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

Oral Levodopa Formulation Does Not Affect Progression of Parkinson Disease

OBJECTIVE

Motor fluctuations develop in most patients treated with carbidopa/levodopa for Parkinson disease. The continuous dopamine stimulation hypothesis suggests that longer-acting forms of levodopa might improve outcomes, but this has been inadequately tested in humans. We undertook to determine if there is any difference in symptom progression rate among patients taking immediate-release carbidopa/levodopa (IR), controlled-release carbidopa/levodopa (CR), or carbidopa/levodopa/entacapone (CLE) using standard outcome measures in a naturalistic study.

METHODS

We evaluated Parkinson disease subjects prospectively followed for up to 48 months in the Parkinson's Disease Biomarker Project. Bayesian linear or generalized linear mixed-effects models were developed to determine if oral levodopa formulation influenced the rate of symptom progression as measured by 8 outcome measures.

RESULTS

At baseline, the IR, CR, and CLE groups were similar except that the CR group had milder disease and was represented at only 1 site, and the CLE group had a longer disease duration. In the primary analysis, there was no difference in rate of symptom progression as measured by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale Part II, Part IV, or total score. In the secondary exploratory analysis, there was no difference in progression rate as measured by change in levodopa equivalent daily dose, Montreal Cognitive Assessment, Parkinson's Disease Questionnaire mobility subscore, Schwab and England Activities of Daily Living Scale, or a global composite outcome.

CONCLUSIONS

We found no difference in symptom progression rate in patients taking IR, CR, or CLE. This clinical observation supports pharmacokinetic studies demonstrating that none of these oral levodopa formulations achieve continuous dopamine stimulation.

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.

Parkinson's Disease Subtypes: Critical Appraisal and Recommendations

BACKGROUND

In Parkinson's disease (PD), there is heterogeneity in the clinical presentation and underlying biology. Research on PD subtypes aims to understand this heterogeneity with potential contribution for the knowledge of disease pathophysiology, natural history and therapeutic development. There have been many studies of PD subtypes but their impact remains unclear with limited application in research or clinical practice.

OBJECTIVE

To critically evaluate PD subtyping systems.

METHODS

We conducted a systematic review of PD subtypes, assessing the characteristics of the studies reporting a subtyping system for the first time. We completed a critical appraisal of their methodologic quality and clinical applicability using standardized checklists.

RESULTS

We included 38 studies. The majority were cross-sectional (n = 26, 68.4%), used a data-driven approach (n = 25, 65.8%), and non-clinical biomarkers were rarely used (n = 5, 13.1%). Motor characteristics were the domain most commonly reported to differentiate PD subtypes. Most of the studies did not achieve the top rating across items of a Methodologic Quality checklist. In a Clinical Applicability Checklist, the clinical importance of differences between subtypes, potential treatment implications and applicability to the general population were rated poorly, and subtype stability over time and prognostic value were largely unknown.

CONCLUSION

Subtyping studies undertaken to date have significant methodologic shortcomings and most have questionable clinical applicability and unknown biological relevance. The clinical and biological signature of PD may be unique to the individual, rendering PD resistant to meaningful cluster solutions. New approaches that acknowledge the individual-level heterogeneity and that are more aligned with personalized medicine are needed.

Rapid eye movement sleep behavior disorder: A proof-of-concept neuroprotection study for prodromal synucleinopathies

BACKGROUND AND PURPOSE

To explore the feasibility of a neuroprotection trial in prodromal synucleinopathy, using idiopathic rapid eye movement sleep behavior disorder (iRBD) as the target population and ¹²³ I-FP-CIT-SPECT as a biomarker of disease progression.

METHODS

Consecutive iRBD patients were randomly assigned to a treatment arm receiving selegiline and symptomatic rapid eye movement sleep behavior disorder treatment, or to a control arm receiving symptomatic treatment only. Selegiline was chosen because of a demonstrated neuroprotection effect in animal models. Patients underwent ¹²³ I-FP-CIT-SPECT at baseline and after 30 months on average. The clinical outcome was the emergence of parkinsonism and/or dementia. A repeated-measures general linear model (GLM) was applied using group (control and treatment) as "between" factor, and both time (baseline and follow-up) and regions (¹²³ I-FP-CIT-SPECT putamen and caudate uptake) as the "within" factors, adjusting for age.

RESULTS

Thirty iRBD patients completed the study (68.2 ± 6.9 years; 29 males; 21% dropout rate), 13 in the treatment arm, and 17 in the control arm. At follow-up (29.8 ± 9.0 months), three patients in the control arm developed dementia and one parkinsonism, whereas two patients in the treatment arm developed parkinsonism. Both putamen and caudate uptake decreased over time in the control arm. In the treatment arm, only the putamen uptake decreased over time, whereas caudate uptake remained stable. GLM analysis demonstrated an effect of treatment on the ¹²³ I-FP-CIT-SPECT uptake change, with a significant interaction between the effect of group, time, and regions (p = 0.004).

CONCLUSIONS

A 30-months neuroprotection study for prodromal synucleinopathy is feasible, using iRBD as the target population and ¹²³ I-FP-CIT-SPECT as a biomarker of disease progression.

