IgA-Biome Profiles Correlate with Clinical Parkinson's Disease Subtypes.

BACKGROUND: Parkinson's disease is a heterogeneous neurodegenerative disorder with distinctive gut microbiome patterns suggesting that interventions targeting the gut microbiota may prevent, slow, or reverse disease progression and severity. OBJECTIVE: Because secretory IgA (SIgA) plays a key role in shaping the gut microbiota, characterization of the IgA-Biome of individuals classified into either the akinetic rigid (AR) or tremor dominant (TD) Parkinson's disease clinical subtypes was used to further define taxa unique to these distinct clinical phenotypes. METHODS: Flow cytometry was used to separate IgA-coated and -uncoated bacteria from stool samples obtained from AR and TD patients followed by amplification and sequencing of the V4 region of the 16 S rDNA gene on the MiSeq platform (Illumina). RESULTS: IgA-Biome analyses identified significant alpha and beta diversity differences between the Parkinson's disease phenotypes and the Firmicutes/Bacteroides ratio was significantly higher in those with TD compared to those with AR. In addition, discriminant taxa analyses identified a more pro-inflammatory bacterial profile in the IgA+ fraction of those with the AR clinical subclass compared to IgA-Biome analyses of those with the TD subclass and with the taxa identified in the unsorted control samples. CONCLUSION: IgA-Biome analyses underscores the importance of the host immune response in shaping the gut microbiome potentially affecting disease progression and presentation. In the present study, IgA-Biome analyses identified a unique proinflammatory microbial signature in the IgA+ fraction of those with AR that would have otherwise been undetected using conventional microbiome analysis approaches.

Progression of clinical markers in prodromal Parkinson's disease and dementia with Lewy bodies: a multicentre study.

The neurodegenerative synucleinopathies, including Parkinson's disease and dementia with Lewy bodies, are characterized by a typically lengthy prodromal period of progressive subclinical motor and non-motor manifestations. Among these, idiopathic REM sleep behaviour disorder is a powerful early predictor of eventual phenoconversion, and therefore represents a critical opportunity to intervene with neuroprotective therapy. To inform the design of randomized trials, it is essential to study the natural progression of clinical markers during the prodromal stages of disease in order to establish optimal clinical end points. In this study, we combined prospective follow-up data from 28 centres of the International REM Sleep Behavior Disorder Study Group representing 12 countries. Polysomnogram-confirmed REM sleep behaviour disorder subjects were assessed for prodromal Parkinson's disease using the Movement Disorder Society criteria and underwent periodic structured sleep, motor, cognitive, autonomic and olfactory testing. We used linear mixed-effect modelling to estimate annual rates of clinical marker progression stratified by disease subtype, including prodromal Parkinson's disease and prodromal dementia with Lewy bodies. In addition, we calculated sample size requirements to demonstrate slowing of progression under different anticipated treatment effects. Overall, 1160 subjects were followed over an average of 3.3 ± 2.2 years. Among clinical variables assessed continuously, motor variables tended to progress faster and required the lowest sample sizes, ranging from 151 to 560 per group (at 50% drug efficacy and 2-year follow-up). By contrast, cognitive, olfactory and autonomic variables showed modest progression with higher variability, resulting in high sample sizes. The most efficient design was a time-to-event analysis using combined milestones of motor and cognitive decline, estimating 117 per group at 50% drug efficacy and 2-year trial duration. Finally, while phenoconverters showed overall greater progression than non-converters in motor, olfactory, cognitive and certain autonomic markers, the only robust difference in progression between Parkinson's disease and dementia with Lewy bodies phenoconverters was in cognitive testing. This large multicentre study demonstrates the evolution of motor and non-motor manifestations in prodromal synucleinopathy. These findings provide optimized clinical end points and sample size estimates to inform future neuroprotective trials.

Fecal microbiota transplantation in Parkinson's disease-A randomized repeat-dose, placebo-controlled clinical pilot study.

