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

Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort

AIMS

Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson's disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI).

METHODS

A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson's Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event.

RESULTS

Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004).

CONCLUSIONS

PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.

Replication and reliability of Parkinson's disease clinical subtypes

BACKGROUND

We recently identified three distinct Parkinson's disease subtypes: "motor only" (predominant motor deficits with intact cognition and psychiatric function); "psychiatric & motor" (prominent psychiatric symptoms and moderate motor deficits); "cognitive & motor" (cognitive and motor deficits).

OBJECTIVE

We used an independent cohort to replicate and assess reliability of these Parkinson's disease subtypes.

METHODS

We tested our original subtype classification with an independent cohort (N = 100) of Parkinson's disease participants without dementia and the same comprehensive evaluations assessing motor, cognitive, and psychiatric function. Next, we combined the original (N = 162) and replication (N = 100) datasets to test the classification model with the full combined dataset (N = 262). We also generated 10 random split-half samples of the combined dataset to establish the reliability of the subtype classifications. Latent class analyses were applied to the replication, combined, and split-half samples to determine subtype classification.

RESULTS

First, LCA supported the three-class solution - Motor Only, Psychiatric & Motor, and Cognitive & Motor- in the replication sample. Next, using the larger, combined sample, LCA again supported the three subtype groups, with the emergence of a potential fourth group defined by more severe motor deficits. Finally, split-half analyses showed that the three-class model also had the best fit in 13/20 (65%) split-half samples; two-class and four-class solutions provided the best model fit in five (25%) and two (10%) split-half replications, respectively.

CONCLUSIONS

These results support the reproducibility and reliability of the Parkinson's disease behavioral subtypes of motor only, psychiatric & motor, and cognitive & motor groups.

Genome-wide determinants of mortality and motor progression in Parkinson's disease

There are 90 independent genome-wide significant genetic risk variants for Parkinson's disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining mechanisms that can be used to develop new treatments. We studied 6766 PD patients, over 15,340 visits with a mean follow-up of between 4.2 and 15.7 years and carried out genome-wide survival studies for time to a motor progression endpoint, defined by reaching Hoehn and Yahr stage 3 or greater, and death (mortality). There was a robust effect of the APOE ε4 allele on mortality in PD. We also identified a locus within the TBXAS1 gene encoding thromboxane A synthase 1 associated with mortality in PD. We also report 4 independent loci associated with motor progression in or near MORN1, ASNS, PDE5A, and XPO1. Only the non-Gaucher disease causing GBA1 PD risk variant E326K, of the known PD risk variants, was associated with mortality in PD. Further work is needed to understand the links between these genomic variants and the underlying disease biology. However, these may represent new candidates for disease modification in PD.

Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson's disease

To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.

AccessPD as a next generation registry to accelerate Parkinson's disease research

Recruitment is a major rate-limiting factor in Parkinson's disease (PD) research. AccessPD is a unique platform that aims to create a registry of more than 2000 PD patients and a rich database of PD-relevant information. Potential participants are identified using electronic health records (EHRs) in primary care. They are contacted via text message with an individualized link to the study portal. Electronic patient-reported outcomes (ePRO) are collected via online questionnaires and integrated with existing EHR. 200 participants were recruited within the first 6 months, of which 191 answered the follow-up questionnaire. Here, to showcase the potential of AccessPD, we described the most common diagnoses before and after PD diagnosis, the most commonly prescribed drugs, and identified participants who could benefit from device-aided therapies using consensus criteria. AccessPD shows its unique ability to link different data sources for patient stratification in longitudinal studies and recruitment into clinical trials.

Transitioning from Subtyping to Precision Medicine in Parkinson's Disease: A Purpose-Driven Approach

The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose-driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose-driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Association of Family History and Polygenic Risk Score With Longitudinal Prognosis in Parkinson Disease

Background and Objectives

Evidence suggests that either family history or polygenic risk score (PRS) is associated with developing Parkinson disease (PD). However, little is known about the longitudinal prognosis of PD according to family history and higher PRS.

Methods

From the Parkinson's Progression Markers Initiative database, 395 patients with PD who followed up for more than 2 years were grouped into those with family history within first-degree, second-degree, and third-degree relatives (N = 127 [32.2%]) vs those without (N = 268 [67.8%]). The PRS of 386 patients was computed using whole-genome sequencing data. Longitudinal assessment of motor, cognition, and imaging based on dopaminergic degeneration was conducted during the regular follow-up period. Effects of family history, PRS, or both on longitudinal changes of cognition, motor severity, and nigrostriatal degeneration were tested using a linear mixed model. The risk of freezing of gait (FOG) according to family history was assessed using the Kaplan-Meier analysis and Cox regression models.

Results

During a median follow-up of 9.1 years, PD with positive family history showed a slower decline of caudate dopamine transporter uptake (β estimate of family history × time = 0.02, 95% CI = 0.002-0.036, p = 0.027). Family history of PD and higher PRS were independently associated with a slower decline of Montreal Cognitive Assessment (β estimate of family history × time = 0.12, 95% CI = 0.02-0.22, p = 0.017; β estimate of PRS × time = 0.09, 95% CI = 0.03-0.16, p = 0.006). In those 364 patients without FOG at baseline, PD with positive family history had a lower risk of FOG (hazard ratio of family history = 0.57, 95% CI = 0.38-0.84, p = 0.005).

Discussion

Having a family history of PD predicts slower progression of cognitive decline and caudate dopaminergic degeneration, and less FOG compared with those without a family history independent of PRS. Taken together, information on family history could be used as a proxy for the clinical heterogeneity of PD.

Trial Registration Information

The study was registered at clinicaltrials.gov (NCT01141023), and the enrollment began June 1, 2010.

Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease

BACKGROUND

Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers.

OBJECTIVE

To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD).

METHODS

Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow-up duration = 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers (dopaminergic denervation).

RESULTS

PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep-all before diagnosis-and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor symptoms.

CONCLUSIONS

Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.

Disease Progression of Data-Driven Subtypes of Parkinson's Disease: 5-Year Longitudinal Study from the Early Parkinson's Disease Longitudinal Singapore (PALS) Cohort

Background

The detailed trajectory of data-driven subtypes in Parkinson's disease (PD) within Asian cohorts remains undisclosed.

Objective

To evaluate the motor, non-motor symptom (NMS) progression among the data-driven PD clusters.

