease

Parkinson’s disease shows a heterogeneous course and different clinical subtyping systems have been described. To compare the capabilities of two clinical classification systems, motor-phenotypes, and a simplified clinical motor-nonmotor subtyping system, a cohort was included at mean 7.9 ± 5.3 years of disease duration, classified using both clinical systems, and reexamined and reclassified at the end of an observation period. Time-points were retrospectively extracted for five major disease milestones: death, dementia, Hoehn and Yahr stage 5, nursing home living, and walking aid use. Eighty-nine patients were observed for 8.1 ± 2.7 years after inclusion. Dementia developed in 32.9% of the patients and 36.0–67.4% reached the other milestones. Motor-phenotypes were unable to stratify risks during this period, but the worst compared with the more favorable groups in the motor-nonmotor system conveyed hazard ratios between 2.6 and 63.6 for all milestones. A clear separation of risks for dying, living at the nursing home, and reaching motor end-stage was also shown when using only postural instability and gait disorder symptoms, without weighing them against the severity of the tremor. At reexamination, 29.4% and 64.7% of patients had changed classification groups in the motor-phenotype and motor-nonmotor systems, respectively. The motor-nonmotor system thus stratified risks of reaching crucial outcomes in mid–late Parkinson’s disease far better than the well-studied motor-phenotypes. Removing the tremor aspect of motor-phenotypes clearly improved this system, however. Classifications in both systems became unstable over time. The simplification of the motor-nonmotor system was easily applicable and showed potential as a prognostic marker during a large part of Parkinson’s disease.

Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning

BACKGROUND

Parkinson's disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better patient management and improve clinical trial design. Previous approaches to modelling Parkinson's disease progression assumed static progression trajectories within subgroups and have not adequately accounted for complex medication effects. Our objective was to develop a statistical progression model of Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects.

METHODS

In this longitudinal data study, data were collected for up to 7-years on 423 patients with early Parkinson's disease and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI) longitudinal observational study. A contrastive latent variable model was applied followed by a novel personalised input-output hidden Markov model to define disease states. Clinical significance of the states was assessed using statistical tests on seven key motor or cognitive outcomes (mild cognitive impairment, dementia, dyskinesia, presence of motor fluctuations, functional impairment from motor fluctuations, Hoehn and Yahr score, and death) not used in the learning phase. The results were validated in an independent sample of 610 patients with Parkinson's disease from the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP).

FINDINGS

PPMI data were download July 25, 2018, medication information was downloaded on Sept 24, 2018, and PDBP data were downloaded between June 15 and June 24, 2020. The model discovered eight disease states, which are primarily differentiated by functional impairment, tremor, bradykinesia, and neuropsychiatric measures. State 8, the terminal state, had the highest prevalence of key clinical outcomes including 18 (95%) of 19 recorded instances of dementia. At study outset 4 (1%) of 333 patients were in state 8 and 138 (41%) of 333 patients reached stage 8 by year 5. However, the ranking of the starting state did not match the ranking of reaching state 8 within 5 years. Overall, patients starting in state 5 had the shortest time to terminal state (median 2·75 [95% CI 1·75-4·25] years).

INTERPRETATION

We developed a statistical progression model of early Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects. Our predictive model discovered non-sequential, overlapping disease progression trajectories, supporting the use of non-deterministic disease progression models, and suggesting static subtype assignment might be ineffective at capturing the full spectrum of Parkinson's disease progression.

FUNDING

Michael J Fox Foundation.

Digital Progression Biomarkers as Novel Endpoints in Clinical Trials: A Multistakeholder Perspective

The burden of Parkinson's disease (PD) continues to grow at an unsustainable pace particularly given that it now represents the fastest growing brain disease. Despite seminal discoveries in genetics and pathogenesis, people living with PD oftentimes wait years to obtain an accurate diagnosis and have no way to know their own prognostic fate once they do learn they have the disease. Currently, there is no objective biomarker to measure the onset, progression, and severity of PD along the disease continuum. Without such tools, the effectiveness of any given treatment, experimental or conventional cannot be measured. Such tools are urgently needed now more than ever given the rich number of new candidate therapies in the pipeline. Over the last decade, millions of dollars have been directed to identify biomarkers to inform progression of PD typically using molecular, fluid or imaging modalities. These efforts have produced novel insights in our understanding of PD including mechanistic targets, disease subtypes and imaging biomarkers. While we have learned a lot along the way, implementation of robust disease progression biomarkers as tools for quantifying changes in disease status or severity remains elusive. Biomarkers have improved health outcomes and led to accelerated drug approvals in key areas of unmet need such as oncology. Quantitative biomarker measures such as HbA1c a standard test for the monitoring of diabetes has impacted patient care and management, both for the healthcare professionals and the patient community. Such advances accelerate opportunities for early intervention including prevention of disease in high-risk individuals. In PD, progression markers are needed at all stages of the disease in order to catalyze drug development-this allows interventions aimed to halt or slow disease progression (very early) but also facilitates symptomatic treatments at moderate stages of the disease. Recently, attention has turned to the role of digital health technologies to complement the traditional modalities as they are relatively low cost, objective and scalable. Success in this endeavor would be transformative for clinical research and therapeutic development. Consequently, significant investment has led to a number of collaborative efforts to identify and validate suitable digital biomarkers of disease progression.

Continuous Real-World Gait Monitoring in Idiopathic REM Sleep Behavior Disorder

BACKGROUND

Patients with REM sleep behavior disorder (RBD) have a high risk of developing PD, and thus can be used to study prodromal biomarkers. RBD has been associated with changes in gait; quantifying these changes using wearable technology is promising; however, most data are obtained in clinical settings precluding pragmatic application.