Background and purpose: The intestinal microbiome plays a primary role in the pathogenesis of neurodegenerative disorders and may provide an opportunity for disease modification. We performed a pilot clinical study looking at the safety of fecal microbiota transplantation (FMT), its effect on the microbiome, and improvement of symptoms in Parkinson's disease. Methods: This was a randomized, double-blind placebo-controlled pilot study, wherein orally administered lyophilized FMT product or matching placebo was given to 12 subjects with mild to moderate Parkinson's disease with constipation twice weekly for 12 weeks. Subjects were followed for safety and clinical improvement for 9 additional months (total study duration 12 months). Results: Fecal microbiota transplantation caused non-severe transient upper gastrointestinal symptoms. One subject receiving FMT was diagnosed with unrelated metastatic cancer and was removed from the trial. Beta diversity (taxa) of the microbiome, was similar comparing placebo and FMT groups at baseline, however, for subjects randomized to FMT, it increased significantly at 6 weeks (p = 0.008) and 13 weeks (p = 0.0008). After treatment with FMT, proportions of selective families within the phylum Firmicutes increased significantly, while proportion of microbiota belonging to Proteobacteria were significantly reduced. Objective motor findings showed only temporary improvement while subjective symptom improvements were reported compared to baseline in the group receiving FMT. Constipation, gut transient times (NS), and gut motility index (p = 0.0374) were improved in the FMT group. Conclusions: Subjects with Parkinson's disease tolerated multi-dose-FMT, and experienced increased diversity of the intestinal microbiome that was associated with reduction in constipation and improved gut transit and intestinal motility. Fecal microbiota transplantation administration improved subjective motor and non-motor symptoms. Clinical trial registration: ClinicalTrial.gov, identifier: NCT03671785.

Updated Parkinson's disease motor subtypes classification and correlation to cerebrospinal homovanillic acid and 5-hydroxyindoleacetic acid levels.

Introduction: Motor classifications of Parkinson's Disease (PD) have been widely used. This paper aims to update a subtype classification using the MDS-UPDRS-III and determine if cerebrospinal neurotransmitter profiles (HVA and 5-HIAA) differ between these subtypes in a cohort from the Parkinson's Progression Marker Initiative (PPMI). Methods: UPDRS and MDS-UPDRS scores were collected for 20 PD patients. Akinetic-rigid (AR), Tremor-dominant (TD), and Mixed (MX) subtypes were calculated using a formula derived from UPDRS, and a new ratio was developed for subtyping patients with the MDS-UPDRS. This new formula was subsequently applied to 95 PD patients from the PPMI dataset, and subtyping was correlated to neurotransmitter levels. Data were analyzed using receiver operating characteristic models and ANOVA. Results: Compared to previous UPDRS classifications, the new MDS-UPDRS TD/AR ratios produced significant areas under the curve (AUC) for each subtype. The optimal sensitivity and specificity cutoff scores were ≥0.82 for TD, ≤0.71 for AR, and >0.71 and <0.82 for Mixed. Analysis of variance showed that the AR group had significantly lower HVA and 5-HIAA levels than the TD and HC groups. A logistic model using neurotransmitter levels and MDS-UPDRS-III could predict the subtype classification. Conclusions: This MDS-UPDRS motor classification system provides a method to transition from the original UPDRS to the new MDS-UPDRS. It is a reliable and quantifiable subtyping tool for monitoring disease progression. The TD subtype is associated with lower motor scores and higher HVA levels, while the AR subtype is associated with higher motor scores and lower 5-HIAA levels.

Computational medication regimen for Parkinson's disease using reinforcement learning.

Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson's disease patients, the Parkinson's Progression Markers Initiative database, we derived clinically relevant disease states and an optimal combination of medications for each of them by using policy iteration of the Markov decision process (MDP). We focused on 8 combinations of medications, i.e., Levodopa, a dopamine agonist, and other PD medications, as possible actions and motor symptom severity, based on the Unified Parkinson Disease Rating Scale (UPDRS) section III, as reward/penalty of decision. We analyzed a total of 5077 visits from 431 PD patients with 55.5 months follow-up. We excluded patients without UPDRS III scores or medication records. We derived a medication regimen that is comparable to a clinician's decision. The RL model achieved a lower level of motor symptom severity scores than what clinicians did, whereas the clinicians' medication rules were more consistent than the RL model. The RL model followed the clinician's medication rules in most cases but also suggested some changes, which leads to the difference in lowering symptoms severity. This is the first study to investigate RL to improve the pharmacological approach of PD patients. Our results contribute to the development of an interactive machine-physician ecosystem that relies on evidence-based medicine and can potentially enhance PD management.

Allogeneic Bone Marrow-Derived Mesenchymal Stem Cell Safety in Idiopathic Parkinson's Disease.