Methods

In this 5-year longitudinal study, NMS scale (NMSS), Hospital Anxiety Depression Scale (HADS), and Epworth sleepiness scale (ESS) were carried out annually to monitor NMS progression. H& Y staging scale, MDS-UPDRS part III motor score, and postural instability gait difficulty (PIGD) score were assessed annually to evaluate disease severity and motor progression. Five cognitive standardized scores were used to assess detailed cognitive progression. Linear mixed model was performed to assess the annual progression rates of the longitudinal outcomes.

Results

Two hundred and six early PD patients, consisting of 43 patients in cluster A, 98 patients in cluster B and 65 subjects in cluster C. Cluster A (severe subtype) had significantly faster progression slope in NMSS Domain 3 (mood/apathy) score (p = 0.01), NMSS Domain 4 (perceptual problems) score (p = 0.02), NMSS Domain 7 (urinary) score (p = 0.03), and ESS Total Score (p = 0.04) than the other two clusters. Cluster A also progressed significantly in PIGD score (p = 0.04). For cognitive outcomes, cluster A deteriorated significantly in visuospatial domain (p = 0.002), while cluster C (mild subtype) deteriorated significantly in executive domain (p = 0.04).

Conclusions

The severe cluster had significantly faster progression, particularly in mood and perceptual NMS domains, visuospatial cognitive performances, and postural instability gait scores. Our findings will be helpful for clinicians to stratify and pre-emptively manage PD patients by developing intervention strategies to counter the progression of these domains.

Future Directions for Developing Non-dopaminergic Strategies for the Treatment of Parkinson's Disease

The symptomatic treatment of Parkinson's disease (PD) has been dominated by the use of dopaminergic medication, but significant unmet need remains, much of which is related to non-motor symptoms and the involvement of non-dopaminergic transmitter systems. As such, little has changed in the past decades that has led to milestone advances in therapy and significantly improved treatment paradigms and patient outcomes, particularly in relation to symptoms unresponsive to levodopa. This review has looked at how pharmacological approaches to treatment are likely to develop in the near and distant future and will focus on two areas: 1) novel non-dopaminergic pharmacological strategies to control motor symptoms; and 2) novel non-dopaminergic approaches for the treatment of non-motor symptoms. The overall objective of this review is to use a 'crystal ball' approach to the future of drug discovery in PD and move away from the more traditional dopamine-based treatments. Here, we discuss promising non-dopaminergic and 'dirty drugs' that have the potential to become new key players in the field of Parkinson's disease treatment.

Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson's disease

Parkinson's Disease (PD) is the second most common neurodegenerative disease behind Alzheimer's Disease, currently affecting more than 10 million people worldwide and 1.5 times more males than females. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. Here we explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional networks identified by a recent study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis.

Radiomics for Parkinson's disease classification using advanced texture-based biomarkers

Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP.•Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder.•Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis.•The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection.

Prognostic significance of peripheral neutrophils and lymphocytes in early untreated Parkinson's disease: an 8-year follow-up study

BACKGROUND

To explore whether peripheral blood neutrophils and lymphocytes are associated with longitudinal motor and cognitive decline in patients with early Parkinson's disease (PD) and, to uncover the disease-specific mechanisms underlying these associations.

METHODS

Data were obtained from the Parkinson's Progression Markers Initiative cohort. We included 376 patients with recently diagnosed, drug-naïve PD and 178 matched healthy controls. The patients underwent annual assessments, including the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part 3 test to measure motor function and the Montreal Cognitive Assessment (MoCA) to measure cognitive function, for up to 8 years of follow-up. Dopamine transporter (DAT) imaging was performed at baseline and the 1-year, 2-year and 4-year follow-up visits.

RESULTS

At baseline, patients with PD showed higher neutrophil and lower lymphocyte counts, resulting in a higher neutrophil-to-lymphocyte ratio (NLR) than that in healthy controls. Higher neutrophil counts were associated with a greater increase in MDS-UPDRS part 3 scores in patients with PD (estimate: 0.25, 95% CI: 0.12 to 0.37, p<0.001). Correspondingly, higher neutrophil levels were related to a greater reduction in DAT activity in the caudate (estimate: -0.007, 95% CI: -0.014 to -0.001, p=0.046) and putamen (estimate: -0.0039, 95% CI: -0.0077 to -0.0002, p=0.042). However, there were no significant effects of lymphocyte count and NLR on changes in the MDS-UPDRS part 3 and MoCA scores and striatal DAT uptake over time.

CONCLUSION

Among the blood biomarkers, only a higher neutrophil count was associated with faster motor progression along with accelerated nigrostriatal dopaminergic degeneration in patients with PD. The impact of neutrophils and lymphocytes on longitudinal cognitive changes remains unclear.

TRIAL REGISTRATION NUMBER

NCT01141023.

Cognitive impairment and dementia in young onset Parkinson's disease

BACKGROUND AND OBJECTIVE

Patients with young-onset Parkinson's disease (YOPD) have a slower progression. Our aim was to analyze the change in cognitive function in YOPD compared to patients with a later onset and controls.

PATIENTS AND METHODS

Patients with Parkinson's disease (PD) and controls from the COPPADIS cohort were included. Cognitive function was assessed with the Parkinson's Disease Cognitive Rating Scale (PD-CRS) at baseline (V0), 2-year ± 1 month (V2y), and 4-year ± 3 months follow-up (V4y). Regarding age from symptoms onset, patients were classified as YOPD (< 50 years) or non-YOPD (≥ 50). A score in the PD-CRS < 81 was defined as cognitive impairment (CI): ≤ 64 dementia; 65-80 mild cognitive impairment (MCI).

RESULTS

One-hundred and twenty-four YOPD (50.7 ± 7.9 years; 66.1% males), 234 non-YOPD (67.8 ± 7.8 years; 59.3% males) patients, and 205 controls (61 ± 8.3 years; 49.5% males) were included. The score on the PD-CRS and its subscore domains was higher at all visits in YOPD compared to non-YOPD patients and to controls (p < 0.0001 in all analysis), but no differences were detected between YOPD patients and controls. Only non-YOPD patients had significant impairment in their cognitive function from V0 to V4y (p < 0.0001). At V4y, the frequency of dementia and MCI was 5% and 10% in YOPD compared to 25.2% and 22.3% in non-YOPD patients (p < 0.0001). A lower score on the Parkinson's Disease Sleep Scale at baseline was a predictor of CI at V4y in YOPD patients (Adjusted R2 = 0.61; OR = 0.965; p = 0.029).