OBJECTIVE

We aimed to investigate if wearable-based, real-world gait monitoring can detect early gait changes and discriminate individuals with RBD from controls, and explore relationships between real-world gait and clinical characteristics.

METHODS

63 individuals with RBD (66±10 years) and 34 controls recruited in the Oxford Parkinson's Disease Centre Discovery Study were assessed. Data were collected using a wearable device positioned on the lower back for 7 days. Real-world gait was quantified in terms of its Macrostructure (volume, pattern and variability (S2)) and Microstructure (14 characteristics). The value of Macro and Micro gait in discriminating RBD from controls was explored using ANCOVA and ROC analysis, and correlation analysis was performed between gait and clinical characteristics.

RESULTS

Significant differences were found in discrete Micro characteristics in RBD with reduced gait velocity, variability and rhythm (p≤0.023). These characteristics significantly discriminated RBD (AUC≥0.620), with swing time as the single strongest discriminator (AUC=0.652). Longer walking bouts discriminated best between the groups for Macro and Micro outcomes (p≤0.036).

CONCLUSIONS

Our results suggest that real-world gait monitoring may have utility as "risk" clinical marker in RBD participants. Real-world gait assessment is low-cost and could serve as a pragmatic screening tool to identify gait impairment in RBD.

Orofacial pain in 1916 patients with early or moderate Parkinson disease

This article reports the largest epidemiological study of orofacial pain prevalence in patients with Parkinson disease to date.

Introduction:

Several studies have reported that some types of orofacial pain are more common in patients with Parkinson disease (PD) than the general population.

Objectives:

In this study, we aimed to investigate the prevalence of self-reported orofacial pain in a larger group of patients with PD than has been previously studied.

Methods:

We analysed data from 1916 participants with PD in a cross-sectional study recruited to the UK Parkinson's Pain Study who had detailed assessments of pain, motor, and nonmotor symptoms. The King's Parkinson's Pain scale was used to quantify different subtypes of pain.

Results:

A total of 139 (7.3%) patients reported the presence of some form of orofacial pain. Burning mouth syndrome was reported in 32 (1.7%), whereas chewing pain was found in 38 (2.0%) and grinding pain in 78 (4.0%). Orofacial pain was significantly more common in females (10.4%) than males (5.9%). Multiple logistic regression analysis showed a significant association between orofacial pain and pain severity, neuropathic pain, and oral motor and nonmotor dysfunction.

Conclusion:

In our study, population cohort of early patients with PD found prevalence of orofacial pain conditions similar to that in the general population.

Functional Connectomics and Disease Progression in Drug-Naïve Parkinson's Disease Patients

BACKGROUND

Functional brain connectivity alterations may be detectable even before the occurrence of brain atrophy, indicating their potential as early markers of pathological processes.

OBJECTIVE

We aimed to determine the whole-brain network topologic organization of the functional connectome in a large cohort of drug-naïve Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging and to explore whether baseline connectivity changes may predict clinical progression.

METHODS

One hundred and forty-seven drug-naïve, cognitively unimpaired PD patients were enrolled in the study at baseline and compared to 38 age- and gender-matched controls. Non-hierarchical cluster analysis using motor and non-motor data was applied to stratify PD patients into two subtypes: 77 early/mild and 70 early/severe. Graph theory analysis and connectomics were used to assess global and local topological network properties and regional functional connectivity at baseline. Stepwise multivariate regression analysis investigated whether baseline functional imaging data were predictors of clinical progression over 2 years.

RESULTS

At baseline, widespread functional connectivity abnormalities were detected in the basal ganglia, sensorimotor, frontal, and occipital networks in PD patients compared to controls. Decreased regional functional connectivity involving mostly striato-frontal, temporal, occipital, and limbic connections differentiated early/mild from early/severe PD patients. Connectivity changes were found to be independent predictors of cognitive progression at 2-year follow-up.

CONCLUSIONS

Our findings revealed that functional reorganization of the brain connectome occurs early in PD and underlies crucial involvement of striatal projections. Connectomic measures may be helpful to identify a specific PD patient subtype, characterized by severe motor and non-motor clinical burden as well as widespread functional connectivity abnormalities. © 2021 International Parkinson and Movement Disorder Society.

Multimodal phenotypic axes of Parkinson's disease

Individuals with Parkinson’s disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the “average” patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method—similarity network fusion—to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinson’s disease from the Parkinson’s Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from the fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.

Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning

OBJECTIVES

It is important to subdivide Parkinson's disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features.

METHODS

We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples.

RESULTS

When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling's T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations.

CONCLUSION

Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features.

Contributions of Molecular and Optical Techniques to the Clinical Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide. The distinctive neuropathological feature of AD is the intracerebral accumulation of two abnormally folded proteins: β-amyloid (Aβ) in the form of extracellular plaques, and tau in the form of intracellular neurofibrillary tangles. These proteins are considered disease-specific biomarkers, and the definite diagnosis of AD relies on their post-mortem identification in the brain. The clinical diagnosis of AD is challenging, especially in the early stages. The disease is highly heterogeneous in terms of clinical presentation and neuropathological features. This phenotypic variability seems to be partially due to the presence of distinct Aβ conformers, referred to as strains. With the development of an innovative technique named Real-Time Quaking-Induced Conversion (RT-QuIC), traces of Aβ strains were found in the cerebrospinal fluid of AD patients. Emerging evidence suggests that different conformers may transmit their strain signature to the RT-QuIC reaction products. In this review, we describe the current challenges for the clinical diagnosis of AD and describe how the RT-QuIC products could be analyzed by a surface-enhanced Raman spectroscopy (SERS)-based systems to reveal the presence of strain signatures, eventually leading to early diagnosis of AD with the recognition of individual disease phenotype.