Deep Brain Stimulation Selection Criteria for Parkinson's Disease: Time to Go beyond CAPSIT-PD

Despite being introduced in clinical practice more than 20 years ago, selection criteria for deep brain stimulation (DBS) in Parkinson's disease (PD) rely on a document published in 1999 called 'Core Assessment Program for Surgical Interventional Therapies in Parkinson's Disease'. These criteria are useful in supporting the selection of candidates. However, they are both restrictive and out-of-date, because the knowledge on PD progression and phenotyping has massively evolved. Advances in understanding the heterogeneity of PD presentation, courses, phenotypes, and genotypes, render a better identification of good DBS outcome predictors a research priority. Additionally, DBS invasiveness, cost, and the possibility of serious adverse events make it mandatory to predict as accurately as possible the clinical outcome when informing the patients about their suitability for surgery. In this viewpoint, we analyzed the pre-surgical assessment according to the following topics: early versus delayed DBS; the evolution of the levodopa challenge test; and the relevance of axial symptoms; patient-centered outcome measures; non-motor symptoms; and genetics. Based on the literature, we encourage rethinking of the selection process for DBS in PD, which should move toward a broad clinical and instrumental assessment of non-motor symptoms, quantitative measurement of gait, posture, and balance, and in-depth genotypic and phenotypic characterization.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

CMT-3 targets different α-synuclein aggregates mitigating their toxic and inflammogenic effects

Parkinson's disease (PD) is a neurodegenerative disorder for which only symptomatic treatments are available. Repurposing drugs that target α-synuclein aggregation, considered one of the main drivers of PD progression, could accelerate the development of disease-modifying therapies. In this work, we focused on chemically modified tetracycline 3 (CMT-3), a derivative with reduced antibiotic activity that crosses the blood–brain barrier and is pharmacologically safe. We found that CMT-3 inhibited α-synuclein amyloid aggregation and led to the formation of non-toxic molecular species, unlike minocycline. Furthermore, CMT-3 disassembled preformed α-synuclein amyloid fibrils into smaller fragments that were unable to seed in subsequent aggregation reactions. Most interestingly, disaggregated species were non-toxic and less inflammogenic on brain microglial cells. Finally, we modelled the interactions between CMT-3 and α-synuclein aggregates by molecular simulations. In this way, we propose a mechanism for fibril disassembly. Our results place CMT-3 as a potential disease modifier for PD and possibly other synucleinopathies.

Association Between REM Sleep Behavior Disorder and Cognitive Dysfunctions in Parkinson's Disease: A Systematic Review and Meta-Analysis of Observational Studies

Background: Rapid eye movement sleep behavior disorder (RBD) is thought to be a prodromal symptom of Parkinson's disease (PD). RBD is also thought to be involved in cognitive decline and dementia in PD. In PD, although the relationship between RBD and cognitive dysfunctions was confirmed by considerable studies, whether RBD was associated with distinct types of cognitive defects is worth of study. Objectives: This systematic review summarizes the evidence relating to cognitive dysfunction in PD patients with RBD (PD-RBD) and those without and explores their specificity to cognitive domains. Methods: A meta-analysis using a random-effects model was performed for 16 different cognitive domains, including global cognitive function, memory (long-term verbal recall, long-term verbal recognition, long-term visual recall, short-term spatial recall, and short-term verbal recall), executive function (general, fluid reasoning, generativity, shifting, inhibition, and updating), language, processing speed/complex attention/working memory, visuospatial/constructional ability, and psychomotor ability. The cognitive difference between the groups of patients was measured as a standardized mean difference (SMD, Cohen's d). PD-RBD patients were classified into Confirmed-RBD (definite diagnosis with polysomnography, PSG) and Probable-RBD (without PSG re-confirmation). In some domains, RBD patients could not be analyzed separately due to the exiguity of primary studies; this analysis refers to such RBD patients as "Mixed-RBD." Results: Thirty-nine studies with 6,695 PD subjects were finally included. Confirmed-RBD patients showed worse performance than those without in global cognitive function, long-term verbal recall, long-term verbal recognition, generativity, inhibition, shifting, language, and visuospatial/constructional ability; Probable-RBD, in global cognitive function and shifting; and Mixed-RBD, in long-term visual recall, short-term spatial recall, general executive function, and processing speed/complex attention/working memory. Conclusion: This meta-analysis strongly suggests a relationship between RBD, Confirmed-RBD in particular, and cognitive dysfunctions in PD patients. Early and routine screening by sensitive and targeted cognitive tasks is necessary for all PD-RBD patients because it may offer the therapeutic time window before they evolve to irreversible dementia.

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.

Parkinson disease and the gut: new insights into pathogenesis and clinical relevance

The classic view portrays Parkinson disease (PD) as a motor disorder resulting from loss of substantia nigra pars compacta dopaminergic neurons. Multiple studies, however, describe prodromal, non-motor dysfunctions that affect the quality of life of patients who subsequently develop PD. These prodromal dysfunctions comprise a wide array of gastrointestinal motility disorders including dysphagia, delayed gastric emptying and chronic constipation. The histological hallmark of PD - misfolded α-synuclein aggregates that form Lewy bodies and neurites - is detected in the enteric nervous system prior to clinical diagnosis, suggesting that the gastrointestinal tract and its neural (vagal) connection to the central nervous system could have a major role in disease aetiology. This Review provides novel insights on the pathogenesis of PD, including gut-to-brain trafficking of α-synuclein as well as the newly discovered nigro-vagal pathway, and highlights how vagal connections from the gut could be the conduit by which ingested environmental pathogens enter the central nervous system and ultimately induce, or accelerate, PD progression. The pathogenic potential of various environmental neurotoxicants and the suitability and translational potential of experimental animal models of PD will be highlighted and appraised. Finally, the clinical manifestations of gastrointestinal involvement in PD and medications will be discussed briefly.