BACKGROUND: Neuroinflammation plays a key role in PD pathogenesis, and allogeneic bone marrow-derived mesenchymal stem cells can be used as an immunomodulatory therapy. OBJECTIVE: The objective of this study was to prove the safety and tolerability of intravenous allogeneic bone marrow-derived mesenchymal stem cells in PD patients. METHODS: This was a 12-month single-center open-label dose-escalation phase 1 study of 20 subjects with mild/moderate PD assigned to a single intravenous infusion of 1 of 4 doses: 1, 3, 6, or 10 × 106 allogeneic bone marrow-derived mesenchymal stem cells/kg, evaluated 3, 12, 24, and 52 weeks postinfusion. Primary outcome safety measures included transfusion reaction, study-related adverse events, and immunogenic responses. Secondary outcomes included impact on peripheral markers, PD progression, and changes in brain perfusion. RESULTS: There were no serious adverse reactions related to the infusion and no responses to donor-specific human leukocyte antigens. Most common treatment-emergent adverse events were dyskinesias (20%, n = 4) with 1 emergent and 3 exacerbations; and hypertension (20%, n = 4) with 3 transient episodes and 1 requiring medical intervention. One possibly related serious adverse event occurred in a patient with a 4-year history of lymphocytosis who developed asymptomatic chronic lymphocytic leukemia. Peripheral inflammation markers appear to be reduced at 52 weeks in the highest dose including, tumor necrosis factor-α (P < 0.05), chemokine (C-C motif) ligand 22 (P < 0.05), whereas brain-derived neurotrophic factor (P < 0.05) increased. The highest dose seems to have demonstrated the most significant effect at 52 weeks, reducing the OFF state UPDRS motor, -14.4 (P < 0.01), and total, -20.8 (P < 0.05), scores. CONCLUSION: A single intravenous infusion of allogeneic bone marrow-derived mesenchymal stem cells at doses of 1, 3, 6, or 10 × 106 allogeneic bone marrow-derived mesenchymal stem cells/kg is safe, well tolerated, and not immunogenic in mild/moderate PD patients. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Toward a Multimodal Computer-Aided Diagnostic Tool for Alzheimer's Disease Conversion.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient's ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model's decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

A Study of the Relationship Between Uric Acid and Substantia Nigra Brain Connectivity in Patients With REM Sleep Behavior Disorder and Parkinson's Disease.

Low levels of the natural antioxidant uric acid (UA) and the presence of REM sleep behavior disorder (RBD) are both associated with an increased likelihood of developing Parkinson's disease (PD). RBD and PD are also accompanied by basal ganglia dysfunction including decreased nigrostriatal and nigrocortical resting state functional connectivity. Despite these independent findings, the relationship between UA and substantia nigra (SN) functional connectivity remains unknown. In the present study, voxelwise analysis of covariance was used in a cross-sectional design to explore the relationship between UA and whole-brain SN functional connectivity using the eyes-open resting state fMRI method in controls without RBD, patients with idiopathic RBD, and PD patients with and without RBD. The results showed that controls exhibited a positive relationship between UA and SN functional connectivity with left lingual gyrus. The positive relationship was reduced in patients with RBD and PD with RBD, and the relationship was found to be negative in PD patients. These results are the first to show differential relationships between UA and SN functional connectivity among controls, prodromal, and diagnosed PD patients in a ventral occipital region previously documented to be metabolically and structurally altered in RBD and PD. More investigation, including replication in longitudinal designs with larger samples, is needed to understand the pathophysiological significance of these changes.

Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach.

Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population. There is a long preclinical period followed by mild cognitive impairment (MCI). Clinical diagnosis and the rate of decline is variable. Progression monitoring remains a challenge in AD, and it is imperative to create better tools to quantify this progression. Brain magnetic resonance imaging (MRI) is commonly used for patient assessment. However, current approaches for analysis require strong a priori assumptions about regions of interest used and complex preprocessing pipelines including computationally expensive non-linear registrations and iterative surface deformations. These preprocessing steps are composed of many stacked processing layers. Any error or bias in an upstream layer will be propagated throughout the pipeline. Failures or biases in the non-linear subject registration and the subjective choice of atlases of specific regions are common in medical neuroimaging analysis and may hinder the translation of many approaches to the clinical practice. Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas, or non-linear registration steps. Our approach is trained end-to-end and learns how a patient's brain structure dynamically changes between two-time points directly from the raw voxels. We compare our approach with Freesurfer longitudinal pipelines and voxel-based methods using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model can identify AD progression with comparable results to existing Freesurfer longitudinal pipelines without the need of predefined regions of interest, non-rigid registration algorithms, or iterative surface deformation at a fraction of the processing time. When compared to other voxel-based methods which share some of the same benefits, our model showed a statistically significant performance improvement. Additionally, we show that our model can differentiate between healthy subjects and patients with MCI. The model's decision was investigated using the epsilon layer-wise propagation algorithm. We found that the predictions were driven by the pallidum, putamen, and the superior temporal gyrus. Our novel longitudinal based, deep learning approach has the potential to diagnose patients earlier and enable new computational tools to monitor neurodegeneration in clinical practice.