Cohort profile: the Oxford Parkinson's Disease Centre Discovery Cohort MRI substudy (OPDC-MRI)

PURPOSE

The Oxford Parkinson's Disease Centre (OPDC) Discovery Cohort MRI substudy (OPDC-MRI) collects high-quality multimodal brain MRI together with deep longitudinal clinical phenotyping in patients with Parkinson's, at-risk individuals and healthy elderly participants. The primary aim is to detect pathological changes in brain structure and function, and develop, together with the clinical data, biomarkers to stratify, predict and chart progression in early-stage Parkinson's and at-risk individuals.

PARTICIPANTS

Participants are recruited from the OPDC Discovery Cohort, a prospective, longitudinal study. Baseline MRI data are currently available for 290 participants: 119 patients with early idiopathic Parkinson's, 15 Parkinson's patients with pathogenic mutations of the leucine-rich repeat kinase 2 or glucocerebrosidase (GBA) genes, 68 healthy controls and 87 individuals at risk of Parkinson's (asymptomatic carriers of GBA mutation and patients with idiopathic rapid eye movement sleep behaviour disorder-RBD).

FINDINGS TO DATE

Differences in brain structure in early Parkinson's were found to be subtle, with small changes in the shape of the globus pallidus and evidence of alterations in microstructural integrity in the prefrontal cortex that correlated with performance on executive function tests. Brain function, as assayed with resting fMRI yielded more substantial differences, with basal ganglia connectivity reduced in early Parkinson'sand RBD. Imaging of the substantia nigra with the more recent adoption of sequences sensitive to iron and neuromelanin content shows promising results in identifying early signs of Parkinsonian disease.

FUTURE PLANS

Ongoing studies include the integration of multimodal MRI measures to improve discrimination power. Follow-up clinical data are now accumulating and will allow us to correlate baseline imaging measures to clinical disease progression. Follow-up MRI scanning started in 2015 and is currently ongoing, providing the opportunity for future longitudinal imaging analyses with parallel clinical phenotyping.

Identification of potential urine biomarkers in idiopathic parkinson's disease using NMR

INTRODUCTION

Parkinson's disease (PD) is the most common neurodegenerative disease caused by the loss of dopamine chemicals resulting in urinary incontinence, gastrointestinal dysfunction, gait impairment and mitochondrial dysfunction. Study investigated urinary metabolic profiles of patients with idiopathic PD as compared to healthy controls (HC) to identify the potential biomarkers.

METHODS

Urine samples were collected from 100 PD subjects and 50 HC using standard protocol. Metabolomic analyses were performed using high resolution nuclear magnetic resonance (NMR) spectroscopy. The integral values of 17 significant metabolites were estimated and concentration values were calculated, which were subjected to univariate and multivariate statistical analysis.

RESULTS

We found significantly increased levels of ornithine, phenylalanine, isoleucine, β-hydroxybutyrate, tyrosine and succinate in the urine of patients with PD in comparison with HC. These metabolites exhibited area under the curve greater than 0.60 on ROC curve analysis. We also observed a significant association between succinate concentration and UPDRS motor scores of PD.

DISCUSSION

Metabolic pathway alterations were observed in aromatic amino acid metabolism, ketone bodies synthesis, branched chain amino acid metabolism and ornithine metabolism. Comprehensive metabolomic profiling revealed variations in urinary signatures associated with severity of idiopathic PD. This profiling relies on non-invasive sampling and is complementary to existing clinical modalities.

Clinical Characterization of Parkinson's Disease Patients With Cognitive Impairment

Background: Cognitive impairment is one of the most frequent and disabling non-motor symptoms in Parkinson disease (PD) and encompasses a continuum from mild cognitive impairment (PD-MCI) to dementia (PDD). The risk factors associated with them are not completely elucidated. Objective: To characterize the presence and clinical presentation of PD-MCI and PDD in patients with idiopathic PD, examining motor and non-motor features and determining factors associated with cognitive impairment. Methods: Multicenter, cross-sectional study in 298 PD patients who underwent clinical [Hoehn and Yahr (HY) staging and Clinical Impression of Severity Index for Parkinson Disease], neurological [Scales for Outcomes in Parkinson's Disease (SCOPA)-Motor], neuropsychological (Mini Mental State Examination, SCOPA-Cognition, Frontal Assessment Battery and Clinical Dementia Rating Scale), neuropsychiatric [SCOPA-Psychiatric complications, SCOPA-Psychosocial (SCOPA-PS), and Hospital Anxiety and Depression Scale (HADS)], and health-related quality of life [Parkinson Disease Questionnaire for quality of life (PDQ-8)] assessment. Movement Disorders Society criteria were applied to classify patients as normal cognition (NC), PD-MCI, and PDD. The association between variables was explored using multivariate binary and multinomial logistic regression models. Results: Seventy-two patients (24.2%) were classified as NC, 82 (27.5%) as PD-MCI, and 144 (48.3%) as PDD. These last two groups reported more psychosocial problems related with the disease (mean SCOPA-PS, 16.27 and 10.39, respectively), compared with NC (7.28) and lower quality-of-life outcomes (PDQ-8 48.98 and 28.42, respectively) compared to NC (19.05). The logistic regression analysis showed that both cognitive impaired groups had a more severe stage of PD measured by HY [odds ratio (OR) for MCI-PD, 2.45; 95% confidence interval (CI), 1.22-4.90; OR for PDD 2.64; 95% CI, 1.17-5.98]. Specifically, age (OR, 1.30; 95% CI, 1.16-1.47), years of education (OR, 0.91; 95% CI, 0.83-0.99), disease duration (OR, 1.19; 95% CI, 1.07-1.32), HADS-D (OR, 1.20; 95% CI, 1.06-1.35), and hallucinations (OR, 2.98; 95% CI, 1.16-7.69) were related to PDD. Conclusions: Cognitive impairment in PD is associated with more severe disease stage, resulting in a global, neuropsychiatric, psychosocial, and quality-of-life deterioration. This study provides a better understanding of the great impact that cognitive impairment has within the natural history of PD and its relationship with the rest of motor and non-motor symptoms in the disease.