Synuclein in neurodegeneration

Neurodegenerative diseases are a heterogeneous group of disorders characterized by gradual progressive neuronal loss in the central nervous system. Unfortunately, the pathogenesis of many of these diseases remains unknown. Synucleins are a family of small, highly charged proteins expressed predominantly in neurons. Following their discovery, much has been learned about their structure, function, interaction with other proteins and role in neurodegenerative disease over the last two decades. One of these proteins, α-Synuclein (α-Syn), appears to be involved in many neurodegenerative disorders. These include Parkinson's disease (PD), dementia with Lewy bodies (DLB), Rapid Eye Movement Sleep Behavior Disorder (RBD) and Pure Autonomic Failure (PAF), i.e., collectively termed α-synucleinopathies. This review focuses on α-Syn dysfunction in neurodegeneration and assesses its role in synucleinopathies from a biochemical, genetic and neuroimaging perspective.

Brain White-Matter Degeneration Due to Aging and Parkinson Disease as Revealed by Double Diffusion Encoding

Microstructure imaging by means of multidimensional diffusion encoding is increasingly applied in clinical research, with expectations that it yields a parameter that better correlates with clinical disability than current methods based on single diffusion encoding. Under the assumption that diffusion within a voxel can be well described by a collection of diffusion tensors, several parameters of this diffusion tensor distribution can be derived, including mean size, variance of sizes, orientational dispersion, and microscopic anisotropy. The information provided by multidimensional diffusion encoding also enables us to decompose the sources of the conventional fractional anisotropy and mean kurtosis. In this study, we explored the utility of the diffusion tensor distribution approach for characterizing white-matter degeneration in aging and in Parkinson disease by using double diffusion encoding. Data from 23 healthy older subjects and 27 patients with Parkinson disease were analyzed. Advanced age was associated with greater mean size and size variances, as well as smaller microscopic anisotropy. By analyzing the parameters underlying diffusion kurtosis, we found that the reductions of kurtosis in aging and Parkinson disease reported in the literature are likely driven by the reduction in microscopic anisotropy. Furthermore, microscopic anisotropy correlated with the severity of motor impairment in the patients with Parkinson disease. The present results support the use of multidimensional diffusion encoding in clinical studies and are encouraging for its future clinical implementation.

Cognitive impairment in Parkinson's disease: A clinical and pathophysiological overview

Cognitive dysfunction in Parkinson's disease (PD) has received increasing attention, and, together with other non-motor symptoms, exert a significant functional impact in the daily lives of patients. This article aims to compile and briefly summarize selected published data about clinical features, cognitive evaluation, biomarkers, and pathophysiology of PD-related dementia (PDD). The literature search included articles indexed in the MEDLINE/PubMed database, published in English, over the last two decades. Despite significant progress on clinical criteria and cohort studies for PD-mild cognitive impairment (PD-MCI) and PDD, there are still knowledge gaps about its exact molecular and pathological basis. Here we overview the scientific literature on the role of functional circuits, neurotransmitter systems (monoaminergic and cholinergic), basal forebrain, and brainstem nuclei dysfunction in PD-MCI. Correlations between neuroimaging and cerebrospinal fluid (CSF) biomarkers, clinical outcomes, and pathological results are described to aid in uncovering the neurodegeneration pattern in PD-MCI and PDD.

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.

Weaker Braking Force, A New Marker of Worse Gait Stability in Alzheimer Disease

Background: Braking force is a gait marker associated with gait stability. This study aimed to determine the alteration of braking force and its correlation with gait stability in Alzheimer disease (AD).

Methods: A total of 32 AD patients and 32 healthy controls (HCs) were enrolled in this study. Gait parameters (braking force, gait variability, and fall risk) in the walking tests of Free walk, Barrier, and Count backward were measured by JiBuEn^® gait analysis system. Gait variability was calculated by the coefficient of variation (COV) of stride time, stance time, and swing time.

Results: The braking force of AD was significantly weaker than HCs in three walking tests (P < 0.001, P < 0.001, P = 0.007). Gait variability of AD showed significant elevation than HCs in the walking of Count backward (COV_stride: P = 0.013; COV_swing: P = 0.006). Fall risk of AD was significantly higher than HCs in three walking tests (P = 0.001, P = 0.001, P = 0.001). Braking force was negatively associated with fall risks in three walking tests (P < 0.001, P < 0.001, P < 0.001). There were significant negative correlations between braking force and gait variability in the walking of Free walk (COV_stride: P = 0.018; COV_swing: P = 0.013) and Barrier (COV_stride: P = 0.002; COV_swing: P = 0.001), but not Count backward (COV_stride: P = 0.888; COV_swing: P = 0.555).

Conclusion: Braking force was weaker in AD compared to HCs, reflecting the worse gait stability of AD. Our study suggests that weakening of braking force may be a new gait marker to indicate cognitive and motor impairment and predict fall risk in AD.

Characterization of idiopathic Parkinson's disease subgroups using quantitative gait analysis and corresponding subregional striatal uptake visualized using 18F-FP-CIT positron emission tomography

BACKGROUND

Gait disturbance is one of the most common symptoms among patients with idiopathic Parkinson's disease (IPD). Nevertheless, Parkinson's disease subtype clustering according to gait characteristics has not been thoroughly investigated.

