Comparing postural instability and gait disorder and akinetic‐rigid subtyping of Parkinson disease and their stability over time

Association of free-water imaging data for the cholinergic nucleus with the motor function and subtypes in Parkinson's disease

Background

Despite the importance of clinical heterogeneity in Parkinson's disease (PD), its underlying pathophysiology remains unclear.

Objective

This study aimed to distinguish the association of free-water (FW) imaging data for the cholinergic nuclei with the motor subtypes of PD.

Methods

The study included 150 cases of idiopathic PD from the Parkinson's Progression Markers Initiative cohort. FW imaging, including FW-corrected diffusion tensor imaging, was used to extract structural metrics from cholinergic nucleus 4 (Ch4) in the basal forebrain and the pedunculopontine nucleus. The motor subtypes were classified as tremor-dominant (TD, n = 99) and non-tremor-dominant (non-TD, n = 51). Statistical analyses were performed at baseline and the 4-year follow-up.

Results

At baseline, FW value for Ch4 (FW-Ch4) was correlated with the tremor subscore, while FW-corrected fractional anisotropy in Ch4 (FA-t-Ch4) was negatively correlated with the rigidity subscore. However, the TD and non-TD groups showed no differences in cholinergic FW imaging data. Among the 84 patients who were followed-up, 36.36% (20/55) in the TD group and 34.48% (10/29) in the non-TD group showed a subtype shift after 4 years. Multivariate binary logistic regression analysis showed that the normalized FW value for Ch4 (nFW-Ch4) was a predictor of subtype at the 4-year follow-up (p = 0.041). In the TD subgroup, both nFW-Ch4 (p = 0.015) and normalized FW-corrected mean diffusivity in Ch4 (MD-t-Ch4) (p = 0.013) predicted subtype stability. The area under the receiver operating characteristic curve values were 0.69 and 0.73, respectively.

Conclusion

Tremor and rigidity subscores were correlated with Ch4 FW imaging data. Moreover, Ch4 FW imaging predicted the motor subtype at the 4-year follow-up, especially identifying potential postural instability and gait difficulty (PIGD) subtype converters from the TD group.

Differential gait features across Parkinson's disease clinical subtypes

BACKGROUND

Clinical subtypes in Parkinson's disease including non-motor manifestations may be more beneficial than subtypes based upon motor manifestations alone. Inclusion of gait metrics may help identity targets for rehabilitation and potentially predict development of non-motor symptoms for individuals with Parkinson's disease. This study aims to characterize gait differences across established multi-domain subtypes.

METHODS

"Motor Only", "Psychiatric & Motor" and "Cognitive & Motor" clinical subtypes were established through motor, cognitive, and psychiatric assessment. Walking was assessed in the "OFF" medication state. Multivariate analysis of variance identified differences in gait domains across clinical subtypes.

FINDINGS

The "Motor Only" subtype exhibited the fastest velocity, longest step length, and least timing variability (swing, step, stance), compared to "Psychiatric & Motor" and "Cognitive & Motor" subtypes. Stance time differed across subtypes; "Psychiatric & Motor" subtype had the longest stance time, followed by "Cognitive & Motor", then "Motor only". The "Psychiatric & Motor" group had different asymmetry from the "Cognitive & Motor" subtype, as "Psychiatric & Motor" walked with longer steps on their less-affected side while the "Cognitive & Motor" subtype displayed the opposite pattern. No differences were observed for swing time, step velocity variability, step length variability, width measures, or other asymmetry measures.

INTERPRETATION

Cognitive and Psychiatric subtypes displayed worse gait performance than the "Motor only" group. Stance time and step length asymmetry were different between Psychiatric and Cognitive subtypes, indicating gait deficits may be related to distinct aspects of non-motor manifestations. Gait signatures may help clinicians distinguish between non-motor subtypes, guiding personalized treatment.

Transcriptomics profiling of Parkinson's disease progression subtypes reveals distinctive patterns of gene expression

Background

Parkinson's Disease (PD) varies widely among individuals, and Artificial Intelligence (AI) has recently helped to identify three disease progression subtypes. While their clinical features are already known, their gene expression profiles remain unexplored.

Objectives

The objectives of this study were (1) to describe the transcriptomics characteristics of three PD progression subtypes identified by AI, and (2) to evaluate if gene expression data can be used to predict disease subtype at baseline.

Design

This is a retrospective longitudinal cohort study utilizing the Parkinson's Progression Markers Initiative (PPMI) database.

Methods

Whole blood RNA-Sequencing data underwent differential gene expression analysis, followed by multiple pathway analyses. A Machine Learning (ML) classifier, namely XGBoost, was trained using data from multiple modalities, including gene expression values.