CONCLUSION

Cognitive dysfunction progressed more slowly in YOPD than in non-YOPD patients.

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.

Multifactorial assessment of Parkinson's disease course and outcomes using trajectory modeling in a multiethnic, multisite cohort - extension of the LONG-PD study

Background

The severity, progression, and outcomes of motor and non-motor symptoms in Parkinson's disease (PD) are quite variable. Following PD cohorts holds promise for identifying predictors of disease severity and progression.

Methods

PD patients (N = 871) were enrolled at five sites. Enrollment occurred within 5 years of initial motor symptom onset. Disease progression was assessed annually for 2-to-10 years after onset. Group-based trajectory modeling was used to identify groups differing in disease progression. Models were developed for UPDRS-III scores, UPDRS-III tremor and bradykinesia-rigidity subscores, Hoehn & Yahr (H&Y) stage, Mini-Mental Status Exam (MMSE) scores, and UPDRS-III, H&Y and MMSE scores considered together. Predictors of trajectory-group membership were modeled simultaneously with the trajectories. Kaplan-Meier survival analysis evaluated survival free of PD outcomes.

Results

The best fitting models identified three groups. One showed a relatively benign, slowly progressing trajectory (Group 1), a second showed a moderate, intermediately progressing trajectory (Group 2), and a third showed a more severe, rapidly progressing trajectory (Group 3). Stable trajectory-group membership occurred relatively early in the disease course, 5 years after initial motor symptom. Predictors of intermediate and more severe trajectory-group membership varied across the single variable models and the multivariable model jointly considering UPDRS-III, H&Y and MMSE scores. In the multivariable model, membership in Group 2 (28.4% of patients), relative to Group 1 (50.5%), was associated with male sex, younger age-at-onset, fewer education-years, pesticide exposure, absence of reported head injury, and akinetic/rigid subtype at initial presentation. Membership in Group 3 (21.3%), relative to Group 1, was associated with older age-at-onset, fewer education-years, pesticide exposure, and the absence of a tremor-predominant subtype at initial presentation. Persistent freezing, persistent falls, and cognitive impairment occurred earliest and more frequently in Group 3, later and less frequently in Group 2, and latest and least frequently in Group 1. Furthermore, autonomic complications, dysphagia, and psychosis occurred more frequently in Groups 2 and 3 than in Group 1.

Conclusion

Modeling disease course using multiple objective assessments over an extended follow-up duration identified groups that more accurately reflect differences in PD course, prognosis, and outcomes than assessing single parameters over shorter intervals.

Identification of Parkinson's Disease Subtypes from Resting-State Electroencephalography

BACKGROUND

Parkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments.

OBJECTIVES

We aimed to identify different PD sub-phenotypes in a longitudinal follow-up analysis and their electrophysiological profile based on resting-state electroencephalography (RS-EEG) and to assess their clinical significance over the course of the disease.

METHODS

Using electrophysiological features obtained from RS-EEG recordings and data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome.

RESULTS

We showed that PD patients (n = 44) can be sub-grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Δ and β band), the frontotemporal network (α2 band) and the default mode network (α1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only-motor) or mild-to-severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped.

CONCLUSIONS

The identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A machine learning approach to predict quality of life changes in patients with Parkinson's Disease

OBJECTIVE

Parkinson disease (PD) is a progressive neurodegenerative disorder with an annual incidence of approximately 0.1%. While primarily considered a motor disorder, increasing emphasis is being placed on its non-motor features. Both manifestations of the disease affect quality of life (QoL), which is captured in part II of the Unified Parkinson's Disease Rating Scale (UPDRS-II). While useful in the management of patients, it remains challenging to predict how QoL will change over time in PD. The goal of this work is to explore the feasibility of a machine learning algorithm to predict QoL changes in PD patients.

METHODS

In this retrospective cohort study, patients with at least 12 months of follow-up were identified from the Parkinson's Progression Markers Initiative database (N = 630) and divided into two groups: those with and without clinically significant worsening in UPDRS-II (n = 404 and n = 226, respectively). We developed an artificial neural network using only UPDRS-II scores, to predict whether a patient would clinically worsen or not at 12 months from follow-up.

RESULTS

Using UPDRS-II at baseline, at 2 months, and at 4 months, the algorithm achieved 90% specificity and 56% sensitivity.

INTERPRETATION

A learning model has the potential to rule in patients who may exhibit clinically significant worsening in QoL at 12 months. These patients may require further testing and increased focus.

Artificial intelligence-based clustering and characterization of Parkinson's disease trajectories

Parkinson's disease (PD) is a highly heterogeneous disease both with respect to arising symptoms and its progression over time. This hampers the design of disease modifying trials for PD as treatments which would potentially show efficacy in specific patient subgroups could be considered ineffective in a heterogeneous trial cohort. Establishing clusters of PD patients based on their progression patterns could help to disentangle the exhibited heterogeneity, highlight clinical differences among patient subgroups, and identify the biological pathways and molecular players which underlie the evident differences. Further, stratification of patients into clusters with distinct progression patterns could help to recruit more homogeneous trial cohorts. In the present work, we applied an artificial intelligence-based algorithm to model and cluster longitudinal PD progression trajectories from the Parkinson's Progression Markers Initiative. Using a combination of six clinical outcome scores covering both motor and non-motor symptoms, we were able to identify specific clusters of PD that showed significantly different patterns of PD progression. The inclusion of genetic variants and biomarker data allowed us to associate the established progression clusters with distinct biological mechanisms, such as perturbations in vesicle transport or neuroprotection. Furthermore, we found that patients of identified progression clusters showed significant differences in their responsiveness to symptomatic treatment. Taken together, our work contributes to a better understanding of the heterogeneity encountered when examining and treating patients with PD, and points towards potential biological pathways and genes that could underlie those differences.

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.

Brain Degeneration in Synucleinopathies Based on Analysis of Cognition and Other Nonmotor Features: A Multimodal Imaging Study

BACKGROUND

We aimed to characterize subtypes of synucleinopathies using a clustering approach based on cognitive and other nonmotor data and to explore structural and functional magnetic resonance imaging (MRI) brain differences between identified clusters.

