Clinical clusters and dopaminergic dysfunction in de-novo Parkinson disease

Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis

The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.

Refining the clinical diagnosis of Parkinson's disease

Our ability to define, understand, and classify Parkinson's disease (PD) has undergone significant changes since the disorder was first described in 1817. Clinical features and neuropathologic signatures can now be supplemented by in-vivo interrogation of genetic and biological substrates of disease, offering great opportunity for further refining the diagnosis of PD. In this mini-review, we discuss the historical perspectives which shaped our thinking surrounding the definition and diagnosis of PD. We highlight the clinical, genetic, pathologic and biologic diversity which underpins the condition, and proceed to discuss how recent developments in our ability to define biologic and pathologic substrates of disease might impact PD definition, diagnosis, individualised prognostication, and personalised clinical care. We argue that Parkinson's 'disease', as currently diagnosed in the clinic, is actually a syndrome. It is the outward manifestation of any array of potential dysfunctional biologic processes, neuropathological changes, and disease aetiologies, which culminate in common outward clinical features which we term PD; each person has their own unique disease, which we can now define with increasing precision. This is an exciting time in PD research and clinical care. Our ability to refine the clinical diagnosis of PD, incorporating in-vivo assessments of disease biology, neuropathology, and neurogenetics may well herald the era of biologically-based, precision medicine approaches PD management. With this however comes a number of challenges, including how to integrate these technologies into clinical practice in a way which is acceptable to patients, promotes meaningful changes to care, and minimises health economic impact.

Genome-wide Analysis of Motor Progression in Parkinson Disease

Background and Objectives

The genetic basis of Parkinson disease (PD) motor progression is largely unknown. Previous studies of the genetics of PD progression have included small cohorts and shown a limited overlap with genetic PD risk factors from case-control studies. Here, we have studied genomic variation associated with PD motor severity and early-stage progression in large longitudinal cohorts to help to define the biology of PD progression and potential new drug targets.

Methods

We performed a GWAS meta-analysis of early PD motor severity and progression up to 3 years from study entry. We used linear mixed-effect models with additive effects, corrected for age at diagnosis, sex, and the first 5 genetic principal components to assess variability in axial, limb, and total Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III scores.

Results

We included 3,572 unrelated European ancestry patients with PD from 5 observational cohorts and 1 drug trial. The average AAO was 62.6 years (SD = 9.83), and 63% of participants were male. We found an average increase in the total MDS-UPDRS III score of 2.3 points/year. We identified an association between PD axial motor progression and variation at the GJA5 locus at 1q12 (β = -0.25, SE = 0.04, p = 3.4e-10). Exploration of the regulation of gene expression in the region (cis-expression quantitative trait loci [eQTL] analysis) showed that the lead variant was associated with expression of ACP6, a lysophosphatidic acid phosphatase that regulates mitochondrial lipid biosynthesis (cis-eQTL p-values in blood and brain RNA expression data sets: <10-14 in eQTLGen and 10-7 in PsychEncode).

Discussion

Our study highlights the potential role of mitochondrial lipid homeostasis in the progression of PD, which may be important in establishing new drug targets that might modify disease progression.

Impact of the Dopamine System on Long-Term Cognitive Impairment in Parkinson Disease: An Exploratory Study

Background

Little is known about the impact of the dopamine system on development of cognitive impairment (CI) in Parkinson disease (PD).

Objectives

We used data from a multi-site, international, prospective cohort study to explore the impact of dopamine system-related biomarkers on CI in PD.

Methods

PD participants were assessed annually from disease onset out to 7 years, and CI determined by applying cut-offs to four measures: (1) Montreal Cognitive Assessment; (2) detailed neuropsychological test battery; (3) Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) cognition score; and (4) site investigator diagnosis of CI (mild cognitive impairment or dementia). The dopamine system was assessed by serial Iodine-123 Ioflupane dopamine transporter (DAT) imaging, genotyping, and levodopa equivalent daily dose (LEDD) recorded at each assessment. Multivariate longitudinal analyses, with adjustment for multiple comparisons, determined the association between dopamine system-related biomarkers and CI, including persistent impairment.

Results

Demographic and clinical variables associated with CI were higher age, male sex, lower education, non-White race, higher depression and anxiety scores and higher MDS-UPDRS motor score. For the dopamine system, lower baseline mean striatum dopamine transporter values (P range 0.003-0.005) and higher LEDD over time (P range <0.001-0.01) were significantly associated with increased risk for CI.

Conclusions

Our results provide preliminary evidence that alterations in the dopamine system predict development of clinically-relevant, cognitive impairment in Parkinson's disease. If replicated and determined to be causative, they demonstrate that the dopamine system is instrumental to cognitive health status throughout the disease course.

TRIAL REGISTRATION

Parkinson's Progression Markers Initiative is registered with ClinicalTrials.gov (NCT01141023).

Staging Parkinson's Disease According to the MNCD (Motor/Non-motor/Cognition/Dependency) Classification Correlates with Disease Severity and Quality of Life

BACKGROUND

Recently, a novel simple classification called MNCD, based on 4 axes (Motor; Non-motor; Cognition; Dependency) and 5 stages, has been proposed to classify Parkinson's disease (PD).

OBJECTIVE

Our aim was to apply the MNCD classification in a cohort of PD patients for the first time and also to analyze the correlation with quality of life (QoL) and disease severity.

METHODS

Data from the baseline visit of PD patients recruited from 35 centers in Spain from the COPPADIS cohort fromJanuary 2016 to November 2017 were used to apply the MNCD classification. Three instruments were used to assess QoL:1) the 39-item Parkinson's disease Questionnaire [PDQ-39]); PQ-10; the EUROHIS-QOL 8-item index (EUROHIS-QOL8).

RESULTS

Four hundred and thirty-nine PD patients (62.05±7.84 years old; 59% males) were included. MNCD stage was:stage 1, 8.4% (N = 37); stage 2, 62% (N = 272); stage 3, 28.2% (N = 124); stage 4-5, 1.4% (N = 6). A more advancedMNCD stage was associated with a higher score on the PDQ39SI (p < 0.0001) and a lower score on the PQ-10 (p< 0.0001) and EUROHIS-QOL8 (p< 0.0001). In many other aspects of the disease, such as disease duration, levodopa equivalent daily dose, motor symptoms, non-motor symptoms, and autonomy for activities of daily living, an association between the stage and severity was observed, with data indicating a progressive worsening related to disease progression throughout the proposed stages.