RESEARCH QUESTION

The aim of this study was to identify subgroups according to gait pattern among patients with IPD.

METHODS

This study included 88 patients with IPD who underwent ¹⁸F-fluorinated-N-3-fluoropropyl-2-β-carboxymethoxy-3-β-4-iodophenyl-nortropane positron emission tomography (¹⁸F-FP-CIT PET) and three-dimensional gait analysis (3DGA) between January 1, 2014 and December 31, 2016. We performed cluster analysis using temporal-spatial gait variables (gait speed, stride length, cadence, and step width) and divided patients into four subgroups. The kinematic and kinetic gait variables in 3DGA were compared among the four subgroups. Furthermore, we compared the uptake patterns of striatum among the four subgroups using ¹⁸F-FP-CIT PET.

RESULTS

The patients were clustered into subgroups based on gait hypokinesia and cadence compensation. Group 1 had decreased stride length compensating with increased cadence. Group 2 had decreased stride length without cadence compensation and wider step width. Group 3 had relatively spared stride length with decreased cadence. Group 4 had spared stride length and cadence. The uptake of posterior putamen was significantly decreased in Group 3 compared with Group 4.

SIGNIFICANCE

Gait hypokinesia and cadence can help to classify gait patterns in IPD patients. Our subgroups may reflect the different gait patterns in IPD patients.

Therapy of Parkinson's Disease Subtypes

Early descriptions of subtypes of Parkinson's disease (PD) are dominated by the approach of predetermined groups. Experts defined, from clinical observation, groups based on clinical or demographic features that appeared to divide PD into clinically distinct subsets. Common bases on which to define subtypes have been motor phenotype (tremor dominant vs akinetic-rigid or postural instability gait disorder types), age, nonmotor dominant symptoms, and genetic forms. Recently, data-driven approaches have been used to define PD subtypes, taking an unbiased statistical approach to the identification of PD subgroups. The vast majority of data-driven subtyping has been done based on clinical features. Biomarker-based subtyping is an emerging but still quite undeveloped field. Not all of the subtyping methods have established therapeutic implications. This may not be surprising given that they were born largely from clinical observations of phenotype and not in observations regarding treatment response or biological hypotheses. The next frontier for subtypes research as it applies to personalized medicine in PD is the development of genotype-specific therapies. Therapies for GBA-PD and LRRK2-PD are already under development. This review discusses each of the major subtyping systems/methods in terms of its applicability to therapy in PD, and the opportunities and challenges designing clinical trials to develop the evidence base for personalized medicine based on subtypes.

Multi-layer Trajectory Clustering: a Network Algorithm for Disease Subtyping

Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work presents a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson's subtypes based on disease trajectory. Modeling patient-variable interactions as a bipartite network, TC first extracts communities of co-expressing disease variables at different stages of progression. Then, it identifies Parkinson's subtypes by clustering similar patient trajectories that are characterized by severity of disease variables through a multi-layer network. Determination of trajectory similarity accounts for direct overlaps between trajectories as well as second-order similarities, i.e., common overlap with a third set of trajectories. This work clusters trajectories across two types of layers: (a) temporal, and (b) ranges of independent outcome variable (representative of disease severity), both of which yield four distinct subtypes. The former subtypes exhibit differences in progression of disease domains (Cognitive, Mental Health etc.), whereas the latter subtypes exhibit different degrees of progression, i.e., some remain mild, whereas others show significant deterioration after 5 years. The TC approach is validated through statistical analyses and consistency of the identified subtypes with medical literature. This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized medicine.

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.

Mining genetic and transcriptomic data using machine learning approaches in Parkinson's disease

High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson’s disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.

High-resolution PET imaging reveals subtle impairment of the serotonin transporter in an early non-depressed Parkinson's disease cohort

PURPOSE

The serotonin transporter (SERT) is a biochemical marker for monoaminergic signaling in brain and has been suggested to be involved inthe pathophysiology of Parkinson's disease (PD). The aim of this PET study was to examine SERT availability in relevant brain regions in early stages ofnon-depressed PD patients.

METHODS

In a cross-sectional study, 18 PD patients (13 M/5F, 64 ± 7 years, range 46-74 years, disease duration 2.9 ± 2.6 years; UPDRS motor 21.9 ± 5.2) and 20 age- and gender-matched healthy control (HC) subjects (15 M/5F, 61 ± 7 years, range 50-72 years) were included. In a subsequent longitudinal phase, ten of the PD patients (7 M/3F, UPDRS motor 20.6 ± 6.9) underwent a second PET measurement after 18-24 months. After a 3-T MRI acquisition, baseline PET measurements were performed with [11C]MADAM using a high-resolution research tomograph. The non-displaceablebinding potential (BPND) was chosen as the outcome measure and was estimated at voxel level on wavelet-aided parametric images, by using the Logan graphical analysis and the cerebellum as reference region. A molecular template was generated to visualize and define different subdivisions of the raphe nuclei in the brainstem. Subortical and cortical regions of interest were segmented using FreeSurfer. Univariate analyses and multivariate network analyses were performed on the PET data.

RESULTS

The univariate region-based analysis showed no differences in SERT levels when the PD patients were compared with the HC neither at baseline or after 2 years of follow-up. The multivariate network analysis also showed no differences at baseline. However, prominent changes in integration and segregation measures were observed at follow-up, indicating a disconnection of the cortical and subcortical regions from the three nuclei of the raphe.