Longitudinal Connectomes as a Candidate Progression Marker for Prodromal Parkinson's Disease.

Parkinson's disease is the second most prevalent neurodegenerative disorder in the Western world. It is estimated that the neuronal loss related to Parkinson's disease precedes the clinical diagnosis by more than 10 years (prodromal phase) which leads to a subtle decline that translates into non-specific clinical signs and symptoms. By leveraging diffusion magnetic resonance imaging brain (MRI) data evaluated longitudinally, at least at two different time points, we have the opportunity of detecting and measuring brain changes early on in the neurodegenerative process, thereby allowing early detection and monitoring that can enable development and testing of disease modifying therapies. In this study, we were able to define a longitudinal degenerative Parkinson's disease progression pattern using diffusion magnetic resonance imaging connectivity information. Such pattern was discovered using a de novo early Parkinson's disease cohort (n = 21), and a cohort of Controls (n = 30). Afterward, it was tested in a cohort at high risk of being in the Parkinson's disease prodromal phase (n = 16). This progression pattern was numerically quantified with a longitudinal brain connectome progression score. This score is generated by an interpretable machine learning (ML) algorithm trained, with cross-validation, on the longitudinal connectivity information of Parkinson's disease and Control groups computed on a nigrostriatal pathway-specific parcellation atlas. Experiments indicated that the longitudinal brain connectome progression score was able to discriminate between the progression of Parkinson's disease and Control groups with an area under the receiver operating curve of 0.89 [confidence interval (CI): 0.81-0.96] and discriminate the progression of the High Risk Prodromal and Control groups with an area under the curve of 0.76 [CI: 0.66-0.92]. In these same subjects, common motor and cognitive clinical scores used in Parkinson's disease research showed little or no discriminative ability when evaluated longitudinally. Results suggest that it is possible to quantify neurodegenerative patterns of progression in the prodromal phase with longitudinal diffusion magnetic resonance imaging connectivity data and use these image-based patterns as progression markers for neurodegeneration.

Earlier Intervention with Deep Brain Stimulation for Parkinson's Disease.

Neuromodulation of subcortical areas of the brain as therapy to reduce Parkinsonian motor symptoms was developed in the mid-twentieth century and went through many technical and scientific advances that established specific targets and stimulation parameters. Deep Brain Stimulation (DBS) was approved by the FDA in 2002 as neuromodulation therapy for advanced Parkinson's disease, prompting several randomized controlled trials that confirmed its safety and effectiveness. The implantation of tens of thousands of patients in North America and Europe ignited research into its potential role in early disease stages and the therapeutic benefit of DBS compared to best medical therapy. In 2013 the EARLY-STIM trial provided Class I evidence for the use of DBS earlier in Parkinson's disease. This finding led to the most recent FDA approval in patients with at least 4 years of disease duration and 4 months of motor complications as an adjunct therapy for patients not adequately controlled with medications. This following review highlights the historical development and advances made overtime in DBS implantation, the current application, and the challenges that come with it.

Slowed Prosaccades and Increased Antisaccade Errors As a Potential Behavioral Biomarker of Multiple System Atrophy.

