Evaluation of severity of predominantly non-dopaminergic symptoms in Parkinson's disease: The SENS-PD scale

Safety and feasibility of faecal microbiota transplantation for patients with Parkinson's disease: a protocol for a self-controlled interventional donor-FMT pilot study

INTRODUCTION

Experimental studies suggest a role of gut microbiota in the pathophysiology of Parkinson's disease (PD) via the gut-brain axis. The gut microbiota can also influence the metabolism of levodopa, which is the mainstay of treatment of PD. Therefore, modifying the gut microbiota by faecal microbiota transplantation (FMT) could be a supportive treatment strategy.

METHODS AND ANALYSIS

We have developed a study protocol for a single-centre, prospective, self-controlled, interventional, safety and feasibility donor-FMT pilot study with randomisation and double-blinded allocation of donor faeces. The primary objectives are feasibility and safety of FMT in patients with PD. Secondary objectives include exploring whether FMT leads to alterations in motor complications (fluctuations and dyskinesias) and PD motor and non-motor symptoms (including constipation), determining alterations in gut microbiota composition, assessing donor-recipient microbiota similarities and their association with PD symptoms and motor complications, evaluating the ease of the study protocol and examining FMT-related adverse events in patients with PD. The study population will consist of 16 patients with idiopathic PD that use levodopa and experience motor complications. They will receive FMT with faeces from one of two selected healthy human donors. FMT will be administered via a gastroscope into the duodenum, after treatment with oral vancomycin, bowel lavage and domperidone. There will be seven follow-up moments during 12 months.

ETHICS AND DISSEMINATION

This study was approved by the Medical Ethical Committee Leiden Den Haag Delft (ref. P20.087). Study results will be disseminated through publication in peer-reviewed journals and international conferences.

TRIAL REGISTRATION NUMBER

International Clinical Trial Registry Platform: NL9438.

Sensor Measurements Can Characterize Fluctuations and Wearing Off in Parkinson’s Disease and Guide Therapy to Improve Motor, Non-motor and Quality of Life Scores

Objectives

The aim was to examine the role of sensor measurement in identifying and managing fluctuations in bradykinesia of Parkinson’s Disease.

Method

Clinical scales and data from wearable sensors obtained before and after optimization of treatment from 107 participants who participated in a previous study was used. Fluctuators were identified by a levodopa response or wearing off in their sensor data and were subdivided according to whether the sensor’s bradykinesia scores were in target range, representing acceptable bradykinesia for part of the dose (Controlled Fluctuator: n = 22) or above target for the whole dose period (Uncontrolled Fluctuator; n = 28). Uncontrolled Non-fluctuators (n = 24) were cases without a levodopa response or wearing-off and sensor bradykinesia scores above target throughout the day (un-controlled). Controlled Non-fluctuators (n = 33) were below target throughout the day (controlled) and used as a reference for good control (MDS-UPDRS III = 33 ± 8.6 and PDQ39 = 28 ± 18).

Results

Treating Fluctuators significantly improved motor and quality of life scores. Converting fluctuators into Controlled Non-fluctuators significantly improved motor, non-motor and quality of life scores and a similar but less significant improvement was obtained by conversion to a Controlled Fluctuator. There was a significantly greater likelihood of achieving these changes when objective measurement was used to guide management.

Conclusions

The sensor’s classification of fluctuators bore a relation to severity of clinical scores and treatment of fluctuation improved clinical scores. The sensor measurement aided in recognizing and removing fluctuations with treatment and resulted in better clinical scores, presumably by assisting therapeutic decisions.

Transcriptomic Signatures Associated With Regional Cortical Thickness Changes in Parkinson's Disease

Cortical atrophy is a common manifestation in Parkinson's disease (PD), particularly in advanced stages of the disease. To elucidate the molecular underpinnings of cortical thickness changes in PD, we performed an integrated analysis of brain-wide healthy transcriptomic data from the Allen Human Brain Atlas and patterns of cortical thickness based on T1-weighted anatomical MRI data of 149 PD patients and 369 controls. For this purpose, we used partial least squares regression to identify gene expression patterns correlated with cortical thickness changes. In addition, we identified gene expression patterns underlying the relationship between cortical thickness and clinical domains of PD. Our results show that genes whose expression in the healthy brain is associated with cortical thickness changes in PD are enriched in biological pathways related to sumoylation, regulation of mitotic cell cycle, mitochondrial translation, DNA damage responses, and ER-Golgi traffic. The associated pathways were highly related to each other and all belong to cellular maintenance mechanisms. The expression of genes within most pathways was negatively correlated with cortical thickness changes, showing higher expression in regions associated with decreased cortical thickness (atrophy). On the other hand, sumoylation pathways were positively correlated with cortical thickness changes, showing higher expression in regions with increased cortical thickness (hypertrophy). Our findings suggest that alterations in the balanced interplay of these mechanisms play a role in changes of cortical thickness in PD and possibly influence motor and cognitive functions.

Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration after Subthalamic Deep Brain Stimulation

Background

Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha‐synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.

Objective

To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.

Methods

Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1‐year follow‐up. A total of 16,674 EEG‐features were extracted per patient; a Boruta algorithm selected EEG‐features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10‐fold cross‐validation with Bayesian optimization provided class‐differentiation.

Results

Tweny‐five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited.