CONCLUSION

We conclude that the serotoninergic system in PD patients seems to become involved with a network dysregulation as the disease progresses, suggesting a disturbed serotonergic signaling from raphe nuclei to target subcortical and cortical regions.

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.

Progressive brain atrophy and clinical evolution in Parkinson's disease

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Cortical and subcortical atrophy is accelerated early after the onset of PD.

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Brain atrophy in PD progressed with cognitive, non-motor and mood deficits.

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Structural MRI may be useful for predicting disease progression in PD.

Clinical manifestations and evolution are very heterogeneous among individuals with Parkinson’s disease (PD). The aims of this study were to investigate the pattern of progressive brain atrophy in PD according to disease stage and to elucidate to what extent cortical thinning and subcortical atrophy are related to clinical motor and non-motor evolution. 154 patients at different PD stages were assessed over time using motor, non-motor and structural MRI evaluations for a maximum of 4 years. Cluster analysis defined clinical subtypes. Cortical thinning and subcortical atrophy were assessed at baseline in patients relative to 60 healthy controls. Longitudinal trends of brain atrophy progression were compared between PD clusters. The contribution of brain atrophy in predicting motor, non-motor, cognitive and mood deterioration was explored. Two main PD clusters were defined: mild (N = 87) and moderate-to-severe (N = 67). Two mild subtypes were further identified: mild motor-predominant (N = 43) and mild-diffuse (N = 44), with the latter group being older and having more severe non-motor and cognitive symptoms. The initial pattern of brain atrophy was more severe in patients with moderate-to-severe PD. Over time, mild-diffuse PD patients had the greatest brain atrophy accumulation in the cortex and the left hippocampus, while less distributed atrophy progression was observed in moderate-to-severe and mild motor-predominant patients. Baseline and 1-year cortical thinning was associated with long-term progression of motor, cognitive, non-motor and mood symptoms. Cortical and subcortical atrophy is accelerated early after the onset of PD and becomes prominent in later stages of disease according to the development of cognitive, non-motor and mood dysfunctions. Structural MRI may be useful for monitoring and predicting disease progression in PD.

The TRACK-PD study: protocol of a longitudinal ultra-high field imaging study in Parkinson's disease

BACKGROUND

The diagnosis of Parkinson's Disease (PD) remains a challenge and is currently based on the assessment of clinical symptoms. PD is also a heterogeneous disease with great variability in symptoms, disease course, and response to therapy. There is a general need for a better understanding of this heterogeneity and the interlinked long-term changes in brain function and structure in PD. Over the past years there is increasing interest in the value of new paradigms in Magnetic Resonance Imaging (MRI) and the potential of ultra-high field strength imaging in the diagnostic work-up of PD. With this multimodal 7 T MRI study, our objectives are: 1) To identify distinctive MRI characteristics in PD patients and to create a diagnostic tool based on these differences. 2) To correlate MRI characteristics to clinical phenotype, genetics and progression of symptoms. 3) To detect future imaging biomarkers for disease progression that could be valuable for the evaluation of new therapies.

METHODS

The TRACK-PD study is a longitudinal observational study in a cohort of 130 recently diagnosed (≤ 3 years after diagnosis) PD patients and 60 age-matched healthy controls (HC). A 7 T MRI of the brain will be performed at baseline and repeated after 2 and 4 years. Complete assessment of motor, cognitive, neuropsychiatric and autonomic symptoms will be performed at baseline and follow-up visits with wearable sensors, validated questionnaires and rating scales. At baseline a blood DNA sample will also be collected.

DISCUSSION

This is the first longitudinal, observational, 7 T MRI study in PD patients. With this study, an important contribution can be made to the improvement of the current diagnostic process in PD. Moreover, this study will be able to provide valuable information related to the different clinical phenotypes of PD and their correlating MRI characteristics. The long-term aim of this study is to better understand PD and develop new biomarkers for disease progression which may help new therapy development. Eventually, this may lead to predictive models for individual PD patients and towards personalized medicine in the future.

TRIAL REGISTRATION

Dutch Trial Register, NL7558 . Registered March 11, 2019.

Clinical Characterization of Parkinson's Disease Patients With Cognitive Impairment

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

Subphenotyping depression using machine learning and electronic health records

Objective

To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.

Materials and Methods

Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.

Results

Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.

Conclusion

Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.

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.

Hospitalization Rates and Comorbidities in Patients with Progressive Supranuclear Palsy in Germany from 2010 to 2017

Progressive supranuclear palsy (PSP) belongs to the disease spectrum of Parkinsonian syndromes. Due to the chronic and progressive neurodegenerative course of the disease, PSP patients often have to be hospitalized to undergo diagnostic and therapeutic measures. The dynamics and characteristics of PSP inpatient treatment in Germany have not been investigated thus far. The current study analyzed trends of inpatient treatment in Germany for the years 2010–2017 based on the German DRG statistics (“diagnostic-related groups”) in the category G23.- (other degenerative diseases of the basal ganglia) and with special focus on PSP (G23.1). Inpatient case numbers of the G23.- category comprised a total of 21,196 patients from 2010–2017, whereas the PSP subcategory (G23.1) amounted to 10,663 cases. In the analyzed time period, PSP patient numbers constantly increased from 963 in 2010 to 1780 in 2017 with yearly growth rates of up to 20%. Similar trends were observed for other Parkinsonian syndromes such as multiple system atrophy (MSA). Differentiating PSP inpatients by gender demonstrated a higher proportion of males (55–60%) in comparison to female patients for the entire observation period. The average age of hospitalized PSP patients over these years was between 72.3 and 73.4 years without relevant differences for gender. The most common comorbidities consisted of cardiovascular, neurological, muscular and urological disorders. In summary, the analysis demonstrates that PSP patients are increasingly hospitalized in Germany and the current concepts of stationary care have to differentiate standard practices for Parkinson’s disease (PD) to also address the needs of patients with PSP and other Parkinsonian syndromes.