METHODS

Sixty-two patients (n = 6 E46K-SNCA, n = 8 dementia with Lewy bodies (DLB) and n = 48 idiopathic Parkinson's disease (PD)) and 37 normal controls underwent nonmotor evaluation with extensive cognitive assessment. Hierarchical cluster analysis (HCA) was performed on patients' samples based on nonmotor variables. T1, diffusion-weighted, and resting-state functional MRI data were acquired. Whole-brain comparisons were performed.

RESULTS

HCA revealed two subtypes, the mild subtype (n = 29) and the severe subtype (n = 33). The mild subtype patients were slightly impaired in some nonmotor domains (fatigue, depression, olfaction, and orthostatic hypotension) with no detectable cognitive impairment; the severe subtype patients (PD patients, all DLB, and the symptomatic E46K-SNCA carriers) were severely impaired in motor and nonmotor domains with marked cognitive, visual and bradykinesia alterations. Multimodal MRI analyses suggested that the severe subtype exhibits widespread brain alterations in both structure and function, whereas the mild subtype shows relatively mild disruptions in occipital brain structure and function.

CONCLUSIONS

These findings support the potential value of incorporating an extensive nonmotor evaluation to characterize specific clinical patterns and brain degeneration patterns of synucleinopathies.

Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care.

Onset of Postural Instability in Parkinson's Disease Depends on Age rather than Disease Duration

Background

Postural instability and falls are considered a major factor of impaired quality of life in patients with advanced Parkinson's disease (PD). The knowledge of the time at which postural instability occurs will help to provide the evidence required to introduce fall-prevention strategies at the right time in PD.

Objective

To investigate whether postural instability of patients with different age at disease onset is associated with age or with disease duration of PD.

Methods

Patients diagnosed with sporadic PD between 1991 and 2017 and postural instability (according to the International Parkinson and Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III, item 3.12 postural instability) were included, with strict inclusion criteria including regular follow-ups, agreement on data use, and exclusion of comorbidities affecting the free stand.

Results

Applying these strict inclusion criteria, we included 106 patients. Those younger than 50 years at PD onset took significantly longer to develop postural instability (n = 23 patients, median: 18.4 years) compared with patients with later onset of PD (50-70 years, n = 66, median: 14.2 years, p < 0.001; and >70 years, n = 17, median: 5.7 years, p < 0.001, Kruskal-Wallis test followed by Dunn's multiple comparisons test). There was no association between total MDS-UPDRS III (as a measure of motor symptom severity) at onset of postural instability.

Conclusions

In PD, postural instability is primarily associated with the age of the patient and not with disease duration.

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.

Subtyping of early-onset Parkinson's disease using cluster analysis: A large cohort study

BACKGROUND

Increasing evidence suggests that early-onset Parkinson's disease (EOPD) is heterogeneous in its clinical presentation and progression. Defining subtypes of EOPD is needed to better understand underlying mechanisms, predict disease course, and eventually design more efficient personalized management strategies.

OBJECTIVE

To identify clinical subtypes of EOPD, assess the clinical characteristics of each EOPD subtype, and compare the progression between EOPD subtypes.

MATERIALS AND METHODS

A total of 1,217 patients were enrolled from a large EOPD cohort of the Parkinson's Disease & Movement Disorders Multicenter Database and Collaborative Network in China (PD-MDCNC) between January 2017 and September 2021. A comprehensive spectrum of motor and non-motor features were assessed at baseline. Cluster analysis was performed using data on demographics, motor symptoms and signs, and other non-motor manifestations. In 454 out of total patients were reassessed after a mean follow-up time of 1.5 years to compare progression between different subtypes.

RESULTS

Three subtypes were defined: mild motor and non-motor dysfunction/slow progression, intermediate and severe motor and non-motor dysfunction/malignant. Compared to patients with mild subtype, patients with the severe subtype were more likely to have rapid eye movement sleep behavior disorder, wearing-off, and dyskinesia, after adjusting for age and disease duration at baseline, and showed a more rapid progression in Unified Parkinson's Disease Rating Scale (UPDRS) total score (P = 0.002), UPDRS part II (P = 0.014), and III (P = 0.001) scores, Hoehn and Yahr stage (P = 0.001), and Parkinson's disease questionnaire-39 item version score (P = 0.012) at prospective follow-up.

CONCLUSION

We identified three different clinical subtypes (mild, intermediate, and severe) using cluster analysis in a large EOPD cohort for the first time, which is important for tailoring therapy to individuals with EOPD.

Stability of motor-nonmotor subtype in early-stage Parkinson’s disease

Background

The different clinical characteristics and prognostic values of the motor-nonmotor subtypes of Parkinson’s disease (PD) have been established by previous studies. However, the consistency of motor-nonmotor subtypes in patients with early-stage Parkinson’s disease required further investigation. The present study aimed to evaluate the consistency of motor-nonmotor subtypes across five years of follow-up in a longitudinal cohort.

Materials and methods

Patients were classified into different subtypes (mild-motor–predominant, intermediate, diffuse malignant; or tremor-dominant, indeterminate, postural instability and gait difficulty) according to previously verified motor-nonmotor and motor subtyping methods at baseline and at every year of follow-up. The agreement between subtypes was examined using Cohen’s kappa and total agreement. The determinants of having the diffuse malignant subtype as of the fifth-year visit were explored using logistic regression.

Results

A total of 421 patients were included. There was a fair degree of agreement between the baseline motor-nonmotor subtype and the subtype recorded at the one-year follow-up visit (κ = 0.30 ± 0.09; total agreement, 60.6%) and at following years’ visits. The motor-nonmotor subtype had a lower agreement between baseline and follow-up than did the motor subtype. The baseline motor-nonmotor subtype was the determinant of diffuse malignant subtype at the fifth-year visit.

Conclusion

Many patients experienced a change in their motor-nonmotor subtype during follow-up. Further studies of consistency in PD subtyping methods should be conducted in the future.

Genetically-informed prediction of short-term Parkinson's disease progression

Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Genetic architecture of Parkinson’s disease subtypes – Review of the literature

The heterogeneity of Parkinson’s disease (PD) has been recognized since its description by James Parkinson over 200 years ago. The complexity of motor and non-motor PD manifestations has led to many attempts of PD subtyping with different prognostic outcomes; however, the pathophysiological foundations of PD heterogeneity remain elusive. Genetic contributions to PD may be informative in understanding the underpinnings of PD subtypes. As such, recognizing genotype-phenotype associations may be crucial for successful gene therapy. We review the state of knowledge on the genetic architecture underlying PD subtypes, discussing the monogenic forms, as well as oligo- and polygenic risk factors associated with various PD subtypes. Based on our review, we argue for the unification of PD subtyping classifications, the dichotomy of studies on genetic factors and genetic modifiers of PD, and replication of results from previous studies.