Conclusions

Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Regional Structural Hippocampal Differences Between Dementia with Lewy Bodies and Parkinson's Disease

Background:

Dementia with Lewy bodies (DLB) and Parkinson’s disease (PD) are considered subtypes of the α-synucleinopathy continuum that show similar and dissimilar clinical and morphological features.

Objective:

To further our understanding of brain abnormalities that might differentiate both disorders more clearly, we performed quantitative magnetic resonance (MR) imaging of the subcortical and cortical grey matter.

Methods:

Three-dimensional T1 weighted 3 tesla MR images of 14 DLB and 62 age- and gender-matched PD patients were examined to study cortical and subcortical grey matter structure. We used volumetric measurements to study total grey matter, and volumes of the pallidum, amygdala, putamen, caudate nucleus, thalamus and hippocampus. Whole-brain and structural network-based methods were used to identify local differences in grey matter and vertex-based shape analysis was used to assess focal hippocampal changes.

Results:

Volumetric, whole-brain and network-based analyses showed reduced hippocampal (p = 0.008) and right parahippocampal region volumes (p = 0.030) in DLB compared to PD patients. Shape analysis showed atrophy in the head and body of the right (p = 0.040) and in the head of the left (p = 0.030) hippocampus of DLB patients.

Conclusion:

DLB patients showed atrophy of the hippocampus and parahippocampal gyrus compared to PD patients with a differential involvement of the head and body of the hippocampus. Further studies should examine if these group-based findings can be used to differentiate both disorders on an individual level.

Peripheral mitochondrial function correlates with clinical severity in idiopathic Parkinson's disease

Background

Parkinson's disease is an intractable disorder with heterogeneous clinical presentation that may reflect different underlying pathogenic mechanisms. Surrogate indicators of pathogenic processes correlating with clinical measures may assist in better patient stratification. Mitochondrial function, which is impaired in and central to PD pathogenesis, may represent one such surrogate indicator.

Methods

Mitochondrial function was assessed by respirometry experiment in fibroblasts derived from idiopathic patients (n = 47) in normal conditions and in experimental settings that do not permit glycolysis and therefore force energy production through mitochondrial function. Respiratory parameters and clinical measures were correlated with bivariate analysis. Machine‐learning‐based classification and regression trees were used to classify patients on the basis of biochemical and clinical measures. The effects of mitochondrial respiration on α‐synuclein stress were assessed monitoring the protein phosphorylation in permitting versus restrictive glycolysis conditions.

Results

Bioenergetic properties in peripheral fibroblasts correlate with clinical measures in idiopathic patients, and the correlation is stronger with predominantly nondopaminergic signs. Bioenergetic analysis under metabolic stress, in which energy is produced solely by mitochondria, shows that patients’ fibroblasts can augment respiration, therefore indicating that mitochondrial defects are reversible. Forcing energy production through mitochondria, however, favors α‐synuclein stress in different cellular experimental systems. Machine‐learning‐based classification identified different groups of patients in which increasing disease severity parallels higher mitochondrial respiration.

Conclusion

The suppression of mitochondrial activity in PD may be an adaptive strategy to cope with concomitant pathogenic factors. Moreover, mitochondrial measures in fibroblasts are potential peripheral biomarkers to follow disease progression. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Altered Whole-Brain and Network-Based Functional Connectivity in Parkinson's Disease

Background: Functional imaging methods, such as resting-state functional magnetic resonance imaging, reflect changes in neural connectivity and may help to assess the widespread consequences of disease-specific network changes in Parkinson's disease. In this study we used a relatively new graph analysis approach in functional imaging: eigenvector centrality mapping. This model-free method, applied to all voxels in the brain, identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. In other neurological disorders, eigenvector centrality mapping has been linked to changes in functional connectivity in certain nodes of brain networks.

Objectives: Examining changes in functional brain connectivity architecture on a whole brain and network level in patients with Parkinson's disease.

Methods: Whole brain resting-state functional architecture was studied with a recently introduced graph analysis approach (eigenvector centrality mapping). Functional connectivity was further investigated in relation to eight known resting-state networks. Cross-sectional analyses included group comparison of functional connectivity measures of Parkinson's disease patients (n = 107) with control subjects (n = 58) and correlations with clinical data, including motor and cognitive impairment and a composite measure of predominantly non-dopaminergic symptoms.

Results: Eigenvector centrality mapping revealed that frontoparietal regions were more prominent in the whole-brain network function in patients compared to control subjects, while frontal and occipital brain areas were less prominent in patients. Using standard resting-state networks, we found predominantly increased functional connectivity, namely within sensorimotor system and visual networks in patients. Regional group differences in functional connectivity of both techniques between patients and control subjects partly overlapped for highly connected posterior brain regions, in particular in the posterior cingulate cortex and precuneus. Clinico-functional imaging relations were not found.

Conclusions: Changes on the level of functional brain connectivity architecture might provide a different perspective of pathological consequences of Parkinson's disease. The involvement of specific, highly connected (hub) brain regions may influence whole brain functional network architecture in Parkinson's disease.

Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease

BACKGROUND

In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data.

METHODS

3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD.

RESULTS

The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, ηp2 = 0.070) and p = 0.001 (β = - 0.264, ηp2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks.

CONCLUSIONS

We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.