Parkinson disease clinical subtypes: key features & clinical milestones

OBJECTIVES

Based on multi-domain classification of Parkinson disease (PD) subtypes, we sought to determine the key features that best differentiate subtypes and the utility of PD subtypes to predict clinical milestones.

METHODS

Prospective cohort of 162 PD participants with ongoing, longitudinal follow-up. Latent class analysis (LCA) delineated subtypes based on score patterns across baseline motor, cognitive, and psychiatric measures. Discriminant analyses identified key features that distinguish subtypes at baseline. Cox regression models tested PD subtype differences in longitudinal conversion to clinical milestones, including deep brain stimulation (DBS), dementia, and mortality.

RESULTS

LCA identified distinct subtypes: "motor only" (N = 63) characterized by primary motor deficits; "psychiatric & motor" (N = 17) characterized by prominent psychiatric symptoms and moderate motor deficits; "cognitive & motor" (N = 82) characterized by impaired cognition and moderate motor deficits. Depression, executive function, and apathy best discriminated subtypes. Since enrollment, 22 had DBS, 48 developed dementia, and 46 have died. Although there were no subtype differences in rate of DBS, dementia occurred at a higher rate in the "cognitive & motor" subtype. Surprisingly, mortality risk was similarly elevated for both "cognitive & motor" and "psychiatric & motor" subtypes compared to the "motor only" subtype (relative risk = 3.15, 2.60).

INTERPRETATION

Psychiatric and cognitive features, rather than motor deficits, distinguish clinical PD subtypes and predict greater risk of subsequent dementia and mortality. These results emphasize the value of multi-domain assessments to better characterize clinical variability in PD. Further, differences in dementia and mortality rates demonstrate the prognostic utility of PD subtypes.

The Dementias Platform UK (DPUK) Data Portal

The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers. Researchers are provided with their own secure 'lab' using VMware which is accessed using two factor authentication. Over the last 2 years, 160 project proposals involving 579 individual cohort data access requests were received. These were received from 268 applicants spanning 72 institutions (56 academic, 13 commercial, 3 government) in 16 countries with 84 requests involving multiple cohorts. Projects are varied including multi-modal, machine learning, and Mendelian randomisation analyses. Data access is usually free at point of use although a small number of cohorts require a data access fee.

Creation of a gene expression classifier for predicting Parkinson's disease rate of progression

Parkinson's disease (PD) etiology is heterogeneous, genetic, and multi-factorial, resulting in a varied disease from a mild slow progression to a more severe rapid progression. Prognostic information on the nature of the patient's disease at diagnosis aids the physician in counseling patients on treatment options and life planning. In a cohort of PD patients from the PPMI study, the relative gene expression levels of SKP1A, UBE2K, ALDH1A1, PSMC4, HSPA8 and LAMB2 were measured in baseline blood samples by real-time quantitative PCR. At baseline PD patients were up to 2 years from diagnosis, H&Y scale ≤ 2 and PD treatment naïve. PD-Prediction algorithm comprised of ALDH1A1, LAMB2, UBE2K, SKP1A and age was created by logistic regression for predicting progression to ≤ 70% Modified Schwab and England Activities of Daily Living (S&E-ADL). In relation to patients negative for PD-Prediction (n = 180), patients positive (n = 30) for Cutoff-1 (at 82% specificity, 80.0% sensitivity) had positive hazard ratio (HR+) of 10.6 (95% CI, 2.2-50.1), and positive (n = 23) for Cutoff-2 (at 93% specificity, 47% sensitivity) had HR+ of 17.1 (95% CI, 3.2-89.9) to progress to ≤ 70% S&E-ADL within 3 years (P value < 0.0001). Likewise, patients positive for PD-Prediction Cutoff-1 (n = 49) had HR+ 4.3 (95% CI, 1.6-11.6) for faster time to H&Y 3 in relation to patients negative (n = 170) for PD-Prediction (P value = 0.0002). Our findings show an algorithm that seems to predict fast PD progression and may potentially be used as a tool to assist the physician in choosing an optimal treatment plan, improving the patient's quality of life and overall health outcome.

Update on the Management of Parkinson's Disease for General Neurologists

Management of Parkinson's disease (PD) is complicated due to its progressive nature, the individual patient heterogeneity, and the wide range of signs, symptoms, and daily activities that are increasingly affected over its course. The last 10–15 years have seen great progress in the identification, evaluation, and management of PD, particularly in the advanced stages. Highly specialized information can be found in the scientific literature, but updates do not always reach general neurologists in a practical and useful way, potentially creating gaps in knowledge of PD between them and neurologists subspecialized in movement disorders, resulting in several unmet patient needs. However, general neurologists remain instrumental in diagnosis and routine management of PD. This review provides updated practical information to identify problems and resolve common issues, particularly when the advanced stage is suspected. Some tips are provided for efficient communication with the members of a healthcare team specialized in movement disorders, in order to find support at any stage of the disease in a given patient, and especially for a well-timed decision on referral.

Immunotherapies in Huntington's disease and α-Synucleinopathies

Modulation of immune activation using immunotherapy has attracted considerable attention for many years as a potential therapeutic intervention for several inflammation-associated neurodegenerative diseases. However, the efficacy of single-target immunotherapy intervention has shown limited or no efficacy in alleviating disease burden and restoring functional capacity. Marked immune system activation and neuroinflammation are important features and prodromal signs in polyQ repeat disorders and α-synucleinopathies. This review describes the current status and future directions of immunotherapies in proteinopathy-induced neurodegeneration with emphasis on preclinical and clinical efficacies of several anti-inflammatory compounds and antibody-based therapies for the treatment of Huntington's disease and α-synucleinopathies. The review concludes with how disease modification and functional restoration could be achieved by using targeted multimodality therapy to target multiple factors.

