Stages Of Parkinson's Disease Research Articles

High pH stability and detection of α-synuclein using an EGFET biosensor with an HfO2 gate deposited by high-power pulsed magnetron sputtering

In this research, hafnium oxide (HfO2) was first fabricated in an in-house-developed high-power pulsed magnetron sputtering system (HiPIMS) functionalized for application as a high-performance extended gate field-effect transistor (EGFET) for the detection of pH and proteins associated with Parkinson's disease (PD). PD is the second most common neurodegenerative disease in elderly people after Alzheimer's disease. The α-synuclein protein in patients’ blood may be a potential biomarker in the early stage of PD. Detection of α-synuclein with high sensitivity and nonspecific binding through HfO2-EGFET biosensors has the advantages of low cost and simple fabrication from disposable EGs. Among the duty cycle conditions tested, HfO2 deposited at a 25% duty cycle in HiPIMS exhibited superior pH sensitivity and linearity, with values of 56.4 mV/pH and 99.7%, respectively. The hysteresis width and drift coefficient are −3 mV and 0.3 mV/h, respectively. To overcome the Debye length limitation, 100-fold diluted phosphate buffer solution and the PMOS configuration were used to determine the α-synuclein for which the isoelectric point was 4.67. The negative gate bias in the PMOS configuration successfully causes α-synuclein to approach the surface of HfO2 EG to achieve a sensitivity of 12.1 mV/dec and a linearity of 99.5% in the range of 0.1–1000 pg/mL. The limit of detection is 0.198 pg/mL, and the immobilized surface ultimately binds specifically, resulting in low interference signals from other related biomarkers. This promising study shows that detecting various proteins involved in different diseases and performing clinical tests can be accomplished in the future.

Safinamide as adjunctive therapy to levodopa monotherapy for patients with Parkinson's disease with wearing-off: The Japanese observational J-SILVER study

BackgroundSafinamide is an effective adjunctive therapy for wearing-off in Parkinson's disease (PD); however, evidence is lacking in older patients and those in the early stages of wearing-off. This study evaluated the efficacy and safety of safinamide as adjunctive therapy in patients with PD treated with levodopa monotherapy in clinical practice. MethodsThis multicentre, open-label observational study was conducted at five sites in Japan. Patients diagnosed with PD and wearing-off initiated safinamide as adjunctive therapy with levodopa monotherapy. Efficacy endpoints were mean changes in Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I, III, and IV scores; daily ON-time without dyskinesia using 24-h patient symptom diaries; and 39-item Parkinson's Disease Questionnaire (PDQ-39) scores at 18 weeks of treatment. ResultsIn total, 24 patients initiated safinamide (66.7% were aged ≥75 years); the mean duration of wearing-off was 1.2 years. MDS-UPDRS Part III total score, Part IV total score, and PDQ-39 summary index decreased significantly from baseline (mean change −7.0 [p = 0.012], −2.4 [p = 0.007] and − 5.3 [p = 0.012], respectively). There was a non-statistically significant increase of 1.55 h in mean daily ON-time without dyskinesia. Numerical Rating Scale total score for pain (p = 0.015), and scores for OFF-period pain (p = 0.012) and nocturnal pain (p = 0.021) subdomains were significantly improved in the subgroup with pain. Most reported adverse events were classified as mild. ConclusionSafinamide improved motor and non-motor symptoms and quality of life-related measures in older patients with PD in the early stages of wearing-off without new safety concerns. Study registrationUniversity Hospital Medical Information Network in Japan; study ID: UMIN000044341.

Relationship of Vitamin D Deficiency with Non-Motor Functions of Parkinson’s disease