Results

Our study identified differentially expressed genes (DEGs) that were uniquely associated with Parkinson's disease (PD) progression subtypes. Importantly, these DEGs had not been previously linked to PD. Gene-pathway analysis revealed both distinct and shared characteristics between the subtypes. Notably, two subtypes displayed opposite expression patterns for pathways involved in immune response alterations. In contrast, the third subtype exhibited a more unique profile characterized by increased expression of genes related to detoxification processes. All three subtypes showed a significant modulation of pathways related to the regulation of gene expression, metabolism, and cell signaling. ML revealed that the progression subtype with the worst prognosis can be predicted at baseline with 0.877 AUROC, yet the contribution of gene expression was marginal for the prediction of the subtypes.

Conclusion

This study provides novel information regarding the transcriptomics profiles of PD progression subtypes, which may foster precision medicine with relevant indications for a finer-grained diagnosis and prognosis.

The added value of anosmic subtype on motor subtype in Parkinson's disease: a pilot study

This study investigates whether incorporating olfactory dysfunction into motor subtypes of Parkinson's disease (PD) improves associations with clinical outcomes. PD is commonly divided into motor subtypes, such as postural instability and gait disturbance (PIGD) and tremor-dominant PD (TDPD), but non-motor symptoms like olfactory dysfunction remain underexplored. We assessed 157 participants with PD using the University of Pennsylvania Smell Identification Test (UPSIT), Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (M-UPDRS), Montreal Cognitive Assessment (MoCA), 39-item Parkinson's Disease Questionnaire Summary Index (PDQ-39 SI), and 99mTc-TRODAT-1 imaging. Motor subtypes were categorized as PIGD and TDPD, and olfactory subtypes were categorized as total anosmia (TA) and non-anosmia (NA). Significant differences were observed, with the highest disease burden occurring in PIGD TA, while the lowest occurred in TDPD NA. The total M-UPDRS scores (59.0, 47.5, 43.0, 36.0; p < 0.001) and PDQ-39 SI scores (22.4, 22.8, 9.6, and 9.0; p < 0.001) varied significantly across groups, and the highest occurred for PIGD TA, followed by PIGD NA, TDPD TA, and TDPD NA. MoCA scores indicated the best cognitive performance in TDPD NA (p = 0.002). Thus, the results show that integrating olfactory dysfunction with motor subtypes may enhance PD classification, particularly in cognitive assessment in cases of TDPD.

Grey matter volume differences across Parkinson's disease motor subtypes in the supplementary motor cortex

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disease worldwide due to loss of dopaminergic neurons projecting from the basal ganglia (BG). It is associated with various motor symptoms that are grouped into subtypes, each with different clinical presentations and disease progressions. Neuroimaging biomarkers focusing on regions a part of motor circuits projecting from the BG can distinguish and improve overall subtyping. The supplementary motor cortex (SMC) is well established in PD neuropathology and associated with freezing of gait and bradykinesia, but has not been thoroughly evaluated across subtypes. This study aims to identify volumetric differences of the SMC based on PD subtypes of tremor dominant (TD), postural instability with gait difficulty (PIGD), and akinetic rigid (AR) using data from Parkinson's Progression Markers Initiative. To segment grey matter volume and extract region of interest values, voxel-based processing was used. Multi-factor ANCOVAs, Tukey Honest Significance Test, and Kruskal-Wallis were utilized for volumetric analyses (α < 0.05). Subjects were classified and evaluated using TD, PIGD, and AR subtypes from the MDS-UPDRS rating scales. Inter-subtype differences in SMC GMV between TD and PIGD were significant in the right hemisphere for females (p = 0.01). No significant inter-subtype differences were found in the TD/AR system. These results support the use of broader motor networks, specifically the SMC in further understanding the neuropathological heterogeneity of PD. Furthermore, it reveals SMC differences across sexes, subtypes, and subtyping systems, calling for further evaluation of subtyping schemas, specifically regarding sex differences.

Microstructural alterations in white matter and related neurobiology based on the new clinical subtypes of Parkinson's disease

Background and objectives

The advent of new clinical subtyping systems for Parkinson's disease (PD) has led to the classification of patients into distinct groups: mild motor predominant (PD-MMP), intermediate (PD-IM), and diffuse malignant (PD-DM). Our goal was to evaluate the efficacy of diffusion tensor imaging (DTI) in the early diagnosis, assessment of clinical progression, and prediction of prognosis of these PD subtypes. Additionally, we attempted to understand the pathological mechanisms behind white matter damage using single-photon emission computed tomography (SPECT) and cerebrospinal fluid (CSF) analyses.