Blood biomarkers with Parkinson's disease clusters and prognosis: The oxford discovery cohort

Background

Predicting prognosis in Parkinson's disease (PD) has important implications for individual prognostication and clinical trials design and targeting novel treatments. Blood biomarkers could help in this endeavor.

Methods

We identified 4 blood biomarkers that might predict prognosis: apolipoprotein A1, C‐reactive protein, uric acid and vitamin D. These biomarkers were measured in baseline serum from 624 Parkinson's disease subjects (median disease duration, 1.0 years; interquartile range, 0.5–2.0) from the Oxford Discovery prospective cohort. We compared these biomarkers against PD subtypes derived from clinical features in the baseline cohort using data‐driven approaches. We used multilevel models with MDS‐UPDRS parts I, II, and III and Montreal Cognitive Assessment as outcomes to test whether the biomarkers predicted subsequent progression in motor and nonmotor domains. We compared the biomarkers against age of PD onset and age at diagnosis. The q value, a false‐discovery rate alternative to P values, was calculated as an adjustment for multiple comparisons.

Results

Apolipoprotein A1 and C‐reactive protein levels differed across our PD subtypes, with severe motor disease phenotype, poor psychological well‐being, and poor sleep subtype having reduced apolipoprotein A1 and higher C‐reactive protein levels. Reduced apolipoprotein A1, higher C‐reactive protein, and reduced vitamin D were associated with worse baseline activities of daily living (MDS‐UPDRS II).

Conclusion

Baseline clinical subtyping identified a pro‐inflammatory biomarker profile significantly associated with a severe motor/nonmotor disease phenotype, lending biological validity to subtyping approaches. No blood biomarker predicted motor or nonmotor prognosis. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Efficient RT-QuIC seeding activity for α-synuclein in olfactory mucosa samples of patients with Parkinson's disease and multiple system atrophy

Background

Parkinson’s disease (PD) is a neurodegenerative disorder whose diagnosis is often challenging because symptoms may overlap with neurodegenerative parkinsonisms. PD is characterized by intraneuronal accumulation of abnormal α-synuclein in brainstem while neurodegenerative parkinsonisms might be associated with accumulation of either α-synuclein, as in the case of Multiple System Atrophy (MSA) or tau, as in the case of Corticobasal Degeneration (CBD) and Progressive Supranuclear Palsy (PSP), in other disease-specific brain regions. Definite diagnosis of all these diseases can be formulated only neuropathologically by detection and localization of α-synuclein or tau aggregates in the brain. Compelling evidence suggests that trace-amount of these proteins can appear in peripheral tissues, including receptor neurons of the olfactory mucosa (OM).

Methods

We have set and standardized the experimental conditions to extend the ultrasensitive Real Time Quaking Induced Conversion (RT-QuIC) assay for OM analysis. In particular, by using human recombinant α-synuclein as substrate of reaction, we have assessed the ability of OM collected from patients with clinical diagnoses of PD and MSA to induce α-synuclein aggregation, and compared their seeding ability to that of OM samples collected from patients with clinical diagnoses of CBD and PSP.

Results

Our results showed that a significant percentage of MSA and PD samples induced α-synuclein aggregation with high efficiency, but also few samples of patients with the clinical diagnosis of CBD and PSP caused the same effect. Notably, the final RT-QuIC aggregates obtained from MSA and PD samples owned peculiar biochemical and morphological features potentially enabling their discrimination.

Conclusions

Our study provide the proof-of-concept that olfactory mucosa samples collected from patients with PD and MSA possess important seeding activities for α-synuclein. Additional studies are required for (i) estimating sensitivity and specificity of the technique and for (ii) evaluating its application for the diagnosis of PD and neurodegenerative parkinsonisms. RT-QuIC analyses of OM and cerebrospinal fluid (CSF) can be combined with the aim of increasing the overall diagnostic accuracy of these diseases, especially in the early stages.

Electronic supplementary material

The online version of this article (10.1186/s40035-019-0164-x) contains supplementary material, which is available to authorized users.

Predicting motor, cognitive & functional impairment in Parkinson's

OBJECTIVE

We recently demonstrated that 998 features derived from a simple 7-minute smartphone test could distinguish between controls, people with Parkinson's and people with idiopathic Rapid Eye Movement sleep behavior disorder, with mean sensitivity/specificity values of 84.6-91.9%. Here, we investigate whether the same smartphone features can be used to predict future clinically relevant outcomes in early Parkinson's.

METHODS

A total of 237 participants with Parkinson's (mean (SD) disease duration 3.5 (2.2) years) in the Oxford Discovery cohort performed smartphone tests in clinic and at home. Each test assessed voice, balance, gait, reaction time, dexterity, rest, and postural tremor. In addition, standard motor, cognitive and functional assessments and questionnaires were administered in clinic. Machine learning algorithms were trained to predict the onset of clinical outcomes provided at the next 18-month follow-up visit using baseline smartphone recordings alone. The accuracy of model predictions was assessed using 10-fold and subject-wise cross validation schemes.

RESULTS

Baseline smartphone tests predicted the new onset of falls, freezing, postural instability, cognitive impairment, and functional impairment at 18 months. For all outcome predictions AUC values were greater than 0.90 for 10-fold cross validation using all smartphone features. Using only the 30 most salient features, AUC values greater than 0.75 were obtained.