Background: Parkinson’s disease (PD), a neurodegenerative disorder is associated with substantial public health and economic burdens. Low levels of vitamin D are a common finding in patients of PD. This study was conducted to assess the serum vitamin D level in PD patients and to investigate the relationship of the vitamin D level with non motor functions of Parkinson’s disease. Methods: This case-control study was conducted in Department of Neurology, BIRDEM General Hospital, Dhaka for a period of 12 months. A total of 120 participants, 60 patients with PD and 60 age and sex matched sample of healthy controls were enrolled and informed written consent was taken. Information on socio-demographic characteristics, clinical features, laboratory parameters and neurological examinations were assessed. A semi-structured questionnaire was used for data collection and data was analyzed with Statistical Package for Social Science (SPSS) version 24.0. Results: Age and gender was not significantly different between PD cases and controls. Mean vitamin D level was significantly lower among the patients with PD compared to controls (25.97±3.44 ng/ml vs38.47±5.08 ng/ml, (p<0.01). Among all the cases of PD, 45% had akathisia, 36.7% had depression and 40% had insomnia. According to Modified Hoehn and Yahr score 53.3% patients were in stage 1-2, 26.7% in stage 2-3 and 20% in stage >3.The mean vitamin-D level was 28.43±2.43, 24.06±1.57 and 21.92±1.62 in stage 1-2, stage 2.5-3 and stage>3 respectively and vitamin D level decreased significantly with advancement in stages of PD(p<0.01). While assessing the depression in patients with PD, it was observed that stages of PD was significantly associated with severity of depression, patients with >3 stage of PD was moderately severe (16.7%) and severely depressed (16.7%) than patients with <3 stage PD (p=0.009). Conclusion: Importance should also be given for recognition of non- motor symptoms in PD patients, since these symptoms have an impact on patient’s quality of life. Bangladesh J Medicine 2024; 35: 82-87

Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset.

Parkinson's disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5. We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets. Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75. The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.

Risk of developing Parkinson's disease and early stage diagnosis through the primary healthcare system

Risk of developing Parkinson's disease and early stage diagnosis through the primary healthcare system

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

CHRONOTYPE AND DAILY FUNCTIONING OF PATIENTS WITH DIFFERENT MOTOR SUBTYPES OF PARKINSON DISEASE

Introduction. Sleep and circadian rhythm disturbances can occur at any stage of Parkinson disease (PD) and significantly affect quality of life. Chronotypes of patients with PD are associated with different phenotypes, in particular with the motor subtype. Thus, we hypothesized that patients with different motor subtypes of PD may have differences in the distribution of chronotypes and patterns of daily activity. Methods and materials. We conducted clinical research on the basis of the Centre for Parkinson Disease and Neurodegenerative Diseases of the Department of Neurological Diseases of Poltava State Medical University. PD was verified according to the recommendations of the International Movement Disorders and Parkinson's Disease Society. The motor subtype of PD was determined by the Stebbin method, which is based on the calculation of the Stebbins coefficient by the sum of the Unified PD Rating Scale scores. The examined patients were divided into 3 groups according to the motor subtype of PD: group 1 (n = 38) - patients with PD subtype with the predominance of postural instability and gait disorders (PIGD); group 2 (n = 26) - patients with PD subtype with the predominance of tremor and mixed subtype; control group (n = 30) - conditionally healthy individuals without CNS lesions. Circadian patterns were analyzed using the Munich Chronotype Questionnaire (MCTQ). Results. It was found that in PD patients sleep onset and time of getting out of bed was later (p<0.001 and p=0.042, respectively), sleep latency was longer (p<0.001), sleep duration was shorter (p=0.001), the mid-sleep corresponded to a later time (p<0.001). Patients with the PIGD subtype had a later time of getting out of bed (p=0.038), longer sleep inertia (p<0.001), shorter sleep duration (p<0.001), and later mid-sleep time (p=0.028). We have shown the tendency of patients with PD, mostly in the PIGD subtype, to later chronotypes (p<0.001). Light exposure indirectly moderately correlated with mid-sleep in all study groups. It was found that both the motor subtype (p<0.001) and the level of light exposure during the day (p<0.001) statistically significantly affect the mid-sleep. Conclusion. Thus, we have found that patients with PD differ from age-matched controls without neurodegenerative diseases by chronotype and circadian pattern of functioning. The motor subtype of the disease is associated with circadian differences, namely, the PIGD subtype is associated with shorter sleep duration, a predisposition to a later chronotype, and longer sleep inertia.