Methods

We classified 135 de novo PD patients based on new clinical criteria and followed them up after 1 year, along with 45 healthy controls (HCs). We utilized tract-based spatial statistics to assess the microstructural changes of white matter at baseline and employed multiple linear regression to examine the associations between DTI metrics and clinical data at baseline and after follow-up.

Results

Compared to HCs, patients with the PD-DM subtype demonstrated reduced fractional anisotropy (FA), increased axial diffusivity (AD), and elevated radial diffusivity (RD) at baseline. The FA and RD values correlated with the severity of motor symptoms, with RD also linked to cognitive performance. Changes in FA over time were found to be in sync with changes in motor scores and global composite outcome measures. Furthermore, baseline AD values and their rate of change were related to alterations in semantic verbal fluency. We also discovered the relationship between FA values and the levels of α-synuclein and β-amyloid. Reduced dopamine transporter uptake in the left putamen correlated with RD values in superficial white matter, motor symptoms, and autonomic dysfunction at baseline as well as cognitive impairments after 1 year.

Conclusions

The PD-DM subtype is characterized by severe clinical symptoms and a faster progression when compared to the other subtypes. DTI, a well-established technique, facilitates the early identification of white matter damage, elucidates the pathophysiological mechanisms of disease progression, and predicts cognitively related outcomes. The results of SPECT and CSF analyses can be used to explain the specific pattern of white matter damage in patients with the PD-DM subtype.

Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data

Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

Plasma GFAP as a prognostic biomarker of motor subtype in early Parkinson's disease

Parkinson's disease (PD) is a heterogeneous movement disorder with different motor subtypes including tremor dominant (TD), indeterminate and postural instability, and gait disturbance (PIGD) motor subtypes. Plasma glial fibrillary acidic protein (GFAP) was elevated in PD patients and may be regarded as a biomarker for motor and cognitive progression. Here we explore if there was an association between plasma GFAP and different motor subtypes and whether baseline plasma GFAP level can predict motor subtype conversion. Patients with PD classified as TD, PIGD or indeterminate subtypes underwent neurological evaluation at baseline and 2 years follow-up. Plasma GFAP in PD patients and controls were measured using an ultrasensitive single molecule array. The study enrolled 184 PD patients and 95 control subjects. Plasma GFAP levels were significantly higher in the PIGD group compared to the TD group at 2-year follow-up. Finally, 45% of TD patients at baseline had a subtype shift and 85% of PIGD patients at baseline remained as PIGD subtypes at 2 years follow-up. Baseline plasma GFAP levels were significantly higher in TD patients converted to PIGD than non-converters in the baseline TD group. Higher baseline plasma GFAP levels were significantly associated with the TD motor subtype conversion (OR = 1.283, P = 0.033) and lower baseline plasma GFAP levels in PIGD patients were likely to shift to TD and indeterminate subtype (OR = 0.551, P = 0.021) after adjusting for confounders. Plasma GFAP may serve as a clinical utility biomarker in differentiating motor subtypes and predicting baseline motor subtypes conversion in PD patients.

Cortical and subcortical morphological alterations in motor subtypes of Parkinson's disease

Parkinson's disease (PD) can be classified into an akinetic-rigid (AR) and a tremor-dominant (TD) subtype based on predominant motor symptoms. Patients with different motor subtypes often show divergent clinical manifestations; however, the underlying neural mechanisms remain unclear. This study aimed to characterize the cortical and subcortical morphological alterations in motor subtypes of PD. T1-weighted MRI images were obtained for 90 patients with PD (64 with the AR subtype and 26 with the TD subtype) and 56 healthy controls (HCs). Cortical surface area, sulcal depth (measured by Freesurfer's Sulc index), and subcortical volume were computed to identify the cortical and subcortical morphological alterations in the two motor subtypes. Compared with HCs, we found widespread surface area reductions in the AR subtype yet sparse surface area reductions in the TD subtype. We found no significant Sulc change in the AR subtype yet increased Sulc in the right supramarginal gyrus in the TD subtype. The hippocampal volumes in both subtypes were lower than those of HCs. In PD patients, the surface area of left posterior cingulate cortex was positively correlated with Mini-Mental State Examination (MMSE) score, while the Sulc value of right middle frontal gyrus was positively correlated with severity of motor impairments. Additionally, the hippocampal volumes were positively correlated with MMSE and Montreal Cognitive Assessment scores and negatively correlated with severity of motor impairments and Hoehn & Yahr scores. Taken together, these findings may contribute to a better understanding of the neural substrates underlying the distinct symptom profiles in the two PD subtypes.