INTERPRETATION

We demonstrate the ability to predict key future clinical outcomes using a simple smartphone test. This work has the potential to introduce individualized predictions to routine care, helping to target interventions to those most likely to benefit, with the aim of improving their outcome.

The Personalized Parkinson Project: examining disease progression through broad biomarkers in early Parkinson's disease

BACKGROUND

Our understanding of the etiology, pathophysiology, phenotypic diversity, and progression of Parkinson's disease has stagnated. Consequently, patients do not receive the best care, leading to unnecessary disability, and to mounting costs for society. The Personalized Parkinson Project (PPP) proposes an unbiased approach to biomarker development with multiple biomarkers measured longitudinally. Our main aims are: (a) to perform a set of hypothesis-driven analyses on the comprehensive dataset, correlating established and novel biomarkers to the rate of disease progression and to treatment response; and (b) to create a widely accessible dataset for discovery of novel biomarkers and new targets for therapeutic interventions in Parkinson's disease.

METHODS/DESIGN

This is a prospective, longitudinal, single-center cohort study. The cohort will comprise 650 persons with Parkinson's disease. The inclusion criteria are purposely broad: age ≥ 18 years; and disease duration ≤5 years. Participants are followed for 2 years, with three annual assessments at the study center. Outcomes include a clinical assessment (including motor and neuro-psychological tests), collection of biospecimens (stool, whole blood, and cerebrospinal fluid), magnetic resonance imaging (both structural and functional), and ECG recordings (both 12-lead and Holter). Additionally, collection of physiological and environmental data in daily life over 2 years will be enabled through the Verily Study Watch. All data are stored with polymorphic encryptions and pseudonyms, to guarantee the participants' privacy on the one hand, and to enable data sharing on the other. The data and biospecimens will become available for scientists to address Parkinson's disease-related research questions.

DISCUSSION

The PPP has several distinguishing elements: all assessments are done in a single center; inclusion of "real life" subjects; deep and repeated multi-dimensional phenotyping; and continuous monitoring with a wearable device for 2 years. Also, the PPP is powered by privacy and security by design, allowing for data sharing with scientists worldwide respecting participants' privacy. The data are expected to open the way for important new insights, including identification of biomarkers to predict differences in prognosis and treatment response between patients. Our long-term aim is to improve existing treatments, develop new therapeutic approaches, and offer Parkinson's disease patients a more personalized disease management approach.

TRIAL REGISTRATION

Clinical Trials NCT03364894 . Registered December 6, 2017 (retrospectively registered).

Altered Neurometabolic Profile in Early Parkinson's Disease: A Study With Short Echo-Time Whole Brain MR Spectroscopic Imaging

Objective: To estimate alterations in neurometabolic profile of patients with early stage Parkinson's disease (PD) by using a short echo-time whole brain magnetic resonance spectroscopic imaging (wbMRSI) as possible biomarker for early diagnosis and monitoring of PD.

Methods: 20 PD patients in early stage (H&Y ≤ 2) without evidence of severe other diseases and 20 age and sex matched healthy controls underwent wbMRSI. In each subject brain regional concentrations of metabolites N-acetyl-aspartate (NAA), choline (Cho), total creatine (tCr), glutamine (Gln), glutamate (Glu), and myo-inositol (mIns) were obtained in atlas-defined lobar structures including subcortical basal ganglia structures (the left and right frontal lobes, temporal lobes, parietal lobes, occipital lobes, and the cerebellum) and compared between patients and matched healthy controls. Clinical characteristics of the PD patients were correlated with spectroscopic findings.

Results: In comparison to controls the PD patients revealed altered lobar metabolite levels in all brain lobes contralateral to dominantly affected body side, i.e., decreases of temporal NAA, Cho, and tCr, parietal NAA and tCr, and frontal as well as occipital NAA. The frontal NAA correlated negatively with the MDS-UPDRS II (R = 22120.585, p = 0.008), MDS-UPDRS IV (R = −0.458, p = 0.048) and total MDS-UPDRS scores (R = −0.679, p = 0.001).

Conclusion: In early PD stages metabolic alterations are evident in all contralateral brain lobes demonstrating that the neurodegenerative process affects not only local areas by dopaminergic denervation, but also the functional network within different brain regions. The wbMRSI-detectable brain metabolic alterations reveal the potential to serve as biomarkers for early PD.

Neuropathology and pathogenesis of extrapyramidal movement disorders: a critical update-I. Hypokinetic-rigid movement disorders

Extrapyramidal movement disorders include hypokinetic rigid and hyperkinetic or mixed forms, most of them originating from dysfunction of the basal ganglia (BG) and their information circuits. The functional anatomy of the BG, the cortico-BG-thalamocortical, and BG-cerebellar circuit connections are briefly reviewed. Pathophysiologic classification of extrapyramidal movement disorder mechanisms distinguish (1) parkinsonian syndromes, (2) chorea and related syndromes, (3) dystonias, (4) myoclonic syndromes, (5) ballism, (6) tics, and (7) tremor syndromes. Recent genetic and molecular-biologic classifications distinguish (1) synucleinopathies (Parkinson's disease, dementia with Lewy bodies, Parkinson's disease-dementia, and multiple system atrophy); (2) tauopathies (progressive supranuclear palsy, corticobasal degeneration, FTLD-17; Guamian Parkinson-dementia; Pick's disease, and others); (3) polyglutamine disorders (Huntington's disease and related disorders); (4) pantothenate kinase-associated neurodegeneration; (5) Wilson's disease; and (6) other hereditary neurodegenerations without hitherto detected genetic or specific markers. The diversity of phenotypes is related to the deposition of pathologic proteins in distinct cell populations, causing neurodegeneration due to genetic and environmental factors, but there is frequent overlap between various disorders. Their etiopathogenesis is still poorly understood, but is suggested to result from an interaction between genetic and environmental factors. Multiple etiologies and noxious factors (protein mishandling, mitochondrial dysfunction, oxidative stress, excitotoxicity, energy failure, and chronic neuroinflammation) are more likely than a single factor. Current clinical consensus criteria have increased the diagnostic accuracy of most neurodegenerative movement disorders, but for their definite diagnosis, histopathological confirmation is required. We present a timely overview of the neuropathology and pathogenesis of the major extrapyramidal movement disorders in two parts, the first one dedicated to hypokinetic-rigid forms and the second to hyperkinetic disorders.