Evaluation of the Effect of Age-Related Macular Degeneration Type And Stage on the Risk of Parkinson’s Disease

The aim of the study was to investigate the distance between Parkinson's Disease (PD) and Age-Related Macular Degeneration (AMD) type and stage.. Our prospective study, the dry-type AMD group consisted of 296 patients with early and 284 patients with late-stage. The neovascular AMD group included 285 early and 277 late-stage patients. The control group consisted of 300 patients. AMD patients were grouped as dry and neovascular type and early and late stage. The patients were questioned about the use of drugs for PD, and the use was recorded as having the disease. If any of the complaints seen in the PD were present, the patient was referred to a neurologist. PD was detected in 1% of the control group and 4.6% in the neovascular type AMD group, and this difference was significant (p:0.04). This difference was present in both the early (%4.5) and late-stage (%4.6 ) (p:0.04, p:0.04). PD was determined 3.78 times greater among neovascular AMD patients(p:0.03), and significant association was present in both early (3.72 times) and late-stage (3.82 times) (p:0.03, p:0.03). In the dry-type AMD group 2.7% PD was detected and there was no statistical difference (p>0.05). This difference was not significant in the early stage (%2.3) or late-stage (%2.8) and also there was no association with dry-type AMD (p>0.05). Also, unilateral and bilateral involvement in AMD was not associated with PD (p>0.05). Our study revealed the association between both early and late neovascular AMD and PD. However, any significant relationship was not detected in terms of both unilateral and bilateral involvement.

Novel Variants Linked to the Prodromal Stage of Parkinson's Disease (PD) Patients.

The symptoms of most neurodegenerative diseases, including Parkinson's disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD. Data were obtained from the Parkinson's Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis. Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD. Genetic biomarkers are crucial for the early detection of Parkinson's disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits.

Advanced Recognizing and Categorizing System for Alzheimer's and Parkinson's Syndromes using CNN Algorithm and Clinical Data

Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) are advancing neurodegenerative diseases that impact a large number of individuals globally. This study provides a novel method to identify and categorize individuals in the early stages of PD and AD by analyzing online handwriting structures, leveraging the current progress in Machine Learning (ML) in the medical domain. The research explores multiple data augmentation strategies to address the limited training information available for classifying neuro-degenerative disorders based on behavioral information. The study used a Convolutional Neural Network (CNN) classification as a foundation model by identifying essential radiomics characteristics from the T1-weighted Magnetic Resonance Imaging (MRI) data. The research explores data augmentation strategies designed explicitly for time-series information, such as online handwriting patterns, instead of standard methods used for hardware-based detection. This approach, with more validation, might assist in distinguishing between difficult instances of AD and PD that have a comparable mild motor and non-motor symptoms.

Diagnosing Parkinson’s disease and monitoring its progression: Biomarkers from combined GC-TOF MS and LC-MS/MS untargeted metabolomics

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with a poorly understood etiology. An accurate diagnosis of idiopathic PD remains challenging as misdiagnosis is common in routine clinical practice. Moreover, current therapeutics focus on symptomatic management rather than curing or slowing down disease progression. Therefore, identification of potential PD biomarkers and providing a better understanding of the underlying disease pathophysiology are urgent. Herein, hydrophilic interaction liquid chromatography–mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-TOF MS) based metabolomics approaches were used to profile the serum metabolome of 50 patients with different stages of idiopathic PD (early, mid and advanced) and 45 age-matched controls. Levels of 57 metabolites including cysteine-S-sulfate and N-acetyl tryptophan were significantly higher in patients with PD compared to controls, with lower amounts of additional 51 metabolites including vanillic acid, and N-acetylaspartic acid. Xanthines, including caffeine and its downstream metabolites, were lowered in patients with PD relative to controls indicating a potential role caffeine and its metabolites against neuronal damage. Seven metabolites, namely cysteine-S-sulfate, 1-methylxanthine, vanillic acid, N-acetylaspartic acid, 3-N-acetyl tryptophan, 5-methoxytryptophol, and 13-HODE yielded a ROC curve with a high classification accuracy (AUC 0.977). Comparison between different PD stages showed that cysteine-S-sulfate levels were significantly increasing with the advancement of PD stages while LPI 20:4 was significantly decreasing with disease progression. Our findings provide new biomarker candidates to assist in the diagnosis of PD and monitor its progression. Unusual metabolites like cysteine-S-sulfate might point to therapeutic targets that could enhance the development of novel PD treatments, such as NMDA antagonists.