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.

Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data via hierarchical clustering analysis

Objective

We wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes.

Methods

Eighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups.

Results

Two subtypes of PD were identified. The “diffuse malignant subtype” was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The “mild subtype” was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function.

Conclusion

Hierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.

Parkinsonism and tremor syndromes

Tremor, the most common movement disorder, may occur in isolation or may co-exist with a variety of other neurologic and movement disorders including parkinsonism, dystonia, and ataxia. When associated with Parkinson's disease, tremor may be present at rest or as an action tremor overlapping in phenomenology with essential tremor. Essential tremor may be associated not only with parkinsonism but other neurological disorders, suggesting the possibility of essential tremor subtypes. Besides Parkinson's disease, tremor can be an important feature of other parkinsonian disorders, such as atypical parkinsonism and drug-induced parkinsonism. In addition, tremor can be a prominent feature in patients with other movement disorders such as fragile X-associated tremor/ataxia syndrome, and Wilson's disease in which parkinsonian features may be present. This article is part of the Special Issue "Parkinsonism across the spectrum of movement disorders and beyond" edited by Joseph Jankovic, Daniel D. Truong and Matteo Bologna.

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.

Consistency and Stability of Motor Subtype Classifications in Patients With de novo Parkinson's Disease

Objective

Patients with Parkinson’s disease (PD) are commonly classified into subtypes based on motor symptoms. The aims of the present study were to determine the consistency between PD motor subtypes, to assess the stability of PD motor subtypes over time, and to explore the variables influencing PD motor subtype stability.

Methods

This study was part of a longitudinal study of de novo PD patients at a single center. Based on three different motor subtype classification systems proposed by Jankovic, Schiess, and Kang, patients were respectively categorized as tremor-dominant/indeterminate/postural instability and gait difficulty (TD/indeterminate/PIGD), TD_S/mixed_S/akinetic-rigid_S (ARS), or TD_K/mixed_K/AR_K at baseline evaluation and then re-assessed 1 month later. Demographic and clinical characteristics were recorded at each evaluation. The consistency between subtypes at baseline evaluation was assessed using Cohen’s kappa coefficient (κ). Additional variables were compared between PD subtype groups using the two-sample t-test, Mann–Whitney U-test or Chi-squared test.

Results

Of 283 newly diagnosed, untreated PD patients, 79 were followed up at 1 month. There was fair agreement between the Jankovic, Schiess, and Kang classification systems (κ_S = 0.383 ± 0.044, κ_K = 0.360 ± 0.042, κ_SK = 0.368 ± 0.038). Among the three classification systems, the Schiess classification was the most stable and the Jankovic classification was the most unstable. The non-motor symptoms questionnaire (NMSQuest) scores differed significantly between PD patients with stable and unstable subtypes based on the Jankovic classification (p = 0.008), and patients with a consistent subtype had more severe NMSQuest scores than patients with an inconsistent subtype.

Conclusion

Fair consistency was observed between the Jankovic, Schiess, and Kang classification systems. For the first time, non-motor symptoms (NMSs) scores were found to influence the stability of the TD/indeterminate/PIGD classification. Our findings support combining NMSs with motor symptoms to increase the effectiveness of PD subtypes.

Comparison of Three Motor Subtype Classifications in de novo Parkinson's Disease Patients

Objective: The aims of this study were to compare the characteristics of three motor subtype classifications in patients with de novo Parkinson's disease (PD) and to find the most suitable motor subtype classification for identifying non-motor symptoms (NMSs).

Methods: According to previous studies, a total of 256 patients with de novo PD were classified using the tremor-dominant/mixed/akinetic-rigid (TD/mixed/AR), TD/indeterminate/postural instability and gait disturbance (PIGD), and predominantly TD/predominantly PIGD (p-TD/p-PIGD) classification systems.

Results: Among the TD/mixed/AR subgroups, the patients with the AR subtype obtained more severe motor scores than the patients with the TD subtype. Among the TD/indeterminate/PIGD subgroups and between the p-TD and p-PIGD subgroups, the patients with the PIGD/p-PIGD subtype obtained more severe scores related to activities of daily living (ADL), motor and non-motor symptoms, including depression, anxiety, and sleep impairment, than the patients with the TD/p-TD subtype. Furthermore, symptoms in the cardiovascular, gastrointestinal, and miscellaneous domains of the Non-motor Questionnaire (NMSQuest) were more prevalent in the patients with the PIGD/p-PIGD subtypes than the patients with the TD/p-TD subtypes.