CONCLUSION

Staging PD according to the MNCD classification correlated with QoL and disease severity. The MNCD could be a proper tool to monitor the progression of PD.

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.

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.

Association Between White Matter Networks and the Pattern of Striatal Dopamine Depletion in Patients With Parkinson Disease

OBJECTIVES

Individual variability in nigrostriatal dopaminergic denervation is an important factor underlying clinical heterogeneity in Parkinson disease (PD). This study aimed to explore whether the pattern of striatal dopamine depletion was associated with white matter (WM) networks in PD.

METHODS

A total of 240 newly diagnosed patients with PD who underwent 18F-FP-CIT PET scans and brain diffusion tensor imaging at initial assessment were enrolled in this study. We measured 18F-FP-CIT tracer uptake as an indirect marker for striatal dopamine depletion. Factor analysis-derived striatal dopamine loss patterns were estimated in each patient to calculate the composite scores of 4 striatal subregion factors (caudate, more-affected and less-affected sensorimotor striata, and anterior putamen) based on the availability of striatal dopamine transporter. The WM structural networks that were correlated with the composite scores of each striatal subregion factor were identified using a network-based statistical analysis.

RESULTS

A higher composite score of caudate (i.e., relatively preserved dopaminergic innervation in the caudate) was associated with a strong structural connectivity in a single subnetwork comprising the left caudate and left frontal gyri. Selective dopamine loss in the caudate was associated with strong connectivity in the structural subnetwork whose hub nodes were bilateral thalami and left insula, which were connected to the anterior cingulum. However, no subnetworks were correlated with the composite scores of other striatal subregion factors. The connectivity strength of the network with a positive correlation with the composite score of caudate affected the frontal/executive function either directly or indirectly through the mediation of dopamine depletion in the caudate.

CONCLUSIONS

Our findings indicate that different patterns of striatal dopamine depletion are closely associated with WM structural alterations, which may contribute to heterogeneous cognitive profiles in individuals with PD.

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.

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.

Dysautonomia and REM sleep behavior disorder contributions to progression of Parkinson's disease phenotypes

Non-motor symptoms of Parkinson's disease (PD) such as dysautonomia and REM sleep behavior disorder (RBD) are recognized to be important prodromal symptoms that may also indicate clinical subtypes of PD with different pathogenesis. Unbiased clustering analyses showed that subjects with dysautonomia and RBD symptoms, as well as early cognitive dysfunction, have faster progression of the disease. Through analysis of the Parkinson's Progression Markers Initiative (PPMI) de novo PD cohort, we tested the hypothesis that symptoms of dysautonomia and RBD, which are readily assessed by standard questionnaires in an ambulatory care setting, may help to independently prognosticate disease progression. Although these two symptoms associate closely, dysautonomia symptoms predict severe progression of motor and non-motor symptoms better than RBD symptoms across the 3-year follow-up period. Autonomic system involvement has not received as much attention and may be important to consider for stratification of subjects for clinical trials and for counseling patients.

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.

Essential tremor plus rest tremor: current concepts and controversies

Since the initial description of Essential Tremor (ET), the entity of ET with rest tremor has proven to be a controversial concept. Some authors argued it could be a late manifestation of ET, others suggested it could be a variant of ET, yet others suggested it could represent a transitional state between ET and Parkinson's disease. The novel tremor classification has proposed the construct of ET-plus to differentiate patients with rest tremor from pure ET. However, there is no clarity of what ET-plus rest tremor represents. With the aim of shedding light on this controversial entity, we have, therefore, systematically reviewed all clinical, electrophysiological, imaging and anatomopathological studies indexed in the Medline database published both before and after the new tremor classification and involving patients with ET-plus rest tremor. Forty-four studies involving 4028 patients were included in this review and analyzed in detail by means of descriptive statistics. The results of the current review suggest that ET-plus rest tremor is a heterogenous group of conditions: thus, rest tremor might represent a late feature of ET, might reflect a different disorder with higher age at onset and lower dependance on genetic susceptibility than ET, might suggest the development of Parkinson's disease or might indicate a misdiagnosis of ET. The reviewed lines of evidence refuse recent claims arguing against the construct of ET-plus, which should be viewed as a syndrome with different possible underpinnings, and highlights methodological issues to be solved in future research.

Characterization of Parkinson's Disease Subtypes and Related Attributes

Parkinson's disease is a progressive neurodegenerative disease with complex, heterogeneous motor and non-motor symptoms. The current evidence shows that there is still a marked heterogeneity in the subtyping of Parkinson's disease using both clinical and data-driven approaches. Another challenge posed in PD subtyping is the reproducibility of previously identified PD subtypes. These issues require additional results to confirm previous findings and help reconcile discrepancies, as well as establish a standardized application of cluster analysis to facilitate comparison and reproducibility of identified PD subtypes. Our study aimed to address this gap by investigating subtypes of Parkinson's disease using comprehensive clinical (motor and non-motor features) data retrieved from 408 de novo Parkinson's disease patients with the complete clinical data in the Parkinson's Progressive Marker Initiative database. A standardized k-means cluster analysis approach was developed by taking into consideration of common practice and recommendations from previous studies. All data analysis codes were made available online to promote data comparison and validation of reproducibility across research groups. We identified two distinct PD subtypes, termed the severe motor-non-motor subtype (SMNS) and the mild motor- non-motor subtype (MMNS). SMNS experienced symptom onset at an older age and manifested more intense motor and non-motor symptoms than MMNS, who experienced symptom onset at a younger age and manifested milder forms of Parkinson's symptoms. The SPECT imaging makers supported clinical findings such that the severe motor-non-motor subtype showed lower binding values than the mild motor- non-motor subtype, indicating more significant neural damage at the nigral pathway. In addition, SMNS and MMNS show distinct motor (ANCOVA test: F = 47.35, p< 0.001) and cognitive functioning (F = 33.93, p< 0.001) progression trends. Such contrast between SMNS and MMNS in both motor and cognitive functioning can be consistently observed up to 3 years following the baseline visit, demonstrating the potential prognostic value of identified PD subtypes.

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.

Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering

Identification of Parkinson's disease subtypes may help understand underlying disease mechanisms and provide personalized management. Although clustering methods have been previously used for subtyping, they have reported generic subtypes of limited relevance in real life practice because patients do not always fit into a single category. The aim of this study was to identify new subtypes assuming that patients could be grouped differently according to certain sets of related symptoms. To this purpose, a novel model-based multi-partition clustering method was applied on data from an international, multi-center, cross-sectional study of 402 Parkinson's disease patients. Both motor and non-motor symptoms were considered. As a result, eight sets of related symptoms were identified. Each of them provided a different way to group patients: impulse control issues, overall non-motor symptoms, presence of dyskinesias and pyschosis, fatigue, axial symptoms and motor fluctuations, autonomic dysfunction, depression, and excessive sweating. Each of these groups could be seen as a subtype of the disease. Significant differences between subtypes (P< 0.01) were found in sex, age, age of onset, disease duration, Hoehn & Yahr stage, and treatment. Independent confirmation of these results could have implications for the clinical management of Parkinson's disease patients.

Correlation of Dopaminergic Denervation and the Progression of Autonomic Dysfunctions in Different Clinical Subtypes of Parkinson's Disease

BACKGROUND

Autonomic dysfunctions occur in the early stage of Parkinson's disease (PD) and impact the quality of life during the progression of the disease. In this study, we evaluated the serial progression of autonomic dysfunctions between different subtypes of a prospective PD cohort.

MATERIALS AND METHODS

From the Parkinson's Progression Markers Initiative (PPMI) database, 325 PD patients (age: 61.2 ± 9.7, M : F = 215 : 110) were enrolled. Patients were subgrouped into tremor-dominant (TD), indeterminate, and postural instability and gait disorder (PIGD) subtypes. The progression of autonomic dysfunctions and dopaminergic denervation from I-123 FP-CIT SPECT images of each group were analyzed and compared at baseline, 12 months, 24 months, and 48 months of follow-up periods.

RESULTS

The SCOPA-AUT score of the indeterminate subtype was significantly higher than that of the TD subtype (P < 0.05) at baseline and was significantly higher than that of both TD and PIGD subtypes (P < 0.05) at 48 months. The indeterminate subtype had the most significant correlation between the aggravation of dopaminergic denervation in I-123 FP-CIT SPECT images and the increase of SCOPA-AUT scores during 48 months of follow-up (r = 0.56, P < 0.01).

CONCLUSIONS

Autonomic dysfunctions were most severe in the indeterminate subtype throughout the 48 months of the follow-up period, with a significant correlation with dopaminergic denervation. We suggest a positive relationship between dopaminergic denervation and autonomic dysfunctions of the indeterminate subtype, beginning from the early stage of PD.

A Systematic Review of Parkinson's Disease Cluster Analysis Research

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

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

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

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

BACKGROUND

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

METHODS

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

FINDINGS

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

INTERPRETATION

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

FUNDING

Michael J Fox Foundation.

Non-Motor Symptoms and Associated Factors in Parkinson's Disease Patients in Addis Ababa, Ethiopia: A Multicenter Cross-Sectional Study

BACKGROUND

Non-motor symptoms (NMSs) of Parkinson's disease (PD) were often overlooked and less studied. Little is known about NMSs in Ethiopia. The aim of the study was to determine the prevalence of NMSs and associated factors.

METHODS

A multi-center cross-sectional observational study was conducted. NMS questionnaire was used to screen for the NMSs. Both descriptive and analytical statistics were used to analyze the data.

RESULTS

Total of 123 PD patients with median of 4 years were investigated. The mean age of PD patients was 62.9 years. The mean age of PD onset was 58.3 years. In 23.6% the age of onset was below age 50. Males accounted 72.4%. Majority of the patients were on Levodopa alone and 31.7% were on levodopa plus trihexyphenidyl. Longer duration of illness was associated with frequent occurrence of NMSs. Constipation was the commonest NMS (78%), followed by urinary urgency (67.5%) and nocturia (63.4%). An unexplained pain was reported by 45.5 %, cognitive impairment (45.5%), and sleep disturbance was reported by 45.5% of the study participants. Neurophysciatric symptoms were reported by small proportion of the patients. Lower monthly earning was associated with swallowing problem, unexplained weight change, and lighheadness.

CONCLUSION

The prevalence of NMS was high among PD patients in Ethiopia. Constipation was the commonest NMS. Longer duration of illness was associated with frequent occurrence of NMSs. Lower monthly earning was associated with swallowing problem, unexplained weight change, and lighheadness.

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.

Natural Phytochemicals as Novel Therapeutic Strategies to Prevent and Treat Parkinson's Disease: Current Knowledge and Future Perspectives

Parkinson's disease (PD) is the second-most common neurodegenerative chronic disease affecting both cognitive performance and motor functions in aged people. Yet despite the prevalence of this disease, the current therapeutic options for the management of PD can only alleviate motor symptoms. Research has explored novel substances for naturally derived antioxidant phytochemicals with potential therapeutic benefits for PD patients through their neuroprotective mechanism, targeting oxidative stress, neuroinflammation, abnormal protein accumulation, mitochondrial dysfunction, endoplasmic reticulum stress, neurotrophic factor deficit, and apoptosis. The aim of the present study is to perform a comprehensive evaluation of naturally derived antioxidant phytochemicals with neuroprotective or therapeutic activities in PD, focusing on their neuropharmacological mechanisms, including modulation of antioxidant and anti-inflammatory activity, growth factor induction, neurotransmitter activity, direct regulation of mitochondrial apoptotic machinery, prevention of protein aggregation via modulation of protein folding, modification of cell signaling pathways, enhanced systemic immunity, autophagy, and proteasome activity. In addition, we provide data showing the relationship between nuclear factor E2-related factor 2 (Nrf2) and PD is supported by studies demonstrating that antiparkinsonian phytochemicals can activate the Nrf2/antioxidant response element (ARE) signaling pathway and Nrf2-dependent protein expression, preventing cellular oxidative damage and PD. Furthermore, we explore several experimental models that evaluated the potential neuroprotective efficacy of antioxidant phytochemical derivatives for their inhibitory effects on oxidative stress and neuroinflammation in the brain. Finally, we highlight recent developments in the nanodelivery of antioxidant phytochemicals and its neuroprotective application against pathological conditions associated with oxidative stress. In conclusion, naturally derived antioxidant phytochemicals can be considered as future pharmaceutical drug candidates to potentially alleviate symptoms or slow the progression of PD. However, further well-designed clinical studies are required to evaluate the protective and therapeutic benefits of phytochemicals as promising drugs in the management of PD.