LncRNA NEAT1 promotes MPP+ induced injury of PC12 cells and accelerates the progression of Parkinson's disease in mice through FUS mediated inhibition of PI3K/AKT/mTOR signalling pathway

Long noncoding RNA nuclear-enriched abundant transcript 1 (NEAT1) is involved in the progression of Parkinson's disease (PD), but the specific regulatory role needs further exploration. This study showed that the expression of NEAT1 was upregulated in the cerebrospinal fluid (CSF) and peripheral blood of patients with different stages of PD. 1-Methyl-4-phenylpyridine (MPP)-treated PC 12 cells were transfected with si-NEAT1, and MPP treatment promoted cell apoptosis, oxidative stress and inflammatory factor secretion. Si-NEAT1 reversed the effects of MPP. NEAT1 silencing eliminated the effect of MPP on the protein expression levels of LC3-II and p62/SQSTM1. By using an online bioinformatics database, Fused in Sarcoma (FUS) was confirmed to be an RNA binding protein of NEAT1, and it was highly expressed in the CSF and peripheral blood of patients with PD. Si-FUS was transfected into MPP-treated PC 12 cells to detect cell apoptosis, oxidative stress, inflammatory factor secretion and autophagy, and the results were the same as those of transfection of si-NEAT1. Furthermore, MPP treatment reduced the phosphorylation levels of PI3K, Akt and mTOR, whereas si-FUS reversed the effects of MPP. In vivo, compared with the model group, the PD mice showed reduced NEAT1 and FUS expression levels and activated PI3K pathway after being injected with si-NEAT1. The brain tissue of NEAT1-silenced PD mice had decreased inflammatory infiltration and apoptosis and increased neurological scores. In conclusion, NEAT1 is involved in PD progression through FUS-mediated inhibition of the PI3K/AKT/mTOR signalling pathway.

Transferrin-conjugated magnetic nanoparticles for the isolation of brain-derived blood exosomal MicroRNAs: A novel approach for Parkinson's disease diagnosis

BackgroundBrain-derived exosomes circulate in the bloodstream and other bodily fluids, serving as potential indicators of neurological disease progression. These exosomes present a promising avenue for the early and precise diagnosis of neurodegenerative conditions. Notably, miRNAs found in plasma extracellular vesicles (EVs) offer distinct diagnostic benefits due to their stability, abundance, and resistance to breakdown. ResultsIn this study, we introduce a method using transferrin conjugated magnetic nanoparticles (TMNs) to isolate these exosomes from the plasma of patients with neurological disorders. This TMNs technique is both quick (<35 min) and cost-effective, requiring no high-priced ingredients or elaborate equipment for EV extraction. Our method successfully isolated EVs from 33 human plasma samples, including those from patients with Parkinson's disease (PD), Multiple Sclerosis (MS), and Dementia. Using quantitative polymerase chain reaction (PCR) analysis, we evaluated the potential of 8 exosomal miRNA profiles as biomarker candidates. Six exosomal miRNA biomarkers (miR-195-5p, miR-495-3p, miR-23b-3P, miR-30c-2-3p, miR-323a-3p, and miR-27a-3p) were consistently linked with all stages of PD. SignificanceThe TMNs method provides a practical, cost-efficient way to isolate EVs from biological samples, paving the way for non-invasive neurological diagnoses. Furthermore, the identified miRNA biomarkers in these exosomes may emerge as innovative tools for precise diagnosis in neurological disorders including PD.

The role of the brain renin-angiotensin system in Parkinson´s disease.

The renin-angiotensin system (RAS) was classically considered a circulating hormonal system that regulates blood pressure. However, different tissues and organs, including the brain, have a local paracrine RAS. Mutual regulation between the dopaminergic system and RAS has been observed in several tissues. Dysregulation of these interactions leads to renal and cardiovascular diseases, as well as progression of dopaminergic neuron degeneration in a major brain center of dopamine/angiotensin interaction such as the nigrostriatal system. A decrease in the dopaminergic function induces upregulation of the angiotensin type-1 (AT1) receptor activity, leading to recovery of dopamine levels. However, AT1 receptor overactivity in dopaminergic neurons and microglial cells upregulates the cellular NADPH-oxidase-superoxide axis and Ca2+ release, which mediate several key events in oxidative stress, neuroinflammation, and α-synuclein aggregation, involved in Parkinson's disease (PD) pathogenesis. An intraneuronal antioxidative/anti-inflammatory RAS counteracts the effects of the pro-oxidative AT1 receptor overactivity. Consistent with this, an imbalance in RAS activity towards the pro-oxidative/pro-inflammatory AT1 receptor axis has been observed in the substantia nigra and striatum of several animal models of high vulnerability to dopaminergic degeneration. Interestingly, autoantibodies against angiotensin-converting enzyme 2 and AT1 receptors are increased in PD models and PD patients and contribute to blood-brain barrier (BBB) dysregulation and nigrostriatal pro-inflammatory RAS upregulation. Therapeutic strategies addressed to the modulation of brain RAS, by AT1 receptor blockers (ARBs) and/or activation of the antioxidative axis (AT2, Mas receptors), may be neuroprotective for individuals with a high risk of developing PD or in prodromal stages of PD to reduce progression of the disease.