Conclusions: The PIGD/p-PIGD subtypes had more severe ADL, motor and non-motor symptoms than the TD/p-TD subtypes. We disclosed for the first time that the TD/indeterminate/PIGD classification seems to be the most suitable classification among the three motor subtype classifications for identifying NMSs in PD.

Self-perceptions of speech, voice, and swallowing in motor phenotypes of Parkinson's disease

INTRODUCTION

The ability of people with Parkinson's Disease (PWPD) to perceive and identify impairments related to communication and swallowing is often impaired. This impairment prolongs the time to diagnosis of dysphonia and dysphagia, and can delay implementation of speech or swallowing therapy. We have limited knowledge of how different motor phenotypes of PD impact speech, voice and swallowing, nor how PWPD perceive these impacts. The purpose of this study was to identify how perceptions of speech and voice impairments predict dysphagia in PD, and how those perceptions differ between motor phenotypes.

METHODS

38 PWPD completed clinical surveys including V-RQOL, DHI, and a speech, voice, and swallow symptom questionnaire. Participants were categorized as either tremor dominant (TD) or non-tremor dominant (NTD) phenotypes. Multiple regression and MANOVA were utilized to identify predictors of dysphagia perceptions, and for differentiating between motor phenotype based on perceptual severity.

RESULTS

Perceptions of speech and voice impairment predicted perceptions of swallow impairment regardless of phenotype (p < .05, CI = 0.08-0.77). NTD participants reported significantly more communication and swallowing impairments than TD (p < .05) and perceived communication impairment severity was the strongest predictor of group classification (OR = 0.50). The survey battery displayed a robust discriminatory ability between phenotype (AUC = 0.87, CI = 0.76-0.98).

CONCLUSION

The use of a noninvasive and cost-efficient survey battery may be useful in predicting patient perceived swallow impairment in PWPD. Speech, voice, and swallow impairments based on survey responses were found to differ between motor phenotypes.

Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?

Introduction: Parkinson's disease hinders the ability of a person to perform daily activities. However, the varying impact of specific symptoms and their interactions on a person's motor repertoire is not understood. The current study investigates the possibility to predict global motor disabilities based on the patient symptomatology and medication.

Methods: A cohort of 115 patients diagnosed with Parkinson's disease (mean age = 67.0 ± 8.7 years old) participated in the study. Participants performed different tasks, including the Timed-Up & Go, eating soup and the Purdue Pegboard test. Performance on these tasks was judged using timing, number of errors committed, and count achieved. K-means method was used to cluster the overall performance and create different motor performance groups. Symptomatology was objectively assessed for each participant from a combination of wearable inertial sensors (bradykinesia, tremor, dyskinesia) and clinical assessment (rigidity, postural instability). A multinomial regression model was derived to predict the performance cluster membership based on the patients' symptomatology, socio-demographics information and medication.

Results: Clustering exposed four distinct performance groups: normal behavior, slightly affected in fine motor tasks, affected only in TUG, and affected in all areas. The statistical model revealed that low to moderate level of dyskinesia increased the likelihood of being in the normal group. A rise in postural instability and rest tremor increase the chance to be affected in TUG. Finally, LEDD did not help distinguishing between groups, but the presence of Amantadine as part of the medication regimen appears to decrease the likelihood of being part of the groups affected in TUG.

Conclusion: The approach allowed to demonstrate the potential of using clinical symptoms to predict the impact of Parkinson's disease on a person's mobility performance.

The role of disease duration and severity on novel clinical subtypes of Parkinson disease

INTRODUCTION

One of the latest subtyping systems of Parkinson disease (PD) identifies motor severity, cognitive dysfunction, dysautonomia, and rapid eye movement behavior disorder as key features for phenotyping patients into three different subtypes (i.e., mild motor-predominant, diffuse-malignant and intermediate). Since PD subtypes are clinically most relevant if they are mutually exclusive and consistent over-time, we explored the impact of disease stage and duration on these novel subtypes.

METHODS

One-hundred-twenty-two consecutive patients, with a disease duration ranging from 0 to 20 years, were allocated as suggested into these three subtypes. The relationship between either disease duration or stage, as measured by the Hoehn and Yahr staging, and subtype allocation was explored.

RESULTS

Significant differences in subtype distribution were observed across patients stratified according to either disease duration or staging, with the diffuse-malignant subtypes increasing in prevalence as the disease advanced. Both disease duration and staging were independent predictors of subtype allocation.

CONCLUSIONS

These novel PD subtypes are significantly influenced by disease duration and staging, which might suggest that they do not represent mutually exclusive disease pathways. This should be taken into account when attempting correlations with putative biomarkers of disease progression.

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.