Current clinical diagnostic tools are limited in their ability to accurately differentiate idiopathic Parkinson's disease (PD) from multiple system atrophy (MSA) and other parkinsonian disorders early in the disease course, but eye movements may stand as objective and sensitive markers of disease differentiation and progression. To assess the use of eye movement performance for uniquely characterizing PD and MSA, subjects diagnosed with PD (N = 21), MSA (N = 11), and age-matched controls (C, N = 20) were tested on the prosaccade and antisaccade tasks using an infrared eye tracker. Twenty of these subjects were retested ~7 months later. Saccade latencies, error rates, and longitudinal changes in saccade latencies were measured. Both PD and MSA patients had greater antisaccade error rates than C subjects, but MSA patients exhibited longer prosaccade latencies than both PD and C patients. With repeated testing, antisaccade latencies improved over time, with benefits in C and PD but not MSA patients. In the prosaccade task, the normal latencies of the PD group show that basic sensorimotor oculomotor function remain intact in mid-stage PD, whereas the impaired latencies of the MSA group suggest additional degeneration earlier in the disease course. Changes in antisaccade latency appeared most sensitive to differences between MSA and PD across short time intervals. Therefore, in these mid-stage patients, increased antisaccade errors combined with slowed prosaccade latencies might serve as a useful marker for early differentiation between PD and MSA, and, antisaccade performance, a measure of MSA progression. Together, our findings suggest that eye movements are promising biomarkers for early differentiation and progression of parkinsonian disorders.

Serum Lymphocyte-Associated Cytokine Concentrations Change More Rapidly over Time in Multiple System Atrophy Compared to Parkinson Disease.

OBJECTIVE: Chronic inflammatory processes contribute to the eventual death of motor neurons and the development of symptoms in both idiopathic Parkinson disease (PD) and multiple system atrophy (MSA). Given the faster rate of progression and more severe symptoms associated with MSA, we hypothesized that markers of inflammation would be more evident in the peripheral blood of MSA than PD patients, and that evidence of this inflammation might assist early diagnosis of MSA versus PD. METHODS: We performed multiplex analysis to determine the concentrations of 37 immune-associated cytokines and chemokines isolated from the plasma of patients with PD (n = 25) and MSA (n = 14) and compared our results to those of age-matched controls (n = 15). We then applied a mixed-effect multiple regression model to determine if the concentration of cytokines in the plasma of patients with PD and MSA changed significantly over time. RESULTS: Patients with MSA had a trend towards overall lower levels of immune-associated cytokines, while serum cytokine levels were increased in patients with PD. Statistically adjusted comparisons of overall changes in cytokine concentrations between the PD and MSA groups revealed higher concentrations of T-cell-associated cytokines TNFß and IL-7 in PD. Comparison of samples taken over time revealed significantly faster rates of change in 4 different cytokine concentrations (IL-4, IL-15, IL-2, and IL-9) in patients with MSA versus patients with PD. CONCLUSIONS: Our results suggest that single measurements of plasma concentrations of inflammation-associated cytokines cannot be used to distinguish disease states. However, measurements made over time may correlate with pathogenesis. The significant changes in T-cell-associated cytokines may shed light on immune mechanisms that contribute to PD and MSA disease progression.

Altered nigrostriatal and nigrocortical functional connectivity in rapid eye movement sleep behavior disorder.

STUDY OBJECTIVES: Rapid eye movement sleep behavior disorder (RBD) is a condition closely associated with Parkinson disease (PD). RBD is a sleep disturbance that frequently manifests early in the development of PD, likely reflecting disruption in normal functioning of anatomical areas affected by neurodegenerative processes. Although specific neuropathological aspects shared by RBD and PD have yet to be fully documented, further characterization is critical to discovering reliable biomarkers that predict PD onset. In the current study, we tested the hypothesis of altered functional connections of the substantia nigra (SN) in patients in whom RBD was diagnosed. DESIGN: Between-groups, single time point imaging. SETTING: UTHSC-H 3 telsa MRI center. PARTICIPANTS: Ten patients with RBD, 11 patients with PD, and 10 age-matched controls. INTERVENTIONS: NA. MEASUREMENTS AND RESULTS: We measured correlations of SN time series using resting state blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) in patients with idiopathic RBD who were at risk for developing PD, patients in whom PD was diagnosed, and age-matched controls. Using voxelwise analysis of variance, different correlations (P < 0.01, whole-brain corrected) between left SN and left putamen were found in patients with RBD compared with controls and patients with PD. SN correlations with right cuneus/precuneus and superior occipital gyrus were significantly different for patients with RBD compared with both controls and patients with PD. CONCLUSIONS: The results suggest that altered nigrostriatal and nigrocortical connectivity characterizes rapid eye movement sleep behavior disorder before onset of obvious motor impairment. The functional changes are discussed in the context of degeneration in dopaminergic and cognition-related networks.