Amantadine use in the French prospective NS-Park cohort.

To assess amantadine use and associated factors in the patients with Parkinson's disease (PD). Immediate-release amantadine is approved for the treatment of PD and is largely used in clinical practice to treat "levodopa-induced dyskinesia (LIDs). Its use varies according to countries and PD stages. The prospective NS-Park cohort collects features of PD patients followed by 26 French PD Expert Centres. Variables used for the analyses included demographics, motor and non-motor PD symptoms and motor complications [motor fluctuations (MFs), LIDs)], antiparkinsonian pharmacological classes and levodopa equivalent daily dose (LEDD). We evaluated: (i) prevalence of amantadine use and compared clinical features of amantadine users vs. non-users (cross-sectional analysis); (ii) factors associated with amantadine initiation (longitudinal analysis); (iii) amantadine effect on LIDs, MFs, apathy, impulse control disorders and freezing of gait (Fog) (longitudinal analysis). Amantadine use prevalence was 12.6% (1,585/12,542, median dose = 200mg). Amantadine users were significantly younger, with longer and more severe PD symptoms, greater LEDD and more frequent use of device-aided/surgical treatment. Factors independently associated with amantadine initiation were younger age, longer PD duration, more frequent LIDs, MFs and FoG, higher LEDD and better cognitive function. 9 of the 658 patients on amantadine had stopped it at the following visit, after 12-18months (1.3%). New users of amantadine presented a higher improvement in LIDs and MF compared to amantadine never users. About 12% of PD patients within the French NS-Park cohort used amantadine, mostly those with younger age and more severe PD. Amantadine initiation was associated with a subsequent reduction in LIDs and MFs.

Artificial Intelligence-Guided Gut-Microenvironment-Triggered Imaging Sensor Reveals Potential Indicators of Parkinson's Disease.

The gut-brain axis has recently emerged as a crucial link in the development and progression of Parkinson's disease (PD). Dysregulation of the gut microbiota has been implicated in the pathogenesis of this disease, sparking growing interest in the quest for non-invasive biomarkers derived from the gut for early PD diagnosis. Herein, an artificial intelligence-guided gut-microenvironment-triggered imaging sensor (Eu-MOF@Au-Aptmer) to achieve non-invasive, accurate screening for various stages of PD is presented. The sensor works by analyzing α-Syn in the gut using deep learning algorithms. By monitoring changes in α-Syn, the sensor can predict the onset of PD with high accuracy. This work has the potential to revolutionize the diagnosis and treatment of PD by allowing for early intervention and personalized treatment plans. Moreover, it exemplifies the promising prospects of integrating artificial intelligence (AI) and advanced sensors in the monitoring and prediction of a broad spectrum of diseases and health conditions.

Nonlinear Relationship Between Homocysteine and Mild Cognitive Impairment in Early Parkinson's Disease: A Cross-Sectional Study.

Cognitive impairment, a prevalent non-motor symptom in advanced Parkinson's disease (PD), has been associated with hyperhomocysteinemia, an important risk factor for PD progression and cognitive decline in PD. However, evidence regarding the association between homocysteine (Hcy) and cognitive function during early PD remains insufficient. Therefore, this study aims to examine the correlation between Hcy levels and cognitive function in the early stage of PD. The study included 218 individuals in the early stages of PD who were consecutively admitted to the Suining Central Hospital Neurology Department. All the individuals completed the Parkinson's Disease Cognitive Rating Scale (PD-CDR). The Unified Parkinson's Disease Rating Scale part III (UPDRS-III) was employed for measuring the severity of motor symptoms, while the Hoehn-Yahr scale was used to measure the clinical symptom stage. Fasting venous blood samples were also drawn to measure the Hcy concentration, red blood cell folate, and vitamin B12. In this cross-sectional study, 47 (21.5%) patients with PD showed cognitive dysfunction. The serum Hcy levels were significantly higher in the cognitive impairment PD (PDCI) group compared with the cognitive normal PD group (P<0.001). The Generalized Additive Model (GAM) analysis revealed a nonlinear relationship between Hcy and the risk of PDCI. Multiple logistic regression analyses demonstrated a positive relationship between elevated Hcy and the risk of PDCI in the fully adjusted model ([OR]:3.1, 95% CI, 1.1-8.5, P=0.028). Segmented linear regression analysis showed that when Hcy levels were above 17.7 umol/l, the risk of PDCI increased by 1.6 times for every 1 unit elevated in Hcy (95% CI:1.1-2.2, P=0.008). This study revealed a nonlinear positive correlation between the risk of PDCI and elevated serum Hcy levels in early PD patients, suggesting hyperhomocysteinemia as one of the treatable factors for cognitive impairment in the early stages of PD.