Improving Medication Regimen Recommendation for Parkinson's Disease Using Sensor Technology

Parkinson's disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson's patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician's initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson's medication changes-clinically assessed by the MDS-Unified Parkinson's Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients' cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose-with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

Deep brain stimulation outcomes in the malignant end of Parkinson's disease spectrum

BACKGROUND

Heterogeneity of Parkinson's Disease (PD) phenotype may influence deep brain stimulation (DBS) outcome. However, DBS response in the malignant end of the PD spectrum has been poorly investigated.

OBJECTIVE

To evaluate and compare DBS outcomes in malignant and benign PD patients, defined according to motor and non-motor symptom presentation at the presurgical selection.

METHODS

We categorized a cohort of 154 parkinsonian patients fulfilling criteria for subthalamic nucleus (STN)-DBS into malignant, benign, and intermediate subtypes, according to a recently validated clinical PD classification. DBS efficacy on daily living independence (Schwab and England -S&E-score ≥70%), motor symptoms, and motor fluctuations (Unified Parkinson's Disease Rating Scale -UPDRS- part-III and -IV, and Ambulatory Capacity Measure) were compared between malignant and benign patients, using corrected binary logistic regressions and repeated measure general linear model.

RESULTS

One year after surgery, the probability of losing daily life independence was 16-fold higher in malignant patients, even after adjusting for age at PD onset, PD duration, and percentage of motor improvement after STN-DBS (OR: 16.233; p: 0.035). Conversely, malignant and benign patients showed a similar extent of improvement after STN-DBS (p > 0.05) in motor symptoms, motor fluctuations, and ambulatory capacity, both in medication-ON and medication-OFF conditions.

CONCLUSION

DBS candidates in the malignant end of the PD spectrum may profit from a similar improvement of motor symptoms and fluctuations after STN-DBS when compared to benign PD. However, patients of the malignant group have a lower probability of maintaining independence in daily life early after surgery.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Multimodal phenotypic axes of Parkinson's disease

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

Minor hallucinations reflect early gray matter loss and predict subjective cognitive decline in Parkinson's disease

BACKGROUND AND PURPOSE

Well-structured hallucinations in Parkinson's disease (PD) are associated with poor prognosis and dementia. However, the predictive value of minor psychotic phenomena in cognitive deterioration is not well known. Cross-sectional studies have shown that PD patients with minor hallucinations have more severe cortical atrophy than non-hallucinators, but baseline and longitudinal studies addressing the evolution of these brain differences are lacking. The impact of developing minor hallucinations on cognitive impairment and cortical atrophy progression in early PD was explored.

METHODS

One hundred and thirty-one de novo PD patients from the Parkinson's Progression Marker Initiative for whom brain magnetic resonance imaging scans were available were included. Cognitive outcome at 5 years was compared between patients with and without minor hallucinations during follow-up. Additionally, using gray matter volume (GMV) voxel-based morphometry, cross-sectional (at baseline) and longitudinal (1- and 2-year GMV loss) structural brain differences between groups were studied.

RESULTS

During follow-up, 35.1% of patients developed minor hallucinations. At 5 years, these patients showed an increased prevalence of subjective cognitive decline compared to non-hallucinators (44.1% vs. 13.9%; p < 0.001), but not formal cognitive impairment. Additionally, compared to non-hallucinators, they exhibited reduced GMV at baseline in visuoperceptive areas and increased GMV loss in left temporal areas (p < 0.05 corrected).

CONCLUSIONS

Minor hallucinations seem to be an early clinical marker of increased neurodegeneration and are associated with mid-term subjective cognitive decline. Longer follow-up analyses would be needed to further define if these findings could reflect a higher risk of future cognitive deterioration.

The Pathophysiological Correlates of Parkinson's Disease Clinical Subtypes

BACKGROUND

Possible pathophysiological mechanisms underlying Parkinson's disease (PD) clinical subtypes are unknown. The objective of this study was to identify pathophysiological substrate of PD subtypes using neurophysiological techniques.

METHODS

One hundred de novo PD patients participated. We collected patient demographic and clinical data, which were used to perform a hierarchical cluster analysis. The neurophysiological assessment tested primary motor cortex excitability and plasticity using transcranial magnetic stimulation. To evaluate motor performance, we performed a kinematic analysis of fast index finger abduction. To investigate sensory function and sensorimotor mechanisms, we measured the somatosensory temporal discrimination threshold at rest and during movement, respectively.

RESULTS

Hierarchical cluster analysis identified 2 clinical clusters. Cluster I ("mild motor-predominant") included patients who had milder motor and nonmotor symptoms severity than cluster II patients, who had a combination of severe motor and nonmotor manifestations (diffuse malignant). We observed that the diffuse malignant subtype had increased cortical excitability and reduced plasticity compared with the mild motor-predominant subtype. Kinematic analysis of motor performance demonstrated that the diffuse malignant subtype was significantly slower than the mild motor-predominant subtype. Conversely, we did not observe any significant differences in sensory function or sensorimotor integration between the two PD subtypes.

CONCLUSIONS

De novo PD subtypes showed different patterns of motor system dysfunction, whereas sensory function and sensorimotor integration mechanisms did not differ between subtypes. Our findings suggest that the subtyping of PD patients is not a mere clinical classification but reflects different pathophysiological mechanisms. Neurophysiological parameters may represent promising biomarkers to evaluate PD subtypes and their progression. © 2020 International Parkinson and Movement Disorder Society.

Association of the Non-Motor Burden with Patterns of Striatal Dopamine Loss in de novo Parkinson's Disease

BACKGROUND

Striatal dopamine deficits play a key role in the pathogenesis of Parkinson's disease (PD), and several non-motor symptoms (NMSs) have a dopaminergic component.

OBJECTIVE

To investigate the association between early NMS burden and the patterns of striatal dopamine depletion in patients with de novo PD.

METHODS

We consecutively recruited 255 patients with drug-naïve early-stage PD who underwent 18F-FP-CIT PET scans. The NMS burden of each patient was assessed using the NMS Questionnaire (NMSQuest), and patients were divided into the mild NMS burden (PDNMS-mild) (NMSQuest score <6; n = 91) and severe NMS burden groups (PDNMS-severe) (NMSQuest score >9; n = 90). We compared the striatal dopamine transporter (DAT) activity between the groups.