Neurodegenerative Diseases and Ageing

This chapter describes the main neuropathological features of the most common age associated neurodegenerative diseases including Alzheimer's disease, Lewy body diseases, vascular dementia and the various types of frontotemporal lobar degeneration. In addition, the more recent concepts of primary age-related tauopathy and ageing-related tau astrogliopathy as well as chronic traumatic encephalopathy are briefly described. One section is dedicated to cerebral multi-morbidity as it is becoming increasingly clear that the old brain is characterised by the presence of multiple pathologies (to varying extent) rather than by one single, disease specific pathology alone. The main aim of this chapter is to inform the reader about the neuropathological basics of age associated neurodegenerative diseases as we feel this is crucial to meaningfully interpret the vast literature that is published in the broad field of dementia research.

Characterization of Vitamin B12 Supplementation and Correlation with Clinical Outcomes in a Large Longitudinal Study of Early Parkinson's Disease

Objective

In Parkinson’s disease (PD), vitamin B12 levels are lower, and comorbid B12 deficiency has been associated with the development of neuropathy and early gait instability. Because little is known about B12 supplement use in PD, we sought to evaluate its use in a large PD cohort and, as an exploratory analysis, to determine whether baseline characteristics or disease progression differed according to B12 supplementation.

Methods

We utilized data collected as part of the National Institutes of Health Exploratory Trials in PD (NET-PD) Long-term Study (LS-1), a longitudinal study of 1,741 participants. We stratified subjects into 4 groups according to daily supplement use: no B12, multivitamin (MVI) containing < 100 μg B12, B12 ≥ 100 μg, and MVI + B12 ≥ 100 μg. Clinical outcomes were assessed at 3 years for each group using the Unified Parkinson’s Disease Rating Scale (UPDRS), its subscores, and selected individual questions.

Results

Of the 1,147 participants who completed the 3-year visit, 41% took an MVI, 2% took B12, 3% took MVI + B12, and 54% reported taking no supplements. At 3 years, no significant differences in clinical outcomes were observed. However, there was a trend toward lower hazard ratios for developing sensory symptoms (UPDRS Item 17) in the MVI (p = 0.08) and B12 + MVI (p = 0.08) groups compared to that in the no supplement group.

Conclusion

These results show that supplementation with vitamin B12 ≥ 100 μg is uncommon in early PD. The finding of a trend toward a lower hazard ratio for the development of sensory symptoms in those taking an MVI or B12 + MVI warrants further study.

Parkinson's Disease in the Era of Personalised Medicine: One Size Does Not Fit All

The concept of personalised medicine in Parkinson's disease has arrived where the implications of findings made in research are certain to have an increasing impact upon clinical practice. Disease heterogeneity in Parkinson's disease has been well described and lends itself to the construct of personalised medicine where it is hypothesised that a greater understanding of genetic and pathophysiological contributions may underpin the sub-groups described. This in turn has driven the development of potentially individualised disease-modifying therapies where, for example, we are beginning to see treatments that target patients with Parkinson's disease with specific genetic mutations. Furthermore, clinicians are increasingly recognising the need to tailor their management approach to patients depending on their age of presentation, acknowledging differential side-effect profiles and responses especially when considering the use of device-assisted technologies such as infusion or surgery. Clearly, individualising the treatment of both motor and non-motor symptoms will remain imperative but, in the future, personalised medicine may provide clearer insights into various aspects of a patient's symptomatology, disease course and thus the best therapeutic approaches.

Peripheral Neuropathy Research Registry: A prospective cohort

The Peripheral Neuropathy Research Registry (PNRR) is a prospective cohort of peripheral neuropathy (PN) patients focused on idiopathic axonal peripheral neuropathy. Patients with diabetic, human immunodeficiency virus-, and chemotherapy-induced peripheral neuropathies are enrolled as comparison groups. The PNRR is a multi-center collaboration initiated and funded by the Foundation for Peripheral Neuropathy (FPN) with the objective to recruit a well characterized cohort of patients with different phenotypes and symptoms in each diagnostic category, and to advance research through development of biomarkers and identification of previously unknown causes of PN. The overall goal of the initiative is to find disease-altering treatments and better symptom relief for patients. We present the study design, types of data collected, and characteristics of the first 1150 patients enrolled. We also discuss ongoing analyses on this dataset, including untargeted-omics methodologies.

Data-Driven Subtyping of Parkinson's Disease Using Longitudinal Clinical Records: A Cohort Study

Parkinson's disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson's Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson's disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time series for subtype identification. Both visualization and statistical analysis were performed for analyzing the obtained PD subtypes. As a result, 466 patients with idiopathic PD were investigated and three subtypes were identified. Subtype I (Mild Baseline, Moderate Motor Progression) is comprised of 43.1% of the participants, with average age 58.79 ± 9.53 years, and was characterized by moderate functional decay in motor ability but stable cognitive ability. Subtype II (Moderate Baseline, Mild Progression) is comprised of 22.9% of the participants, with average age 61.93 ± 6.56 years, and was characterized by mild functional decay in both motor and non-motor symptoms. Subtype III (Severe Baseline, Rapid Progression) is comprised 33.9% of the patients, with average age 65.32 ± 8.86 years, and was characterized by rapid progression of both motor and non-motor symptoms. These subtypes suggest that when comprehensive clinical and biomarker data are incorporated into a deep learning algorithm, the disease progression rates do not necessarily associate with baseline severities, and the progression rate of non-motor symptoms is not necessarily correlated with the progression rate of motor symptoms.

