Gut microbiota in Parkinson's disease : Temporal stability and relations to disease progression

Background: Several publications have described differences in cross-sectional comparisons of gut microbiota between patients with Parkinson's disease and control subjects, with considerable variability of the reported differentially abundant taxa. The temporal stability of such microbiota alterations and their relationship to disease progression have not been previously studied with a high-throughput sequencing based approach. Methods: We collected clinical data and stool samples from 64 Parkinson's patients and 64 control subjects twice, on average 2.25 years apart. Disease progression was evaluated based on changes in Unified Parkinson's Disease Rating Scale and Levodopa Equivalent Dose, and microbiota were characterized with 16S rRNA gene amplicon sequencing. Findings: We compared patients to controls, and patients with stable disease to those with faster progression. There were significant differences between microbial communities of patients and controls when corrected for confounders, but not between timepoints. Specific bacterial taxa that differed between patients and controls at both timepoints included several previously reported ones, such as Roseburia, Prevotella and Bifidobacterium. In progression comparisons, differentially abundant taxa were inconsistent across methods and timepoints, but there was some support for a different distribution of enterotypes and a decreased abundance of Prevotella in faster-progressing patients. Interpretation: The previously detected gut microbiota differences between Parkinson's patients and controls persisted after 2 years. While we found some evidence for a connection between microbiota and disease progression, a longer follow-up period is required to confirm these findings. (C) 2019 The Authors. Published by Elsevier B.V.Peer reviewe

Oral and nasal microbiota in Parkinson's disease

Introduction: Parkinson's disease (PD) is associated with neuropathological changes in olfactory and gastrointestinal tissues, and PD patients frequently suffer from hyposmia, hyposalivation, and dysphagia. Since hyposmia and gastrointestinal dysfunction are frequently premotor symptoms, it has been speculated that an external, for example microbial, agent could trigger the pathologic process in the corresponding organs, subsequently spreading to the central nervous system. We recently showed evidence for compositional differences between the fecal microbiota of PD patients and control subjects. In this study, our objective was to explore a possible connection between nasal and oral microbiota and PD. Methods: We compared the oral and nasal bacterial communities of PD patients (oral: n = 72, nasal: n = 69) and control subjects (oral: n = 76, nasal: n = 67) using a 16S rRNA gene amplicon sequencing approach. Results: Oral and nasal microbiota differed markedly from each other, with no notable similarity within subjects. Oral microbiota of PD patients and control subjects had differences in beta diversity and abundances of individual bacterial taxa. An increase in the abundance of opportunistic oral pathogens was detected in males, both with and without PD. Our data did not reveal convincing differences between the nasal microbiota of control subjects and PD patients. Conclusion: The oral microbiome deserves additional research regarding its connection to PD and its biomarker potential. The higher abundance of oral pathogens in men underlines the importance of monitoring and promoting male dental health. (C) 2017 Elsevier Ltd. All rights reserved.Peer reviewe

Manifestations of intellectual disability, dystonia, and Parkinson’s disease in an adult patient with ARX gene mutation c.558_560dup p.(Pro187dup)

Abstract We describe a 38-year-old male patient with intellectual disability and progressive motor symptoms who lacked an etiological diagnosis for many years. Finally, clinical exome sequencing showed a likely pathogenic variant of the ARX gene suggesting Partington syndrome. His main symptoms were mild intellectual disability, severe kinetic apraxia, resting and action tremor, dysarthria, tonic pupils, constant dystonia of one upper limb, and focal dystonia in different parts of the body, axial rigidity, spasticity, epilepsy, and poor sleep. Another likely pathogenic gene variant was observed in the PKP2 gene and is in accordance with the observed early cardiomyopathy. Single-photon emission computed tomography imaging of dopamine transporters showed a reduced signal in the basal ganglia consistent with Parkinson’s disease. Therapies with a variable number of drugs, including antiparkinsonian medications, have yielded poor responses. Our case report extends the picture of the adult phenotype of Partington syndrome

A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity

Synaptic disruption is an early pathological sign of the neurodegeneration of Dementia of the Alzheimer's type (DAT). The changes in network synchronization are evident in patients with Mild Cognitive Impairment (MCI) at the group level, but there are very few Magnetoencephalography (MEG) studies regarding discrimination at the individual level. In an international multicenter study, we used MEG and functional connectivity metrics to discriminate MCI from normal aging at the individual person level. A labeled sample of features (links) that distinguished MCI patients from controls in a training dataset was used to classify MCI subjects in two testing datasets from four other MEG centers. We identified a pattern of neuronal hypersynchronization in MCI, in which the features that best discriminated MCI were fronto-parietal and interhemispheric links. The hypersynchronization pattern found in the MCI patients was stable across the five different centers, and may be considered an early sign of synaptic disruption and a possible preclinical biomarker for MCI/DAT. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).Peer reviewe

Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease

Abstract In the quantification of symptoms of Parkinson’s disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed. 42 PD patients and 23 control subjects were monitored with Garmin Vivosmart 4 wearable device and asked to fill a symptom and medication diary with a mobile application, at home, for about four weeks. Subsequent analyses are based on continuous accelerometer data from the device. Accelerometer data from the Levodopa Response Study (MJFFd) were reanalyzed, with symptoms quantified with linear spectral models trained on expert evaluations present in the data. Variational autoencoders (VAE) were trained on both our study accelerometer data and on MJFFd to detect movement states (e.g., walking, standing). A total of 7590 self-reported symptoms were recorded during the study. 88.9% (32/36) of PD patients, 80.0% (4/5) of DBS PD patients and 95.5% (21/22) of control subjects reported that using the wearable device was very easy or easy. Recording a symptom at the time of the event was assessed as very easy or easy by 70.1% (29/41) of subjects with PD. Aggregated spectrograms of the collected accelerometer data show relative attenuation of low (<5Hz) frequencies in patients. Similar spectral patterns also separate symptom periods from immediately adjacent non-symptomatic periods. Discriminative power of linear models to separate symptoms from adjacent periods is weak, but aggregates show partial separability of patients vs. controls. The analysis reveals differential symptom detectability across movement tasks, motivating the third part of the study. VAEs trained on either dataset produced embedding from which movement states in MJFFd could be predicted. A VAE model was able to detect the movement states. Thus, a pre-detection of these states with a VAE from accelerometer data with good S/N ratio, and subsequent quantification of PD symptoms is a feasible strategy. The usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Finally, the usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients

A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity

Synaptic disruption is an early pathological sign of the neurodegeneration of Dementia of the Alzheimer's type (DAT). The changes in network synchronization are evident in patients with Mild Cognitive Impairment (MCI) at the group level, but there are very few Magnetoencephalography (MEG) studies regarding discrimination at the individual level. In an international multicenter study, we used MEG and functional connectivity metrics to discriminate MCI from normal aging at the individual person level. A labeled sample of features (links) that distinguished MCI patients from controls in a training dataset was used to classify MCI subjects in two testing datasets from four other MEG centers. We identified a pattern of neuronal hypersynchronization in MCI, in which the features that best discriminated MCI were fronto-parietal and interhemispheric links. The hypersynchronization pattern found in the MCI patients was stable across the five different centers, and may be considered an early sign of synaptic disruption and a possible preclinical biomarker for MCI/DAT