Clinical trials

Habitual adherence to a predominantly plant-based diet, rich in low-processed food (LPF) has been associated with a reduced risk for development and slower progression of Parkinson's Disease (PD). This could be due to neuroprotective effects by modulation of the gut microbiota and decreased neuronal and metabolic inflammation. So far, the effect of a predominantly plant-based LPF-diet on the microbiome-immune-brain axis in patients with PD remains unknown. In addition, the influence of dietetic measures on the gut microbiome is variable and may depend on (long-term) adherence as well as on PD-specific factors and lifestyle. The investigators hypothesize that compared to an average German diet, the predominantly plant-based New Nordic LPF-diet, as a culturally adapted diet, which is rich in fermentable fiber and phytochemicals, will have beneficial effects on the gut microbiome of patients with PD by increasing the abundance of short-chain fatty acid (SCFA)-producing bacteria (primary outcome) and will improve gut motility, metabolic resilience, and inflammation (secondary outcomes). Furthermore, the investigators postulate that a patient-centered dietary intervention program, including a multifaceted patient education and supported by a web-application, will lead to high adherence as a key determinant of long-term changes in the gut microbiome. This dietary intervention will be accepted by patients as a low-threshold treatment that balances personal benefits, therapeutic barriers and ethical concerns of early risk disclosure in PD.

Aim of the study is to evaluate the impact of a vegan vs. vegetarian dietary pattern compared to an omnivore diet on body weight, saliva and stool microbiome profiles, as well as saliva, urine and plasma metabolome profile changes in freshmen after the first year at university/college.

The focus of the study is on patients Parkinson's disease showing as well behavioral disorders that can be described as pathological and are summarized under the term impulse control disorder (ICD). Changes in behavior and also pathological disorders are a common side effect of treatment for Parkinson's disease. The goal of this academic study is to compare the effect of surgical (deep brain stimulation, DBS) treatment combined with a coordinated and adapted best medical treatment (BMT) to be compared with the effect of optimized best medical treatment (BMT) alone. The stimulation arm (DBS+BMT) as well as the medication arm (BMT only) will be monitored according to clinical routine. Participants will have to agree to be randomly assigned to either deep brain stimulation in combination with the best medical treatment (DBS group) or the best medical treatment alone (BMT group). Participants will have to come regularly according to clinical routine to the clinic and complete various questionaires and scales for the study.

The neurological examination (NE) is a cornerstone of clinical neurology, with ocular motor assessment being a key component. Technology offers an opportunity to augment and standardize parts of the NE. Eye-tracking systems provide objective quantitative data on eye movements by continuously tracking the eye over time. This data can be used to derive parameters like saccadic latency, gaze velocity, and fixation stability with a precision that is impossible to achieve through human observation by neurologists. The integration of such technology could enhance the traditional NE. Before such technology can be widely adopted, its feasibility and acceptability in a clinical population must be established. The primary purpose of this study is to assess the usability of a novel eye-tracking system from the patient's perspective when used in a clinical settings. A secondary purpose is to determine if quantitative data from the eye-tracker correlate with the findings of the traditional clinical neurological examination and to explore whether eye-tracking can provide additional, complementary information not typically captured by standard clinical assessment. To achieve these aims, the study will assess several outcome measures. The primary outcome measure is the Usability of the Eye-Tracking System, which will be measured using the System Usability Scale (SUS). Beyond the primary objectives, this study will investigate two secondary objectives. The first involves assessing the relationship between quantitative eye-tracking parameters and clinical ocular motor assessment. Specifically, the investigators will analyze objective, numerical data obtained from eye-tracking systems and the clinician's subjectively graded assessment of ocular movements derived from the standard neurological examination. The second is the exploratory analysis of novel eye-tracking biomarkers. This involves quantifying and analyzing eye-tracking parameters not typically assessed during a routine NE. For example, the dynamics of the pupillary light reflex or the frequency of microsaccades. The aim is to identify potential digital biomarkers that could provide additional objective insights into ocular motor function and neurological status.