Working Memory in Alzheimer's Disease and Parkinson's Disease

Working memory impairments are frequently observed in patients with Alzheimer's disease (AD) and Parkinson's disease (PD). Recent research suggests that the mechanisms underlying these deficits might be dissociable using sensitive tasks, specifically those that rely on the reproduction of the exact quality of features held in memory.In patients with AD, working memory impairments are mainly due to an increase in misbinding errors. They arise when patients misremember which features (e.g., color, orientation, shape, and location) belong to different objects held in memory. Hence, they erroneously report features that belong to items in memory other than the one they are probed on. This misbinding of features that belong to different objects in memory can be considered a form of interference between stored items. Such binding errors are evident even in presymptomatic individuals with familial AD (due to gene mutations) who do not have AD yet. Overall, these findings are in line with the role of the medial temporal lobes, and specifically the hippocampus, in retention of feature bindings, regardless of retention duration, i.e., in both short- or long-term memory.Patients with PD, on the other hand, do not show increased misbinding. Their working memory deficits are associated with making more random errors or guesses. These random responses are not modulated by manipulations of their dopaminergic medication and hence may reflect involvement of non-dopaminergic neurotransmitters in this deficit. In addition, patients with PD demonstrate impairments in gating of information into relevant vs. irrelevant items in memory, a cognitive operation that is modulated by dopaminergic manipulation in line with a frontal executive effect of this neurotransmitter. Thus, although AD and PD are both associated with working memory impairments, these surface manifestations appear to be underpinned by very different mechanisms.

Cerebrospinal fluid biomarker for Parkinson's disease: An overview

In Parkinson's disease (PD), there is a wide field of recent and ongoing search for useful biomarkers for early and differential diagnosis, disease monitoring or subtype characterization. Up to now, no biofluid biomarker has entered the daily clinical routine. Cerebrospinal fluid (CSF) is often used as a source for biomarker development in different neurological disorders because it reflects changes in central-nervous system homeostasis. This review article gives an overview about different biomarker approaches in PD, mainly focusing on CSF analyses. Current state and future perspectives regarding classical protein markers like alpha‑synuclein, but also different "omics" techniques are described. In conclusion, technical advancements in the field already yielded promising results, but further multicenter trials with well-defined cohorts, standardized protocols and integrated data analysis of different modalities are needed before successful translation into routine clinical application.

Developing and validating Parkinson's disease subtypes and their motor and cognitive progression

OBJECTIVES

To use a data-driven approach to determine the existence and natural history of subtypes of Parkinson's disease (PD) using two large independent cohorts of patients newly diagnosed with this condition.

METHODS

1601 and 944 patients with idiopathic PD, from Tracking Parkinson's and Discovery cohorts, respectively, were evaluated in motor, cognitive and non-motor domains at the baseline assessment. Patients were recently diagnosed at entry (within 3.5 years of diagnosis) and were followed up every 18 months. We used a factor analysis followed by a k-means cluster analysis, while prognosis was measured using random slope and intercept models.

RESULTS

We identified four clusters: (1)  fast motor progression with symmetrical motor disease, poor olfaction, cognition and postural hypotension; (2) mild motor and non-motor disease with intermediate motor progression; (3) severe motor disease, poor psychological well-being and  poor sleep with an intermediate motor progression; (4) slow motor progression with tremor-dominant, unilateral disease. Clusters were moderately to substantially stable across the two cohorts (kappa 0.58). Cluster 1 had the fastest motor progression in Tracking Parkinson's at 3.2 (95% CI 2.8 to 3.6) UPDRS III points per year while cluster 4 had the slowest at 0.6 (0.1-1.1). In Tracking Parkinson's, cluster 2 had the largest response to levodopa 36.3% and cluster 4 the lowest 28.8%.

CONCLUSIONS

We have found four novel clusters that replicated well across two independent early PD cohorts and were associated with levodopa response and motor progression rates. This has potential implications for better understanding disease pathophysiology and the relevance of patient stratification in future clinical trials.

A detailed clinical study of pain in 1957 participants with early/moderate Parkinson's disease

INTRODUCTION

The causes of pain in early/moderate Parkinson's disease (PD) are not well understood. Although peripheral factors such as rigidity, reduced joint movements and poor posture may contribute towards the development of pain, central mechanisms including altered nociceptive processing may also be involved.

METHODS

We performed a large clinical study to investigate potential factors contributing towards pain in early/moderate PD. We recruited 1957 PD participants who had detailed assessments of pain, motor and non-motor symptoms. The King's Parkinson's Pain scale was used to quantify different subtypes of pain.

RESULTS

85% of participants reported pain (42% with moderate to severe pain). Pain influenced quality of life more than motor symptoms in a multiple regression model. Factors predicting overall pain severity included affective symptoms, autonomic symptoms, motor complications, female gender and younger age, but not motor impairment or disease duration. There was negligible correlation between the severity of motor impairment and the severity of musculoskeletal or dystonic pain as well as between the severity of OFF period motor problems and the severity of OFF period pain or OFF period dystonic pain. Features of central sensitization, including allodynia and altered pain sensation were common in this population. The use of drugs targeting central pain was very low.

CONCLUSIONS

Pain in early/moderate PD cannot be explained by peripheral factors. Central causes may play a much more important role than previously considered. These results should lead to a major shift in the investigation and management of this common and disabling symptom.