RESULTS

Patients in the PDNMS-severe group had more severe parkinsonian motor signs than those in the PDNMS-mild group, despite comparable DAT activity in the posterior putamen. DAT activity was more severely depleted in the PDNMS-severe group in the caudate and anterior putamen compared to that in the PDMNS-mild group. The inter-sub-regional ratio of the associative/limbic striatum to the sensorimotor striatum was lower in the PDNMS-severe group, although this value itself lacked fair accuracy for distinguishing between the patients with different NMS burdens.

CONCLUSION

This study demonstrated that PD patients with severe NMS burden exhibited severe motor deficits and relatively diffuse dopamine depletion throughout the striatum. These findings suggest that the level of NMS burden could be associated with distinct patterns of striatal dopamine depletion, which could possibly indicate the overall pathological burden in PD.

Association of specific biotypes in patients with Parkinson disease and disease progression

Objective

To identify biotypes in patients with newly diagnosed Parkinson disease (PD) and to test whether these biotypes could explain interindividual differences in longitudinal progression.

Methods

In this longitudinal analysis, we use a data-driven approach clustering PD patients from the Parkinson's Progression Markers Initiative (n = 314, age 61.0 ± 9.5, years 34.1% female, 5 years of follow-up). Voxel-level neuroanatomic features were estimated with deformation-based morphometry (DBM) of T1-weighted MRI. Voxels with deformation values that were significantly correlated (p < 0.01) with clinical scores (Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale Parts I–III and total score, tremor score, and postural instability and gait difficulty score) at baseline were selected. Then, these neuroanatomic features were subjected to hierarchical cluster analysis. Changes in the longitudinal progression and neuroanatomic pattern were compared between different biotypes.

Results

Two neuroanatomic biotypes were identified: biotype 1 (n = 114) with subcortical brain volumes smaller than heathy controls and biotype 2 (n = 200) with subcortical brain volumes larger than heathy controls. Biotype 1 had more severe motor impairment, autonomic dysfunction, and much worse REM sleep behavior disorder than biotype 2 at baseline. Although disease durations at the initial visit and follow-up were similar between biotypes, patients with PD with smaller subcortical brain volume had poorer prognosis, with more rapid decline in several clinical domains and in dopamine functional neuroimaging over an average of 5 years.

Conclusion

Robust neuroanatomic biotypes exist in PD with distinct clinical and neuroanatomic patterns. These biotypes can be detected at diagnosis and predict the course of longitudinal progression, which should benefit trial design and evaluation.

Unsupervised clustering of dopamine transporter PET imaging discovers heterogeneity of parkinsonism

Parkinsonism has heterogeneous nature, showing distinctive patterns of disease progression and prognosis. We aimed to find clusters of parkinsonism based on ¹⁸F‐fluoropropyl‐carbomethoxyiodophenylnortropane (FP‐CIT) PET as a data‐driven approach to evaluate heterogenous dopaminergic neurodegeneration patterns. Two different cohorts of patients who received FP‐CIT PET were collected. A labeled cohort (n = 94) included patients with parkinsonism who underwent a clinical follow‐up of at least 3 years (mean 59.0 ± 14.6 months). An unlabeled cohort (n = 813) included all FP‐CIT PET data of a single‐center. All PET data were clustered by a dimension reduction method followed by hierarchical clustering. Four distinct clusters were defined according to the imaging patterns. When the diagnosis of the labeled cohort of 94 patients was compared with the corresponding cluster, parkinsonism patients were mostly included in two clusters, cluster “0” and “2.” Specifically, patients with progressive supranuclear palsy were significantly more included in cluster 0. The two distinct clusters showed significantly different clinical features. Furthermore, even in PD patients, two clusters showed a trend of different clinical features. We found distinctive clusters of parkinsonism based on FP‐CIT PET‐derived heterogeneous neurodegeneration patterns, which were associated with different clinical features. Our results support a biological underpinning for the heterogeneity of neurodegeneration in parkinsonism.

Parkinson disease clinical subtypes: key features & clinical milestones

OBJECTIVES

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

METHODS

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

RESULTS

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

INTERPRETATION

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

Overall and Disease Related Mortality in Parkinson's Disease - a Longitudinal Cohort Study

Background:

Earlier research showed that Parkinson’s disease is related to increased overall mortality, but it remains unclear which patient level factors are predictive of increased mortality in Parkinson’s disease.

Objective:

To jointly evaluate potential risk factors for overall and Parkinson’s disease (PD) related mortality, we collected detailed information from a cohort of newly diagnosed PD patients which was consequently followed for over a decade.

Methods:

A total of 133 consecutive patients with newly diagnosed PD were followed for at least 13 years. Survival analysis of observed mortality was used to evaluate risk factors for overall mortality, whereas survival analysis of mortality as corrected for the general population was used to evaluate risk factors for PD-related mortality.

Results:

Overall mortality increased with age, male sex, higher levodopa equivalent dose, and presence of mild cognitive impairment. PD-related mortality increased with earlier onset of Parkinson’s disease, higher levodopa equivalent dose, and mild cognitive impairment.

Conclusions:

Our findings provide confirmation and extension of risk factors for overall mortality and generate new insights into PD-related mortality.

Patterns of striatal dopamine depletion in early Parkinson disease

Objective

To investigate whether the patterns of striatal dopamine depletion on dopamine transporter (DAT) scans could provide information on the long-term prognosis in Parkinson disease (PD).

Methods

We enrolled 205 drug-naive patients with early-stage PD, who underwent18F-FP-CIT PET scans at initial assessment and received PD medications for 3 or more years. After quantifying the DAT availability in each striatal subregion, factor analysis was conducted to simplify the identification of striatal dopamine depletion patterns and to yield 4 striatal subregion factors. We assessed the effect of these factors on the development of levodopa-induced dyskinesia (LID), wearing-off, freezing of gait (FOG), and dementia during the follow-up period (6.84 ± 1.80 years).