Predicting Progression in Parkinson's Disease Using Baseline and 1-Year Change Measures

BACKGROUND

Improved prediction of Parkinson's disease (PD) progression is needed to support clinical decision-making and to accelerate research trials.

OBJECTIVES

To examine whether baseline measures and their 1-year change predict longer-term progression in early PD.

METHODS

Parkinson's Progression Markers Initiative study data were used. Participants had disease duration ≤2 years, abnormal dopamine transporter (DAT) imaging, and were untreated with PD medications. Baseline and 1-year change in clinical, cerebrospinal fluid (CSF), and imaging measures were evaluated as candidate predictors of longer-term (up to 5 years) change in Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) score and DAT specific binding ratios (SBR) using linear mixed-effects models.

RESULTS

Among 413 PD participants, median follow-up was 5 years. Change in MDS-UPDRS from year-2 to last follow-up was associated with disease duration (β= 0.351; 95% CI = 0.146, 0.555), male gender (β= 3.090; 95% CI = 0.310, 5.869), and baseline (β= -0.199; 95% CI = -0.315, -0.082) and 1-year change (β= 0.540; 95% CI = 0.423, 0.658) in MDS-UPDRS; predictors in the model accounted for 17.6% of the variance in outcome. Predictors of percent change in mean SBR from year-2 to last follow-up included baseline rapid eye movement sleep behavior disorder score (β= -0.6229; 95% CI = -1.2910, 0.0452), baseline (β= 7.232; 95% CI = 2.268, 12.195) and 1-year change (β= 45.918; 95% CI = 35.994,55.843) in mean striatum SBR, and 1-year change in autonomic symptom score (β= -0.325;95% CI = -0.695, 0.045); predictors in the model accounted for 44.1% of the variance.

CONCLUSIONS

Baseline clinical, CSF, and imaging measures in early PD predicted change in MDS-UPDRS and dopamine-transporter binding, but the predictive value of the models was low. Adding the short-term change of possible predictors improved the predictive value, especially for modeling change in dopamine-transporter binding.

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.

White Matter Hyperintensities Mediate Impact of Dysautonomia on Cognition in Parkinson's Disease

Background

Patients with Parkinson's disease (PD) present with a broad spectrum of nonmotor features including autonomic disorders. More severe autonomic dysfunction in PD is associated with increased cognitive deficits. The presence of cerebral small-vessel disease, measured by T2-weighted magnetic resonance imaging white matter hyperintensity (WMH) burden, is also observed in patients with PD with faster cognitive decline.

Objective

To investigate whether baseline orthostatic hypotension and autonomic dysfunction in early-stage PD affect later cognitive decline via mediation through cerebral small-vessel disease.

Methods

De novo PD patients (N = 365) and age-matched controls (N = 174) with baseline T2-weighted/ fluid-attenuated inversion recovery scans were selected from the Parkinson's Progression Markers Initiative. WMHs were automatically segmented. Mediation analysis was used to assess whether WMH load mediates the effect of orthostatic hypotension and autonomic dysfunction (measured by Scales for Outcomes in Parkinson's Disease-Autonomic) on future cognitive decline (measured by Montreal Cognitive Assessment) in an average of 4 years of follow-up.

Results

Mediation analysis supported the existence of a full mediation of WMHs on the effect of diastolic orthostatic hypotension in patients with PD and future cognitive decline (average causal mediation effect: ab = -0.032, 95% confidence interval = -0.064 to -0.01, P = 0.01). There was also a partial mediation for overall autonomic dysfunction (ab = -0.027, 95% confidence interval = -0.054 to 0.00, P = 0.02).

Conclusions

WMHs fully mediate the effect of diastolic orthostatic hypotension and partially mediate the effect of autonomic dysregulation on future cognitive decline in patients with PD. Our findings support the hypothesis that autonomic dysfunction in early clinical stages predisposes the brain to WMHs through dysregulation of the blood flow in the small vessels. This in turn increases the risk of future cognitive impairment in early PD.

NfL as a biomarker for neurodegeneration and survival in Parkinson disease

OBJECTIVE

To determine whether neurofilament light chain protein in CSF (cNfL), a sensitive biomarker of neuroaxonal damage, reflects disease severity or can predict survival in Parkinson disease (PD).

METHODS

We investigated whether disease severity, phenotype, or survival in patients with new-onset PD correlates with cNfL concentrations around the time of diagnosis in the population-based New Parkinsonism in Umeå (NYPUM) study cohort (n = 99). A second, larger new-onset PD cohort (n = 194) was used for independent validation. Association of brain pathology with the cNfL concentration was examined with striatal dopamine transporter imaging and repeated diffusion tensor imaging at baseline and 1 and 3 years.

RESULTS

Higher cNfL in the early phase of PD was associated with greater severity of all cardinal motor symptoms except tremor in both cohorts and with shorter survival and impaired olfaction. cNfL concentrations above the median of 903 ng/L conferred an overall 5.8 times increased hazard of death during follow-up. After adjustment for age and sex, higher cNfL correlated with striatal dopamine transporter uptake deficits and lower fractional anisotropy in diffusion tensor imaging of several axonal tracts.