Early Parkinson's Disease Phenotypes Tailored by Personality, Behavior, and Motor Symptoms

BACKGROUND

Previous studies described a parkinsonian personality characterized as rigid, introverted, and cautious; however, little is known about personality traits in de novo Parkinson's disease (PD) patients and their relationships with motor and neuropsychiatric symptoms.

OBJECTIVE

To investigate personality in de novo PD and explore its relationship with PD symptoms.

METHODS

Using Cloninger's biosocial model, we assessed personality in 193 de novo PD patients. Motor and non-motor symptoms were measured using several validated scales. Cluster analysis was conducted to investigate the interrelationship of personality traits, motor, and non-motor symptoms.

RESULTS

PD patients showed low novelty seeking, high harm avoidance, and normal reward dependence and persistence scores. Harm avoidance was positively correlated with the severity of depression, anxiety, and apathy (rs = [0.435, 0.676], p < 0.001) and negatively correlated with quality of life (rs = -0.492, p < 0.001). Novelty seeking, reward dependence, and persistence were negatively correlated with apathy (rs = [-0.274, -0.375], p < 0.001). Classification of patients according to personality and PD symptoms revealed 3 distinct clusters: i) neuropsychiatric phenotype (with high harm avoidance and low novelty seeking, hypodopaminergic neuropsychiatric symptoms and higher impulsivity), ii) motor phenotype (with low novelty seeking and higher motor severity), iii) benign phenotype (with low harm avoidance and high novelty seeking, reward dependence, and persistence traits clustered with lower symptoms severity and low impulsivity).

CONCLUSION

Personality in early PD patients allows us to recognize 3 patients' phenotypes. Identification of such subgroups may help to better understand their natural history. Their longitudinal follow-up will allow confirming whether some personality features might influence disease evolution and treatment.

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.

A composite clinical motor score as a comprehensive and sensitive outcome measure for Parkinson's disease

BACKGROUND

An unmet need remains for sensitive outcome measures in neuroprotective trials. The study aims to determine whether a composite clinical motor score, combining the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III motor examination score, Purdue Pegboard Test, and Timed Up and Go, provides greater sensitivity in detecting motor change in early disease than the MDS-UPDRS III alone.

METHODS

The Oxford Discovery longitudinal cohort study involves individuals with isolated rapid eye movement sleep behaviour disorder (iRBD) (n=272, confirmed polysomnographically, median follow-up: 1.6 years), idiopathic Parkinson's disease (PD) (n=909, median follow-up: 3.5 years, baseline: <3.5 years disease duration) and controls (n=316, age-matched and sex-matched, without a first-degree family history of PD). Motor and non-motor assessments were performed at each in-person visit.

RESULTS

Compared with the MDS-UPDRS III, the composite clinical motor score demonstrated a wider score distribution in iRBD and controls, lower coefficient of variation (37% vs 67%), and higher correlation coefficients with self-reported measures of motor severity (0.65 vs 0.61) and overall health status (-0.40 vs -0.33). Greater score range in mild to moderate PD, higher magnitude of longitudinal change in iRBD and longitudinal score linearity suggest better sensitivity in detecting subtle motor change. The composite clinical motor score was more accurate than the MDS-UPDRS III in predicting clinical outcomes, requiring 64% fewer participants with PD and 51% fewer participants with iRBD in sample size estimations for a hypothetical 18-month placebo-controlled clinical trial.

CONCLUSION

The composite clinical motor score may offer greater consistency and sensitivity in detecting change than the MDS-UPDRS III.

Statistical power for cluster analysis

BACKGROUND

Cluster algorithms are gaining in popularity in biomedical research due to their compelling ability to identify discrete subgroups in data, and their increasing accessibility in mainstream software. While guidelines exist for algorithm selection and outcome evaluation, there are no firmly established ways of computing a priori statistical power for cluster analysis. Here, we estimated power and classification accuracy for common analysis pipelines through simulation. We systematically varied subgroup size, number, separation (effect size), and covariance structure. We then subjected generated datasets to dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection) and cluster algorithms (k-means, agglomerative hierarchical clustering with Ward or average linkage and Euclidean or cosine distance, HDBSCAN). Finally, we directly compared the statistical power of discrete (k-means), "fuzzy" (c-means), and finite mixture modelling approaches (which include latent class analysis and latent profile analysis).

RESULTS

We found that clustering outcomes were driven by large effect sizes or the accumulation of many smaller effects across features, and were mostly unaffected by differences in covariance structure. Sufficient statistical power was achieved with relatively small samples (N = 20 per subgroup), provided cluster separation is large (Δ = 4). Finally, we demonstrated that fuzzy clustering can provide a more parsimonious and powerful alternative for identifying separable multivariate normal distributions, particularly those with slightly lower centroid separation (Δ = 3).

CONCLUSIONS

Traditional intuitions about statistical power only partially apply to cluster analysis: increasing the number of participants above a sufficient sample size did not improve power, but effect size was crucial. Notably, for the popular dimensionality reduction and clustering algorithms tested here, power was only satisfactory for relatively large effect sizes (clear separation between subgroups). Fuzzy clustering provided higher power in multivariate normal distributions. Overall, we recommend that researchers (1) only apply cluster analysis when large subgroup separation is expected, (2) aim for sample sizes of N = 20 to N = 30 per expected subgroup, (3) use multi-dimensional scaling to improve cluster separation, and (4) use fuzzy clustering or mixture modelling approaches that are more powerful and more parsimonious with partially overlapping multivariate normal distributions.