Results

The 4 factors indicated which striatal subregions were relatively preserved: factor 1 (caudate), factor 2 (more-affected sensorimotor striatum), factor 3 (less-affected sensorimotor striatum), and factor 4 (anterior putamen). Cox regression analyses using the composite scores of these striatal subregion factors as covariates demonstrated that selective dopamine depletion in the sensorimotor striatum was associated with a higher risk for developing LID. Selective dopamine loss in the putamen, particularly in the anterior putamen, was associated with early development of wearing-off. Selective involvement of the anterior putamen was associated with a higher risk for dementia conversion. However, the patterns of striatal dopamine depletion did not affect the risk of FOG.

Conclusions

These findings suggested that the patterns of striatal dopaminergic denervation, which were estimated by the equation derived from the factor analysis, have a prognostic implication in patients with early-stage PD.

Clinically relevant connectivity features define three subtypes of Parkinson's disease patients

Parkinson's disease (PD) is characterized by complex clinical symptoms, including classic motor and nonmotor disturbances. Patients with PD vary in clinical manifestations and prognosis, which point to the existence of subtypes. This study aimed to find the fiber connectivity correlations with several crucial clinical symptoms and identify PD subtypes using unsupervised clustering analysis. One hundred and thirty-four PD patients and 77 normal controls were enrolled. Canonical correlation analysis (CCA) was performed to define the clinically relevant connectivity features, which were then used in the hierarchical clustering analysis to identify the distinct subtypes of PD patients. Multimodal neuroimaging analyses were further used to explore the neurophysiological basis of these subtypes. The methodology was validated in an independent data set. CCA revealed two significant clinically relevant patterns (motor-related pattern and depression-related pattern; r = .94, p < .001 and r = .926, p = .001, respectively) among PD patients, and hierarchical clustering analysis identified three neurophysiological subtypes ("mild" subtype, "severe depression-dominant" subtype and "severe motor-dominant" subtype). Multimodal neuroimaging analyses suggested that the patients in the "severe depression-dominant" subtype exhibited widespread disruptions both in function and structure, while the other two subtypes exhibited relatively mild abnormalities in brain function. In the independent validation, three similar subtypes were identified. In conclusion, we revealed heterogeneous subtypes of PD patients according to their distinct clinically relevant connectivity features. Importantly, depression symptoms have a considerable impact on brain damage in patients with PD.

Neuropsychiatric symptoms and cognitive abilities over the initial quinquennium of Parkinson disease

OBJECTIVE

To determine the evolution of numerous neuropsychiatric symptoms and cognitive abilities in Parkinson disease from disease onset.

METHODS

Prospectively collected, longitudinal (untreated, disease onset to year 5), observational data from Parkinson's Progression Markers Initiative annual visits was used to evaluate prevalence, correlates, and treatment of 10 neuropsychiatric symptoms and cognitive impairment in Parkinson disease participants and matched healthy controls.

RESULTS

Of 423 Parkinson disease participants evaluated at baseline, 315 (74.5%) were assessed at year 5. Eight neuropsychiatric symptoms studied increased in absolute prevalence by 6.2-20.9% at year 5 relative to baseline, and cognitive impairment increased by 2.7-6.2%. In comparison, the frequency of neuropsychiatric symptoms in healthy controls remained stable or declined over time. Antidepressant and anxiolytic/hypnotic use in Parkinson disease were common at baseline and increased over time (18% to 27% for the former; 13% to 24% for the latter); antipsychotic and cognitive-enhancing medication use was uncommon throughout (2% and 5% of patients at year 5); and potentially harmful anticholinergic medication use was common and increased over time. At year 5 the cross-sectional prevalence for having three or more neuropsychiatric disorders/cognitive impairment was 56% for Parkinson disease participants versus 13% for healthy controls, and by then seven of the examined disorders had either occurred or been treated at some time point in the majority of Parkinson disease patients. Principal component analysis suggested an affective disorder subtype only.

INTERPRETATION

Neuropsychiatric features in Parkinson disease are common from the onset, increase over time, are frequently comorbid, and fluctuate in severity.

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.

Animal models for preclinical Parkinson's research: An update and critical appraisal

Animal models of Parkinson's disease (PD) are essential to investigate pathogenic pathways at the whole-organism level. Moreover, they are necessary for a preclinical investigation of potential new therapies. Different pathological features of PD can be induced in a variety of invertebrate and vertebrate species using toxins, drugs, or genetic perturbations. Each model has a particular utility and range of applicability. Invertebrate PD models are particularly useful for high throughput-screening applications, whereas mammalian models are needed to explore complex motor and non-motor features of the human disease. Here, we provide a comprehensive review and critical appraisal of the most commonly used mammalian models of PD, which are produced in rats and mice. A substantial loss of nigrostriatal dopamine neurons is necessary for the animal to exhibit a hypokinetic motor phenotype responsive to dopaminergic agents, thus resembling clinical PD. This level of dopaminergic neurodegeneration can be induced using specific neurotoxins, environmental toxicants, or proteasome inhibitors. Alternatively, nigrostriatal dopamine degeneration can be induced via overexpression of α-synuclein using viral vectors or transgenic techniques. In addition, protein aggregation pathology can be triggered by inoculating preformed fibrils of α-synuclein in the substantia nigra or the striatum. Thanks to the conceptual and technical progress made in the past few years a vast repertoire of well-characterized animal models are currently available to address different aspects of PD in the laboratory.

Prognosis and Neuropathologic Correlation of Clinical Subtypes of Parkinson Disease

Importance

Clinical subtyping of Parkinson disease at diagnosis is useful in estimating disease course and survival. Severity and rate of progression of neuropathologies are important determinants of clinical Parkinson subtypes.

Objective

To provide longitudinal clinical disease-course data and neuropathologic correlation for newly proposed Parkinson disease subtypes.

Design, Setting, and Participants

Retrospective cohort study of consecutive patients with autopsy-confirmed Parkinson disease who were regularly seen throughout their disease course by hospital specialists in the United Kingdom and donated their brain at death to the Queen Square Brain Bank between January 2009 and December 2017. Patients with additional neuropathologic diagnoses, monogenic forms of parkinsonism, or insufficiently detailed clinical information were excluded. Based on severity of motor symptoms, rapid eye movement sleep behavior disorder, and autonomic and cognitive function at diagnosis, patients were classified adapting a subtyping classification into mild-motor predominant, intermediate, or diffuse malignant subtypes.

