scholarly journals Metabolomic Profiling of Bile Acids in an Experimental Model of Prodromal Parkinson’s Disease

Metabolites ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 71 ◽  
Author(s):  
Stewart Graham ◽  
Nolwen Rey ◽  
Zafer Ugur ◽  
Ali Yilmaz ◽  
Eric Sherman ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Logistic Regression ◽  
Early Diagnosis ◽  
Bile Acids ◽  
Cross Validation ◽  
High Accuracy ◽  
Accurate Diagnosis ◽  
Metabolomic Profiling ◽  
Potential Biomarkers

For people with Parkinson’s disease (PD), considered the most common neurodegenerative disease behind Alzheimer’s disease, accurate diagnosis is dependent on many factors; however, misdiagnosis is extremely common in the prodromal phases of the disease, when treatment is thought to be most effective. Currently, there are no robust biomarkers that aid in the early diagnosis of PD. Following previously reported work by our group, we accurately measured the concentrations of 18 bile acids in the serum of a prodromal mouse model of PD. We identified three bile acids at significantly different concentrations (p < 0.05) when mice representing a prodromal PD model were compared with controls. These include ω-murichoclic acid (MCAo), tauroursodeoxycholic acid (TUDCA) and ursodeoxycholic acid (UDCA). All were down-regulated in prodromal PD mice with TUDCA and UDCA at significantly lower levels (17-fold and 14-fold decrease, respectively). Using the concentration of three bile acids combined with logistic regression, we can discriminate between prodromal PD mice from control mice with high accuracy (AUC (95% CI) = 0.906 (0.777–1.000)) following cross validation. Our study highlights the need to investigate bile acids as potential biomarkers that predict PD and possibly reflect the progression of manifest PD.

Current Therapeutic Strategies and Perspectives for Neuroprotection in Parkinson’s Disease

2020 ◽  
Vol 26 (37) ◽  
pp. 4738-4746
Author(s):  
Mohan K. Ghanta ◽  
P. Elango ◽  
Bhaskar L. V. K. S.
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Therapeutic Strategies ◽  
Neuroprotective Agents ◽  
Striatal Neurons ◽  
The Status ◽  
Advanced Stages ◽  
Dopaminergic Pathways

Parkinson’s disease is a progressive neurodegenerative disorder of dopaminergic striatal neurons in basal ganglia. Treatment of Parkinson’s disease (PD) through dopamine replacement strategies may provide improvement in early stages and this treatment response is related to dopaminergic neuronal mass which decreases in advanced stages. This treatment failure was revealed by many studies and levodopa treatment became ineffective or toxic in chronic stages of PD. Early diagnosis and neuroprotective agents may be a suitable approach for the treatment of PD. The essentials required for early diagnosis are biomarkers. Characterising the striatal neurons, understanding the status of dopaminergic pathways in different PD stages may reveal the effects of the drugs used in the treatment. This review updates on characterisation of striatal neurons, electrophysiology of dopaminergic pathways in PD, biomarkers of PD, approaches for success of neuroprotective agents in clinical trials. The literature was collected from the articles in database of PubMed, MedLine and other available literature resources.

scholarly journals The Role of Salivary Biomarkers in the Early Diagnosis of Alzheimer’s Disease and Parkinson’s Disease

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 371
Author(s):  
Patrycja Pawlik ◽  
Katarzyna Błochowiak
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis ◽  
Neurodegenerative Diseases ◽  
Diagnostic Tool ◽  
Clinical Symptoms ◽  
Early Screening ◽  
Salivary Biomarkers

Many neurodegenerative diseases present with progressive neuronal degeneration, which can lead to cognitive and motor impairment. Early screening and diagnosis of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are necessary to begin treatment before the onset of clinical symptoms and slow down the progression of the disease. Biomarkers have shown great potential as a diagnostic tool in the early diagnosis of many diseases, including AD and PD. However, screening for these biomarkers usually includes invasive, complex and expensive methods such as cerebrospinal fluid (CSF) sampling through a lumbar puncture. Researchers are continuously seeking to find a simpler and more reliable diagnostic tool that would be less invasive than CSF sampling. Saliva has been studied as a potential biological fluid that could be used in the diagnosis and early screening of neurodegenerative diseases. This review aims to provide an insight into the current literature concerning salivary biomarkers used in the diagnosis of AD and PD. The most commonly studied salivary biomarkers in AD are β-amyloid1-42/1-40 and TAU protein, as well as α-synuclein and protein deglycase (DJ-1) in PD. Studies continue to be conducted on this subject and researchers are attempting to find correlations between specific biomarkers and early clinical symptoms, which could be key in creating new treatments for patients before the onset of symptoms.

scholarly journals Can Biomarkers Help the Early Diagnosis of Parkinson’s Disease?

2017 ◽  
Vol 33 (5) ◽  
pp. 535-542 ◽  
Author(s):  
Weidong Le ◽  
Jie Dong ◽  
Song Li ◽  
Amos D. Korczyn
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Diagnosis

Predictors of Pharyngeal Dysphagia in Patients with Parkinson’s Disease

2020 ◽  
Vol 10 (4) ◽  
pp. 1727-1735
Author(s):  
Inga Claus ◽  
Paul Muhle ◽  
Judith Suttrup ◽  
Bendix Labeit ◽  
Sonja Suntrup-Krueger ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Logistic Regression ◽  
Rating Scale ◽  
Equivalent Dose ◽  
Binary Logistic Regression Analysis ◽  
Screening Methods ◽  
Clinical Predictors ◽  
Pharyngeal Dysphagia ◽  
Increased Risk

Background: Diagnosis of pharyngeal dysphagia in patients with Parkinson’s disease is often difficult as reliable screening methods are lacking so far and clinical examination fails to adequately assess the pharyngeal phase of swallowing. Objective: To identify clinical predictors indicating the presence of pharyngeal dysphagia in patients at risk. Methods: We examined pharyngeal dysphagia in a large cohort of patients with Parkinson’s disease (n = 200) divided in three clinical subtypes (tremor-dominant (TD), mainly bradykinetic (BK) and early postural instability and gait difficulty PIGD)) by using flexible endoscopic evaluation of swallowing. ANOVA-multivariance analysis and following t-tests as well as binary logistic regression analysis were performed to detect group differences and to identify clinical predictors for dysphagia. Results: Statistically significant differences were found in the dysphagic group: age, male gender, disease duration, stage of the disease, Levodopa equivalent dose and higher scores on the Unified Parkinson’s disease rating scale III and II, item 7. The PIGD subtype was affected more frequently than the TD and BK subtype. In a logistic regression model higher age (>63.5 years p < 0.05) and Levodopa equivalent dose (>475 mg, p < 0.01) were identified to be independent predictors for the presence of pharyngeal dysphagia. Conclusion: Particularly patients with an age > 63.5 years and a daily Levodopa equivalent dose >475 mg show an increased risk for pharyngeal dysphagia. These findings may partly be influenced by presbyphagia but are likely to represent disease progression. The PIGD subtype seems to be a risk factor due to more pronounced dyscoordination of oropharyngeal muscle movements.

scholarly journals A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

2022 ◽  
Vol 12 (1) ◽  
pp. 55
Author(s):  
Fatih Demir ◽  
Kamran Siddique ◽  
Mohammed Alswaitti ◽  
Kursat Demir ◽  
Abdulkadir Sengur
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Bayesian Optimization ◽  
L1 Norm ◽  
Bayesian Optimization Algorithm ◽  
Knn Classifier ◽  
Multi Level

Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

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