Diagnostic value of cerebrospinal fluid alpha-synuclein seed quantification in synucleinopathies

Several studies have confirmed the α-synuclein real-time quaking-induced conversion (RT-QuIC) assay to have high sensitivity and specificity for Parkinson's disease. However, whether the assay can be used as a robust, quantitative measure to monitor disease progression, stratify different synucleinopathies and predict disease conversion in patients with idiopathic REM sleep behaviour disorder remains undetermined. The aim of this study was to assess the diagnostic value of CSF α-synuclein RT-QuIC quantitative parameters in regard to disease progression, stratification and conversion in synucleinopathies. We performed α-synuclein RT-QuIC in the CSF samples from 74 Parkinson's disease, 24 multiple system atrophy and 45 idiopathic REM sleep behaviour disorder patients alongside 55 healthy controls, analysing quantitative assay parameters in relation to clinical data. α-Synuclein RT-QuIC showed 89% sensitivity and 96% specificity for Parkinson's disease. There was no correlation between RT-QuIC quantitative parameters and Parkinson's disease clinical scores (e.g. Unified Parkinson's Disease Rating Scale motor), but RT-QuIC positivity and some quantitative parameters (e.g. Vmax) differed across the different phenotype clusters. RT-QuIC parameters also added value alongside standard clinical data in diagnosing Parkinson's disease. The sensitivity in multiple system atrophy was 75%, and CSF samples showed longer T50 and lower Vmax compared to Parkinson's disease. All RT-QuIC parameters correlated with worse clinical progression of multiple system atrophy (e.g. change in Unified Multiple System Atrophy Rating Scale). The overall sensitivity in idiopathic REM sleep behaviour disorder was 64%. In three of the four longitudinally followed idiopathic REM sleep behaviour disorder cohorts, we found around 90% sensitivity, but in one sample (DeNoPa) diagnosing idiopathic REM sleep behaviour disorder earlier from the community cases, this was much lower at 39%. During follow-up, 14 of 45 (31%) idiopathic REM sleep behaviour disorder patients converted to synucleinopathy with 9/14 (64%) of convertors showing baseline RT-QuIC positivity. In summary, our results showed that α-synuclein RT-QuIC adds value in diagnosing Parkinson's disease and may provide a way to distinguish variations within Parkinson's disease phenotype. However, the quantitative parameters did not correlate with disease severity in Parkinson's disease. The assay distinguished multiple system atrophy patients from Parkinson's disease patients and in contrast to Parkinson's disease, the quantitative parameters correlated with disease progression of multiple system atrophy. Our results also provided further evidence for α-synuclein RT-QuIC having potential as an early biomarker detecting synucleinopathy in idiopathic REM sleep behaviour disorder patients prior to conversion. Further analysis of longitudinally followed idiopathic REM sleep behaviour disorder patients is needed to better understand the relationship between α-synuclein RT-QuIC signature and the progression from prodromal to different synucleinopathies.

Mechanistic Insights Into Gut Microbiome Dysbiosis-Mediated Neuroimmune Dysregulation and Protein Misfolding and Clearance in the Pathogenesis of Chronic Neurodegenerative Disorders

The human gut microbiota is a complex, dynamic, and highly diverse community of microorganisms. Beginning as early as in utero fetal development and continuing through birth to late-stage adulthood, the crosstalk between the gut microbiome and brain is essential for modulating various metabolic, neurodevelopmental, and immune-related pathways. Conversely, microbial dysbiosis – defined as alterations in richness and relative abundances – of the gut is implicated in the pathogenesis of several chronic neurological and neurodegenerative disorders. Evidence from large-population cohort studies suggests that individuals with neurodegenerative conditions have an altered gut microbial composition as well as microbial and serum metabolomic profiles distinct from those in the healthy population. Dysbiosis is also linked to psychiatric and gastrointestinal complications – comorbidities often associated with the prodromal phase of Parkinson’s disease (PD) and Alzheimer’s disease (AD). Studies have identified potential mediators that link gut dysbiosis and neurological disorders. Recent findings have also elucidated the potential mechanisms of disease pathology in the enteric nervous system prior to the onset of neurodegeneration. This review highlights the functional pathways and mechanisms, particularly gut microbe-induced chronic inflammation, protein misfolding, propagation of disease-specific pathology, defective protein clearance, and autoimmune dysregulation, linking gut microbial dysbiosis and neurodegeneration. In addition, we also discuss how pathogenic transformation of microbial composition leads to increased endotoxin production and fewer beneficial metabolites, both of which could trigger immune cell activation and enteric neuronal dysfunction. These can further disrupt intestinal barrier permeability, aggravate the systemic pro-inflammatory state, impair blood–brain barrier permeability and recruit immune mediators leading to neuroinflammation and neurodegeneration. Continued biomedical advances in understanding the microbiota-gut-brain axis will extend the frontier of neurodegenerative disorders and enable the utilization of novel diagnostic and therapeutic strategies to mitigate the pathological burden of these diseases.

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.

Serum Neurofilament Light Chain as a Marker of Progression in Parkinson's Disease: Long-Term Observation and Implications of Clinical Subtypes

BACKGROUND

Biochemical and clinical biomarkers correlate with progression rate and disease severity in Parkinson's disease (PD) but are not sufficiently studied in late PD.

OBJECTIVE

To examine how serum neurofilament light chain (S-NfL) alone or combined with clinical classifications predicts PD outcome in later disease stages.

METHODS

Eighty-five patients with 7.9±5.1 years of PD duration were included in an observational cohort. Clinical scores were obtained at two separate examinations 8.2±2.0 years apart. S-NfL levels were determined with single molecule array (SiMoA). Five predefined disease progression milestones were assessed. After affirming combination potential of S-NfL and either of two clinical classifications, three combined models were constructed based on these factors and age at onset in different combinations.

RESULTS

S-NfL levels showed significant hazard ratios for four out of five disease progression milestones: walking-aid usage (HR 3.5; 95% CI 1.4-8.5), nursing home living (5.1; 2.1-12.5), motor end-stage (6.2; 2.1-17.8), and death (4.1; 1.7-9.7). Higher S-NfL levels were associated with lower ability in activities of daily living and poorer cognition at baseline and/or at follow-up. Combined models showed significantly improved area under receiver operating characteristic curves (0.77-0.91) compared to S-NfL levels alone (0.68-0.71) for predicting the five disease milestones.

CONCLUSION

S-NfL levels stratified patients according to their likelihood to reach clinically relevant progression milestones during this long-term observational study. S-NfL alone reflected motor and social outcomes in later stages of PD. Combining S-NfL with clinical factors was possible and exploratory combined models improved prognostic accuracy.