CONCLUSIONS

cNfL shows usefulness as a biomarker of disease severity and to predict survival in PD. The present results indicate that the cNfL concentration reflects the intensity of the neurodegenerative process, which could be important in future clinical trials.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in patients with PD, cNfL concentrations are associated with more severe disease and shorter survival.

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.

Early autonomic and cognitive dysfunction in PD, DLB and MSA: blurring the boundaries between α-synucleinopathies

Differential diagnosis between Parkinson's disease, dementia with Lewy bodies and multiple system atrophy can be difficult, especially because in early phase they might present with overlapping clinical features. Notably, orthostatic hypotension and cognitive dysfunction are common nonmotor aspects of parkinsonian syndromes and can be both present from the earliest stages of all α-synucleinopathies, indicating a common neurobiological basis in their strong relationship. In view of the increasing awareness about the prevalence of mild cognitive dysfunction in multiple system atrophy, the relevance of autonomic dysfunction in demented parkinsonian patients, the critical role of non-motor symptoms in clustering Parkinson's disease patients and the shift to studying patients in the prodromal phase, we will discuss some intrinsic limitations of current clinical diagnostic criteria, even when applied by movement disorder specialists. In particular, we will focus on the early coexistence of autonomic and cognitive dysfunction in the setting of overt or latent parkinsonism as pitfalls in the differential diagnosis of α-synucleinopathies. As early and accurate diagnosis remains of outmost importance for counselling of patients and timely enrolment into disease-modifying clinical trials, a continuous effort of research community is ongoing to further improve the clinical diagnostic accuracy of α-synucleinopathies.

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.

Identifying and predicting Parkinson's disease subtypes through trajectory clustering via bipartite networks

Chronic medical conditions show substantial heterogeneity in their clinical features and progression. We develop the novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of disease subtypes and 2) early prediction of subtype/disease progression patterns. TPC is an easily generalizable method that identifies subtypes by clustering patients with similar disease trajectory profiles, based not only on Parkinson’s Disease (PD) variable severity, but also on their complex patterns of evolution. TPC is derived from bipartite networks that connect patients to disease variables. Applying our TPC algorithm to a PD clinical dataset, we identify 3 distinct subtypes/patient clusters, each with a characteristic progression profile. We show that TPC predicts the patient’s disease subtype 4 years in advance with 72% accuracy for a longitudinal test cohort. Furthermore, we demonstrate that other types of data such as genetic data can be integrated seamlessly in the TPC algorithm. In summary, using PD as an example, we present an effective method for subtype identification in multidimensional longitudinal datasets, and early prediction of subtypes in individual patients.

Evolution of prodromal Parkinson's disease and dementia with Lewy bodies: a prospective study

Parkinson's disease has a long prodromal stage with various subclinical motor and non-motor manifestations; however, their evolution in the years before Parkinson's disease is diagnosed is unclear. We traced the evolution of early motor and non-motor manifestations of synucleinopathy from the stage of idiopathic rapid eye movement (REM) sleep behaviour disorder until defined neurodegenerative disease. During 2004-16, we recruited and then annually followed 154 polysomnography-proven patients with idiopathic REM sleep behaviour disorder, of whom 55 phenoconverted to defined parkinsonism or dementia. Longitudinal data on multiple prodromal features, including the Unified Parkinson's Disease Rating Scale parts I-III, quantitative motor tests, olfaction, colour vision, cognition, and autonomic functions were gathered annually (average = five follow-up visits, range: 2-12 years). The same measures were also assessed in 102 age- and sex-matched healthy control subjects. By looking backward from the time of dementia or parkinsonism diagnosis, we examined trajectories of each prodromal feature using mixed effect models. Based on analysis, olfactory loss was first to develop, with predicted onset >20 years before phenoconversion. This was followed by impaired colour vision, constipation, and erectile dysfunction, starting 10-16 years prior to phenoconversion. At 7-9 years before phenoconversion, slight urinary dysfunction and subtle cognitive decline could be detected. Among motor symptoms altered handwriting, turning in bed, walking, salivation, speech, and facial expression began to be disrupted starting 7-11 years prior to parkinsonism diagnosis, but remained mild until soon before phenoconversion. Motor examination abnormalities began 5-7 years before phenoconversion, with the alternate tap test having the longest interval (8 years before phenoconversion). Among cardinal motor phenotypes, bradykinesia appeared first, ∼5-6 years prior to phenoconversion, followed by rigidity (Year -3) and tremor (Year -2). With direct prospective evaluation of an idiopathic REM sleep behaviour disorder cohort during phenoconversion, we documented an evolution of prodromal manifestations similar to that predicted by pathological staging models, with predicted prodromal intervals as long as 20 years.