Main Outcomes and Measures

Time from diagnosis to disease milestones (recurrent falls, wheelchair dependence, dementia, and care home placement) and death were compared between subtypes, and their risk was estimated using Cox hazard regression models. Severity and distribution of Lewy pathology and Alzheimer disease-related pathology were assessed using staging systems.

Results

From a total of 146 patients, 111 patients were included (67 men [60.4%]; mean [SD] age at diagnosis, 62.5 [11.5] years). The diffuse malignant subtype had earlier development of milestones and reduced survival. Cox proportional hazard regression showed an increased adjusted risk of any disease milestone (hazard ratio, 10.90; 95% CI, 5.51-21.58; P < .001) and death (hazard ratio, 3.65; 95% CI, 1.98-6.75; P < .001) in the diffuse malignant group. Age at diagnosis was the only additional variable with statistical significance (adjusted hazard ratio for death, 1.14; 95% CI, 1.11-1.17; P <.001). Staging of Lewy pathology and Alzheimer disease-related pathology did not differ between subtypes, although they showed different rates of progression, and the latter was associated with age at death.

Conclusions and Relevance

Parkinson clinical subtypes at diagnosis may estimate disease course and survival, which may be useful in providing a more accurate prognosis in individual patients in clinical practice and helping to stratify subgroups in clinical trials. Different severity and progression of neuropathologies are important determinants of Parkinson subtypes, and age at diagnosis should be included in future subtype classifications.

Pathological Influences on Clinical Heterogeneity in Lewy Body Diseases

PD, PD with dementia, and dementia with Lewy bodies are clinical syndromes characterized by the neuropathological accumulation of alpha-synuclein in the CNS that represent a clinicopathological spectrum known as Lewy body disorders. These clinical entities have marked heterogeneity of motor and nonmotor symptoms with highly variable disease progression. The biological basis for this clinical heterogeneity remains poorly understood. Previous attempts to subtype patients within the spectrum of Lewy body disorders have centered on clinical features, but converging evidence from studies of neuropathology and ante mortem biomarkers, including CSF, neuroimaging, and genetic studies, suggest that Alzheimer's disease beta-amyloid and tau copathology strongly influence clinical heterogeneity and prognosis in Lewy body disorders. Here, we review previous clinical biomarker and autopsy studies of Lewy body disorders and propose that Alzheimer's disease copathology is one of several likely pathological contributors to clinical heterogeneity of Lewy body disorders, and that such pathology can be assessed in vivo. Future work integrating harmonized assessments and genetics in PD, PD with dementia, and dementia with Lewy bodies patients followed to autopsy will be critical to further refine the classification of Lewy body disorders into biologically distinct endophenotypes. This approach will help facilitate clinical trial design for both symptomatic and disease-modifying therapies to target more homogenous subsets of Lewy body disorders patients with similar prognosis and underlying biology. © 2019 International Parkinson and Movement Disorder Society.

Subtyping of Parkinson's Disease - Where Are We Up To?

Heterogenous clinical presentations of Parkinson's disease have aroused several attempts in its subtyping for the purpose of strategic implementation of treatment in order to maximise therapeutic effects. Apart from a priori classifications based purely on motor features, cluster analysis studies have achieved little success in receiving widespread adoption. A priori classifications demonstrate that their chosen factors, whether it be age or certain motor symptoms, do have an influence on subtypes. However, the cluster analysis approach is able to integrate these factors and other clinical features to produce subtypes. Differences in inclusion criteria from datasets, in variable selection and in methodology between cluster analysis studies have made it difficult to compare the subtypes. This has impeded such subtypes from clinical applications. This review analysed existing subtypes of Parkinson's disease, and suggested that future research should aim to discover subtypes that are robustly replicable across multiple datasets rather than focussing on one dataset at a time. Hopefully, through clinical applicable subtyping of Parkinson's disease would lead to translation of these subtypes into research and clinical use.

Longitudinal assessment of autonomic dysfunction in early Parkinson's disease

INTRODUCTION

Clinical correlates of autonomic nervous system (ANS) dysfunction in early Parkinson's disease (PD) have been addressed mainly in a cross-sectional way.

METHODS

This is a combined cross-sectional and longitudinal prospective study of ANS dysfunction using the SCOPA-AUT in PD patients at the Hoehn and Yahr stage 1 with disease duration <2 years. PD patients (n = 107) were compared to healthy controls (HC, n = 79), and then followed-up for over 3 years. The severity of PD, depression, anxiety, apathy and cognitive impairment were evaluated using rating scales.

RESULTS

At least one symptom of ANS dysfunction was present in 71% of PD patients in comparison to 30.4% of HC, and in all PD patients after three years. The overall severity of dysautonomia symptoms was mild (SCOPA-AUT mean ± SD; 4.16 ± 5.0), but worsened by 23%, 86% and 0.3% during the 1st, 2nd and 3rd year respectively. Nighttime voiding (38.3%), constipation (30.8%) and straining for defecation (29%) were the most common symptoms. Prevalence and severity of urinary, gastrointestinal, and orthostatic symptoms increased, in contrast to thermoregulatory and pupillomotor symptoms. Frequency of symptoms suggestive of multi-domain ANS dysfunction rose from 49% to 79%. Psychiatric symptoms and age, but not motor impairment, were associated with dysautonomia symptoms.

CONCLUSION

Symptoms of ANS dysfunction were frequent in the initial motor stage of PD and progressed, yet remaining mild, within 3 years. An independent progression of dysautonomia symptoms from motor disability and its associations with non-motor, mainly psychiatric symptoms and age support the non-motor clustering in PD.

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

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

Clinical subtypes and genetic heterogeneity: of lumping and splitting in Parkinson disease

PURPOSE OF REVIEW

Recent studies on clinical, genetic and pathological heterogeneity of Parkinson disease have renewed the old debate whether we should think of Parkinson disease as one disease with variations, or as a group of independent diseases that happen to present with similar phenotypes. Here, we provide an overview of where the debate is coming from, and how recent findings in clinical subtyping, genetics and clinico-pathological correlation have shaped this controversy over the last few years.