Correlation between head tremble and the severity of Parkinson's disease

Introduction

Parkinson's (PD) is a common degenerative disease of the central nervous system. It affects more than 6 million individuals worldwide. The typical clinical manifestations include static tremor, slow movement, and unstable posture. However, the correlation between head tremor and the severity of PD remains unclear.

Methods

In the current study, 18 patients and 18 healthy subjects were recruited to undergo a phonation test. Noldus facereader 7.0 software was used to analyze the range of head trembling between the two groups.

Results

The data revealed that patients with PD had significant differences in the x‐, y‐, and z‐axis of head movement with respect to the specific pronunciation syllables compared with the normal group. Moreover, the head movement of the patients with PD was positively correlated with the severity of the disease in the single, double, and multiple syllable tests. In the phonetic test, the head displacement of patients with PD was significantly greater than that of healthy individuals, and the displacement range was positively correlated with the severity of the disease.

Conclusion

These pieces of evidence suggested that the measurement of head displacement assists the early diagnosis and severity of the disease.

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.

New therapeutic approaches to Parkinson's disease targeting GBA, LRRK2 and Parkin

Parkinson's disease (PD) is defined as a complex disorder with multifactorial pathogenesis, yet a more accurate definition could be that PD is not a single entity, but rather a mixture of different diseases with similar phenotypes. Attempts to classify subtypes of PD have been made based on clinical phenotypes or biomarkers. However, the most practical approach, at least for a portion of the patients, could be to classify patients based on genes involved in PD. GBA and LRRK2 mutations are the most common genetic causes or risk factors of PD, and PRKN is the most common cause of autosomal recessive form of PD. Patients carrying variants in GBA, LRRK2 or PRKN differ in some of their clinical characteristics, pathology and biochemical parameters. Thus, these three PD-associated genes are of special interest for drug development. Existing therapeutic approaches in PD are strictly symptomatic, as numerous clinical trials aimed at modifying PD progression or providing neuroprotection have failed over the last few decades. The lack of precision medicine approach in most of these trials could be one of the reasons why they were not successful. In the current review we discuss novel therapeutic approaches targeting GBA, LRRK2 and PRKN and discuss different aspects related to these genes and clinical trials.

Comprehensive subtyping of Parkinson's disease patients with similarity fusion: a case study with BioFIND data

Parkinson’s disease (PD) is a complex neurodegenerative disorder with diverse clinical manifestations. To better understand this disease, research has been done to categorize, or subtype, patients, using an array of criteria derived from clinical assessments and biospecimen analyses. In this study, using data from the BioFIND cohort, we aimed at identifying subtypes of moderate-to-advanced PD via comprehensively considering motor and non-motor manifestations. A total of 103 patients were included for analysis. Through the use of a patient-wise similarity matrix fusion technique and hierarchical agglomerative clustering analysis, three unique subtypes emerged from the clustering results. Subtype I, comprised of 60 patients (~58.3%), was characterized by mild symptoms, both motor and non-motor. Subtype II, comprised of 20 (~19.4%) patients, was characterized by an intermediate severity, with a high tremor score and mild non-motor symptoms. Subtype III, comprised of 23 (~22.3%) patients, was characterized by more severe motor and non-motor symptoms. These subtypes show statistically significant differences when looking at motor (on and off medication) clinical features and non-motor clinical features, while there was no clear difference in demographics, biomarker levels, and genetic risk scores.

Longitudinal Changes in Parkinson's Disease Symptoms with and Without Rapid Eye Movement Sleep Behavior Disorder: The Oxford Discovery Cohort Study

BACKGROUND

Parkinson's disease (PD) comorbid with rapid eye movement sleep behavior disorder (RBD) may show more severe motor and nonmotor symptoms, suggesting a distinct PD subtype.

OBJECTIVE

The aim of this study was to investigate the impact of RBD on the longitudinal change of motor and nonmotor symptoms in patients with PD.

METHODS

Patients with early PD (diagnosed within 3.5 years) recruited from 2010 to 2019 were followed every 18 months in the Oxford Parkinson's Disease Centre Discovery cohort. At each visit, we used standard questionnaires and measurements to assess demographic features and motor and nonmotor symptoms (including RBD, daytime sleepiness, mood, autonomic symptoms, cognition, and olfaction). Data were analyzed with linear mixed effects and Cox regression models. Possible RBD (pRBD) was longitudinally determined according to RBD Screening Questionnaire scores.

RESULTS

A total of 923 patients were recruited (mean age: 67.1 ± 9.59 years; 35.9% female), and 788 had follow-up assessment(s) (mean: 4.8 ± 1.98 years, range: 1.3-8.3). Among them, 33.3% were identified as pRBD (PD + pRBD). Patients with PD + pRBD had more severe baseline symptoms and showed faster progression on Movement Disorder Society-Unified Parkinson's Disease Rating Scale parts I and III, Purdue Pegboard test, and Beck Depression Inventory scores. Moreover, PD + pRBD was associated with an increased level of risk for mild cognitive impairment (hazard ratio [HR] = 1.36, 95% confidence interval [CI]: 1.01-1.83), freezing of gait (HR = 1.42, 95% CI: 1.10-1.86), and frequent falling (HR = 1.62, 95% CI: 1.02-2.60).

CONCLUSIONS

Patients with PD + pRBD progress faster on motor, mood, and cognitive symptoms, confirming a more aggressive PD subtype that can be identified at baseline and has major clinical implications. © 2021 International Parkinson and Movement Disorder Society.

Intrasubject relationship between striatal 18F-FP-CIT uptake and cardiac 123I-MIBG uptake differs by motor subtype in early Parkinson disease

Parkinson disease (PD) is a heterogeneous neurodegenerative disorder. Dopamine transporter imaging using 123I-2β-carbomethoxy-3β-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane (FP-CIT) and noradrenergic cardiac imaging using 123I-meta-iodobenzylguanidine (MIBG) have been used in combination or separately to study PD patients. Published results regarding uptake of the 2 tracers in each motor subtype are fairly abundant and mostly in agreement. However, data on the intrasubject association between dopaminergic and noradrenergic systems in PD patients are relatively scant and vary. We aimed to assess the intrasubject relationship between striatal dopamine transporter density using a PET tracer and cardiac sympathetic innervation in tremor-dominant subtype (TD) and akinetic-rigid subtype (AR) of PD.This study has a cross-sectional design. Thirty-one patients with early PD (17 TD/14 AR) who underwent both 123I-MIBG cardiac scintigraphy and 18F-FP-CIT PET/CT were retrospectively selected. We assessed the relationship between heart-to-mediastinum ratio (H/M) of 123I-MIBG and specific (striatal)-to-nonspecific (cerebellar) dopamine transporter binding ratio (S/N) measured from 4 separate regions-of-interest (bilateral caudate nuclei and lentiform nuclei) of 18F-FP-CIT in each motor subtype.S/N of all 4 striatal regions were significantly lower in the AR subgroup than in the TD subgroup. H/M was not significantly different. There was a significant intrasubject correlation between H/M and S/N of the lentiform nucleus in AR-PD but no correlation between H/M and any of 4 S/N in TD-PD.Our data suggest a coupled degeneration of nigrostriatal dopaminergic and myocardial sympathetic denervation in AR subtype, but not in TD subtype, of early PD patients. These different results between the 2 motor subtypes likely reflects the heterogeneous pathophysiology of PD.