Progression of grey and white matter brain damage in Parkinson's disease: a critical review of structural MRI literature

The current review summarizes the current knowledge on longitudinal cortical and subcortical grey and white matter MRI findings assessed using T1-weighted and one-tensor diffusion-weighted MRI in Parkinson's disease (PD) patients. Results were reviewed according to disease duration, disease severity and cognitive impairment. The most consistent findings are those showing a progressive cortical atrophy accumulation in caudate, putamen, temporal/hippocampal, frontal and parietal areas in de novo PD cases and in the early/middle phase of the disease, with the achievement of a plateau in the later stage. Analyzing results according to the patient cognitive status, only a few studies used longitudinal MRI metrics to predict mild cognitive impairment or dementia conversion in PD patients, suggesting that atrophy of the hippocampus, fronto-temporal areas, caudate, thalamus and accumbens might play a role in this process. Stratifying patients according to disease severity, findings appear partially controversial, although showing a progressive atrophy of basal ganglia over 1 year of follow up and a widespread cortical thinning over 3-6 years in mild to moderate PD patients. Finally, microstructural damage of the main motor and associative WM tracts seems to be present, and rapidly progress, even in the early phase of PD. The utility of structural MRI metrics as biomarkers of PD progression and their role in improving the accuracy of disease progression prediction is still debated.

Brain atrophy in Parkinson's disease with polysomnography-confirmed REM sleep behavior disorder

We aimed to investigate cortical and subcortical brain alterations in people with Parkinson's disease with polysomnography-confirmed rapid eye movement (REM) sleep behavior disorder (RBD). Thirty people with Parkinson's disease, including 15 people with RBD, were recruited and compared with 41 healthy controls. Surface-based cortical and subcortical analyses were performed on T1-weighted images to investigate thickness and shape abnormalities between groups, and voxel-based and deformation-based morphometry were performed to investigate local volume. Correlations were performed in patients to investigate the structural correlates of motor activity during REM sleep. People with RBD showed cortical thinning in the right perisylvian and inferior temporal cortices and shape contraction in the putamen compared with people without RBD. Compared with controls, people with RBD had extensive cortical thinning and volume loss, brainstem volume was reduced, and shape contraction was found in the basal ganglia and hippocampus. In comparison to controls, people without RBD showed more restricted thinning in the sensorimotor, parietal, and occipital cortices, reduced volume in the brainstem, temporal and more posterior areas, and shape contraction in the pallidum and hippocampus. In Parkinson's disease, higher tonic and phasic REM sleep motor activity was associated with contraction of the thalamic surface, extensive cortical thinning, and subtle volume reduction in the middle temporal gyrus. In Parkinson's disease, the presence of RBD is associated with extensive cortical and subcortical abnormalities, suggesting more severe neurodegeneration in people with RBD. This provides potential neuroanatomical correlates for the more severe clinical phenotype reported in people with Parkinson's disease with RBD.

Parkinsonism as a late presentation of lymphomatosis cerebri following high-dose chemotherapy with autologous stem cell transplantation for primary central nervous system lymphoma

Primary central nervous system lymphoma is an aggressive form of non-Hodgkin lymphoma arising in the eyes, meninges, spinal cord, or brain. Treatment of primary CNS lymphoma with a combination of high-dose chemotherapy and autologous stem cell transplantation has been shown to have high rates of remission which is frequently sustained for multiple years. Recurrence of primary CNS lymphoma generally presents with one or multiple contrast enhancing lesions on MRI. In rare cases, lymphoma cells may proliferate diffusely within the brain parenchyma without mass formation, a pattern termed lymphomatosis cerebri. Lymphomatosis cerebri presents a significant diagnostic challenge, and has not been reported to present with parkinsonism. Here, we present a case of initially mass forming, contrast-enhancing primary CNS lymphoma which remitted following chemotherapy and autologous stem cell transplantation, and recurred 7 years post-transplant with symptoms of parkinsonism and a lack of typical lesions on imaging, with lymphomatosis cerebri confirmed at autopsy.

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.

MRI biomarkers of motor and non-motor symptoms in Parkinson's disease

Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.

Neuropathological correlates of parkinsonian disorders in a large Dutch autopsy series

The clinical diagnosis in patients with parkinsonian disorders can be challenging, and a definite diagnosis requires neuropathological confirmation. The aim of this study was to examine whether a clinical diagnosis of Parkinson’s disease (PD) and atypical parkinsonian disorders predict the presence of Lewy pathology (LP) and concomitant neuropathological lesions.

We included 293 donors with a history of parkinsonism without dementia at disease onset, collected by the Netherlands Brain Bank (NBB) from 1989 to 2015. We retrospectively categorized donors according the International Parkinson and Movement Disorder Society clinical diagnostic criteria for PD (MDS-PD criteria) as ‘not PD’, ‘probable PD’ or ‘established PD’. We compared the final clinical diagnosis to presence of neuropathological lesions as defined by BrainNet Europe and National Institute on Aging – Alzheimer's Association guidelines.

LP was present in 150 out of 176 donors (85%) with a clinical diagnosis of PD, in 8 out of 101 donors (8%) with atypical parkinsonian disorders and in 4 out of 16 donors (25%) without a definite clinical diagnosis. Independent from age at death, stages of amyloid-β, but not neurofibrillary tau or neuritic plaques, were higher in donors with LP compared to other types of pathology (p = 0.009). The MDS-PD criteria at a certainty level of ‘probable PD’ predicted presence of LP with a diagnostic accuracy of 89.3%. Among donors with LP, ‘established PD’ donors showed similar Braak α-synuclein stages and stages of amyloid-β, neurofibrillary tau and neuritic plaques compared to ‘not PD’ or ‘probable PD’ donors.

In conclusion, both a clinical diagnosis of PD as well as MDS-PD criteria accurately predicted presence of LP in NBB donors. LP was associated with more widespread amyloid-β pathology, suggesting a link between amyloid-β accumulation and LP formation.

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.