Parkinson's disease (PD) affects over 10 million worldwide, causing unstable gait and falls in 70% of patients despite medication. This leads to confidence loss, isolation, fractures, and hospitalizations. Treadmill training, augmented by mechanical/virtual-reality triggers, has proven effective in enhancing gait and reducing falls. However, underlying treadmill training mechanisms are unclear. To personalize training, we'll explore how PD patients benefit and transfer effects to daily life. This trial is part of three parallel randomized controlled trials within the Steps Against the Burden of Parkinson's Disease (CT-IDs: 6ef2e427b002, 6ef2e427b003, 6ef2e427b004) project, which will perform a pooled analysis across all sites in addition to individual RCT analyses. Each trial adheres to a shared core protocol while allowing for adaptations in the perturbation protocol, ensuring that data can be combined. Importantly, mechanistic findings and outcomes from this specific RCT will be reported independently, but also as part of a pooled analysis. In this trials, PD patients will undergo treadmill training with and without adaptaions (perturbations). 12 sessions of treadmill training will be provided, with pre/post assessments and a follow-up with pre/post assessments and a follow-up at 8 to 12 weeks after the post assessment. For post treadmill training a phone app will be offered as a home-based speed dependent walk training intervention. This intervention is an App based training for gait adaptability and allows users to set their own training time and pace. It delivers a rhythmic metronomic beat for three different walking speeds, designed to trigger movement and encourage better walking patterns.. Gait improvements are expected, driven by sensorimotor integration improving balance control. Biomechanical data analysis will reveal enhanced foot placement control. Neurophysiological changes will be studied through EEG and EMG, aiming to find improved gait stability with reduced EEG beta power and increased EEG-EMG coherence. Gait improvement in the lab might not correlate with daily-life results. Gait self-efficacy could influence transfer, prompting investigation into mechanistic associations with mobility outcomes. Remote digital tools will assess week-long mobility outcomes, employing machine learning to comprehend why some improve both in lab and life, while others don't. This will uncover mechanisms translating treatment effects into real-world outcomes, aiding personalized intervention development.

Parkinson's disease (PD) affects over 10 million worldwide, causing unstable gait and falls in 70% of patients despite medication. This leads to confidence loss, isolation, fractures, and hospitalizations. Treadmill training, augmented by mechanical/virtual-reality triggers, has proven effective in enhancing gait and reducing falls. However, underlying treadmill training mechanisms are unclear. To personalize training, we'll explore how PD patients benefit and transfer effects to daily life. This trial is part of three parallel randomized controlled trials within the Steps Against the Burden of Parkinson's Disease (CT-IDs: 6ef2e427b002, 6ef2e427b003, 6ef2e427b004) project, which will perform a pooled analysis across all sites in addition to individual RCT analyses. Each trial adheres to a shared core protocol while allowing for adaptations in the perturbation protocol, ensuring that data can be combined. Importantly, mechanistic findings and outcomes from this specific RCT will be reported independently, but also as part of a pooled analysis. In this trials, PD patients will undergo treadmill training with and without adaptations (perturbations). 12 sessions of treadmill training will be provided, with pre/post assessments and a Follow-up 12±2 weeks following T1 with pre/post assessments and a Follow-up 12±2 weeks following T1 at 8 to 12 weeks after the post assessment. For post treadmill training a phone app will be offered as a home-based speed dependent walk training intervention. This intervention is an App based training for gait adaptability and allows users to set their own training time and pace. It delivers a rhythmic metronomic beat for three different walking speeds, designed to trigger movement and encourage better walking patterns. Gait improvements are expected, driven by sensorimotor integration improving balance control. Biomechanical data analysis will reveal enhanced foot placement control. Neurophysiological changes will be studied through EEG and EMG, aiming to find improved gait stability with reduced EEG beta power and increased EEG-EMG coherence. Gait improvement in the lab might not correlate with daily-life results. Gait self-efficacy could influence transfer, prompting investigation into mechanistic associations with mobility outcomes. Remote digital tools will assess week-long mobility outcomes, employing machine learning to comprehend why some improve both in lab and life, while others don't. This will uncover mechanisms translating treatment effects into real-world outcomes, aiding personalized intervention development.