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.

Serum lipid profile among sporadic and familial forms of Parkinson's disease

Brain cholesterol metabolism has been described as altered in Parkinson's disease (PD) patients. Serum lipid levels have been widely studied in PD with controversial results among different populations and age groups. The present study is aimed at determining if the serum lipid profile could be influenced by the genetic background of PD patients. We included 403 PD patients (342 sporadic PD patients, 30 GBA-associated PD patients, and 31 LRRK2-associated PD patients) and 654 healthy controls (HCs). Total cholesterol, HDL, LDL, and triglycerides were measured in peripheral blood. Analysis of covariance adjusting for sex and age (ANCOVA) and post hoc tests were applied to determine the differences within lipid profiles among the groups. Multivariate ANCOVA revealed significant differences among the groups within cholesterol and LDL levels. GBA-associated PD patients had significantly lower levels of total cholesterol and LDL compared to LRRK2-associated PD patients and HCs. The different serum cholesterol levels in GBA-associated PD might be related to diverse pathogenic mechanisms. Our results support the hypothesis of lipid metabolism disruption as one of the main PD pathogenic mechanisms in patients with GBA-associated PD. Further studies would be necessary to explore their clinical implications.

Gait Analysis by Causal Decomposition

Recent studies have investigated bilateral gaits based on the causality analysis of kinetic (or kinematic) signals recorded using both feet. However, these approaches have not considered the influence of their simultaneous causation, which might lead to inaccurate causality inference. Furthermore, the causal interaction of these signals has not been investigated within their frequency domain. Therefore, in this study we attempted to employ a causal-decomposition approach to analyze bilateral gait. The vertical ground reaction force (VGRF) signals of Parkinson's disease (PD) patients and healthy control (HC) individuals were taken as an example to illustrate this method. To achieve this, we used ensemble empirical mode decomposition to decompose the left and right VGRF signals into intrinsic mode functions (IMFs) from the high to low frequency bands. The causal interaction strength (CIS) between each pair of IMFs was then assessed through the use of their instantaneous phase dependency. The results show that the CISes between pairwise IMFs decomposed in the high frequency band of VGRF signals can not only markedly distinguish PD patients from HC individuals, but also found a significant correlation with disease progression, while other pairwise IMFs were not able to produce this. In sum, we found for the first time that the frequency specific causality of bilateral gait may reflect the health status and disease progression of individuals. This finding may help to understand the underlying mechanisms of walking and walking-related diseases, and offer broad applications in the fields of medicine and engineering.

The inconsistency and instability of Parkinson's disease motor subtypes

INTRODUCTION

Tremor-dominant (TD), indeterminate/mixed (ID/M) and postural instability gait difficulty/akinetic-rigid (PIGD/AR) are commonly used subtypes to categorize Parkinson's disease (PD) patients based on their most prominent motor signs. Three different algorithms to determine these motor subtypes are used. Here, we examined if PD subtypes are consistent among algorithms and if subtype stability over time depends on the applied algorithm.

METHODS

Using a large longitudinal PD database, we applied 3 published algorithms of PD motor subtype classification in two sets of analyses: 1) cross-sectional analysis in 1185 patients, determining the prevalence of subtypes in 5-year intervals of disease duration; 2) longitudinal analysis of 178 patients, comparing subtypes of individual patients at baseline (within 5 years of diagnosis) and at follow-up ≥ 5 years after baseline.

RESULTS

Cross-sectionally, prevalence of subtypes varied widely among the 3 algorithms: 5-32% TD, 9-31% ID/M, and 59-75% PIGD/AR. For all 3 algorithms, cross-sectional analysis showed a marked decline of TD prevalence with disease duration and a corresponding increase in PIGD/AR prevalence, driven by increasing gait/balance scores over time. Longitudinally, only 15-36% of baseline TD patients were still categorized as TD at 6.2 ± 1.0 years of follow-up. In 15-39% of baseline TD patients, the subtype changed to ID/M, and 46-50% changed to PIGD/AR. This shift was observed using all 3 algorithms.

CONCLUSION

PD motor subtypes determined by different established algorithms are inconsistent and unstable over time. Lack of subtype fidelity should be considered when interpreting biomarker-subtype correlation and highlights the need for better definition of PD subtypes.

Levodopa-Based Changes on Vocalic Speech Movements during Prosodic Prominence Marking

The present study investigates speech changes in Parkinson’s disease on the acoustic and articulatory level with respect to prosodic prominence marking. To display movements of the underlying articulators, speech data from 16 patients with Parkinson’s disease were recorded using electromagnetic articulography. Speech tasks focused on strategies of prominence marking. Patients’ ability to encode prominence in the laryngeal and supra-laryngeal domain is tested in two conditions to examine the influence of motor performance on speech production further: without dopaminergic medication and with dopaminergic medication. The data reveal that patients with Parkinson’s disease are able to highlight important information in both conditions. They maintain prominence relations across- and within-accentuation by adjusting prosodic markers, such as vowel duration and pitch modulation, while the acoustic vowel space remains the same. For differentiating across-accentuation, not only intensity but also all temporal and spatial parameters related to the articulatory tongue body movements during the production of vowels are modulated to signal prominence. In response to the levodopa intake, gross motor performance improved significantly by 42%. The improvement in gross motor performance was accompanied by an improvement in speech motor performance in terms of louder speech and shorter, larger and faster tongue body movements. The tongue body is more agile under levodopa increase, a fact that is not necessarily detectable on the acoustic level but important for speech therapy.