RECENT FINDINGS

New and innovative clinical diagnostic criteria for Parkinson disease have been proposed and await validation. Studies using functional imaging or wearable biosensors, as well as biomarker studies, provide new support for the validity of the traditional clinical subtypes of Parkinson disease (tremor-dominant versus akinetic-rigid or postural instability/gait difficulty). A recent cluster analysis (as unbiased data-driven approach to subtyping) included a wide spectrum of nonmotor variables, and showed correlation of the proposed subtypes with disease progression in a longitudinal analysis. New genetic factors contributing to Parkinson disease susceptibility continue to be identified, including rare mutations causing monogenetic disease, common variants with small effect size and risk factors (like mutations in the gene for glucocerebrosidase) that fall in between the two other categories. Recent studies show some limited correlation between genetic factors and clinical heterogeneity. Despite some variations in patterns of pathology, Lewy bodies are still the hallmark of Parkinson disease, including the vast majority of genetic subgroups.

SUMMARY

Evidence of clinical, genetic and pathological heterogeneity of Parkinson disease continues to emerge, but clearly defined subtypes that hold up in more than one of these domains remain elusive. For research to identify such subtypes, splitting is likely the way forward; until then, for clinical practice, lumping remains the more pragmatic approach.

Clinical criteria for subtyping Parkinson's disease: biomarkers and longitudinal progression

Parkinson's disease varies widely in clinical manifestations, course of progression and biomarker profiles from person to person. Identification of distinct Parkinson's disease subtypes is of great priority to illuminate underlying pathophysiology, predict progression and develop more efficient personalized care approaches. There is currently no clear way to define and divide subtypes in Parkinson's disease. Using data from the Parkinson's Progression Markers Initiative, we aimed to identify distinct subgroups via cluster analysis of a comprehensive dataset at baseline (i.e. cross-sectionally) consisting of clinical characteristics, neuroimaging, biospecimen and genetic information, then to develop criteria to assign patients to a Parkinson's disease subtype. Four hundred and twenty-one individuals with de novo early Parkinson's disease were included from this prospective longitudinal multicentre cohort. Hierarchical cluster analysis was performed using data on demographic and genetic information, motor symptoms and signs, neuropsychological testing and other non-motor manifestations. The key classifiers in cluster analysis were a motor summary score and three non-motor features (cognitive impairment, rapid eye movement sleep behaviour disorder and dysautonomia). We then defined three distinct subtypes of Parkinson's disease patients: 223 patients were classified as 'mild motor-predominant' (defined as composite motor and all three non-motor scores below the 75th percentile), 52 as 'diffuse malignant' (composite motor score plus either ≥1/3 non-motor score >75th percentile, or all three non-motor scores >75th percentile) and 146 as 'intermediate'. On biomarkers, people with diffuse malignant Parkinson's disease had the lowest level of cerebrospinal fluid amyloid-β (329.0 ± 96.7 pg/ml, P = 0.006) and amyloid-β/total-tau ratio (8.2 ± 3.0, P = 0.032). Data from deformation-based magnetic resonance imaging morphometry demonstrated a Parkinson's disease-specific brain network had more atrophy in the diffuse malignant subtype, with the mild motor-predominant subtype having the least atrophy. Although disease duration at initial visit and follow-up time were similar between subtypes, patients with diffuse malignant Parkinson's disease progressed faster in overall prognosis (global composite outcome), with greater decline in cognition and in dopamine functional neuroimaging after an average of 2.7 years. In conclusion, we introduce new clinical criteria for subtyping Parkinson's disease based on a comprehensive list of clinical manifestations and biomarkers. This clinical subtyping can now be applied to individual patients for use in clinical practice using baseline clinical information. Even though all participants had a recent diagnosis of Parkinson's disease, patients with the diffuse malignant subtype already demonstrated a more profound dopaminergic deficit, increased atrophy in Parkinson's disease brain networks, a more Alzheimer's disease-like cerebrospinal fluid profile and faster progression of motor and cognitive deficits.

Latent Cognitive Phenotypes in De Novo Parkinson's Disease: A Person-Centered Approach

OBJECTIVES

Cognitive impairment is an important aspect of Parkinson's disease (PD), but there is considerable heterogeneity in its presentation. This investigation aims to identify and characterize latent cognitive phenotypes in early PD.

METHODS

Latent class analysis, a data-driven, person-centered, cluster analysis was performed on cognitive data from the Parkinson's Progressive Markers Initiative baseline visit. This analytic method facilitates identification of naturally occurring endophenotypes. Resulting classes were compared across biomarker, symptom, and demographic data.

RESULTS

Six cognitive phenotypes were identified. Three demonstrated consistent performance across indicators, representing poor ("Weak-Overall"), average ("Typical-Overall"), and strong ("Strong-Overall") cognition. The remaining classes demonstrated unique patterns of cognition, characterized by "Strong-Memory," "Weak-Visuospatial," and "Amnestic" profiles. The Amnestic class evidenced greater tremor severity and anosmia, but was unassociated with biomarkers linked with Alzheimer's disease. The Weak-Overall class was older and reported more non-motor features associated with cognitive decline, including anxiety, depression, autonomic dysfunction, anosmia, and REM sleep behaviors. The Strong-Overall class was younger, more female, and reported less dysautonomia and anosmia. Classes were unrelated to disease duration, functional independence, or available biomarkers.

CONCLUSIONS

Latent cognitive phenotypes with focal patterns of impairment were observed in recently diagnosed individuals with PD. Cognitive profiles were found to be independent of traditional biomarkers and motoric indices of disease progression. Only globally impaired class was associated with previously reported indicators of cognitive decline, suggesting this group may drive the effects reported in studies using variable-based analysis. Longitudinal and neuroanatomical characterization of classes will yield further insight into the evolution of cognitive change in the disease. (JINS, 2017, 23, 551-563).

Quality of Life and Nonmotor Symptoms in Parkinson's Disease

Health-related quality of life (HRQoL) is defined as "the perception and evaluation by patients themselves of the impact caused on their lives by the disease and its consequences." HRQoL is conceptualized as a combination of physical, psychological, and social well-being in the context of a particular disease. Following earlier studies revolving on the impact of the classic motor symptoms of Parkinson's disease on HRQoL, mounting evidence have been produced that nonmotor symptoms (NMS) significantly and independently contribute to worse HRQoL. This holds particularly true for such NMS such as neuropsychiatric disturbances, cognitive impairment, and fatigue, the burden of which might well exceed the effects of the motor symptoms. Nonetheless, there is very sparse evidence on how to manage these NMS and whether targeting NMS would in fact lead to an improvement of HRQoL, which calls for the need of future trials with NMS as primary outcomes.