White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.

Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

Responsiveness of respiratory function in Parkinson's Disease to an integrative exercise programme: A prospective cohort study.

Respiratory disorders are the most common cause of death in Parkinson's Disease (PD). Conflicting data exist on the aetiology of respiratory dysfunction in PD and few studies examine the effects of exercise-based interventions on respiratory measures. This study was conducted to better understand respiratory dysfunction in PD and to identify measures of dysfunction responsive to an integrative exercise programme. The objectives were to compare baseline respiratory measures with matched, published population norms and to examine immediate and longer-term effects of a 12-week integrated exercise programme on these measures. Twenty-three people with mild PD (median Hoehn & Yahr = 2) self-selected to participate in this exploratory prospective cohort study. Evaluation of participants occurred at three time points: at baseline; following the 12-week exercise programme and at 4-month follow-up. Outcome measures included: Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), FEV1/FVC ratio, Peak Expiratory Flow (PEF), Inspiratory Muscle Strength (MIP), Expiratory Muscle Strength (MEP), Peak Cough Flow (PCF), and Cardiovascular Fitness measures of estimated VO2 max and 6-Minute Walk Test (6MWT). Compared to published norms, participants had impaired cough, reduced respiratory muscle strength, FEV, FVC, PEF and cardiovascular fitness. Post exercise intervention, statistically significant improvements were noted in MEP, cardiovascular fitness, and PEF. However only gains in PEF were maintained at 4-month follow-up. Significant respiratory dysfunction exists, even in the early stages of PD. Metrics of respiratory muscle strength, peak expiratory flow and cardiovascular fitness appear responsive to an integrative exercise programme.

Safety and efficacy of faecal microbiota transplantation in patients with mild to moderate Parkinson's disease (GUT-PARFECT): a double-blind, placebo-controlled, randomised, phase 2 trial

Dysregulation of the gut microbiome has been implicated in Parkinson's disease (PD). This study aimed to evaluate the clinical effects and safety of a single faecal microbiota transplantation (FMT) in patients with early-stage PD. The GUT-PARFECT trial, a single-centre randomised, double-blind, placebo-controlled trial was conducted at Ghent University Hospital between December 01, 2020 and December 12, 2022. Participants (aged 50-65 years, Hoehn and Yahr stage 2) were randomly assigned to receive nasojejunal FMT with either healthy donor stool or their own stool. Computer-generated randomisation was done in a 1:1 ratio through permutated-block scheduling. Treatment allocation was concealed for participants and investigators. The primary outcome measure at 12 months was the change in the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor score obtained during off-medication evaluations. Intention-to-treat analysis was performed using a mixed model for repeated measures analysis. This completed trial is registered on ClinicalTrials.gov (NCT03808389). Between December 2020 and December 2021, FMT procedures were conducted on 46 patients with PD: 22 in the healthy donor group and 24 in the placebo group. Clinical evaluations were performed at baseline, 3, 6, and 12 months post-FMT. Full data analysis was possible for 21 participants in the healthy donor group and 22 in the placebo group. After 12 months, the MDS-UPDRS motor score significantly improved by a mean of 5.8 points (95% CI-11.4 to-0.2) in the healthy donor group and by 2.7 points (-8.3 to 2.9) in the placebo group (p=0.0235). Adverse events were limited to temporary abdominal discomfort. Our findings suggested a single FMT induced mild, but long-lasting beneficial effects on motor symptoms in patients with early-stage PD. These findings highlight the potential of modulating the gut microbiome as a therapeutic approach and warrant a further exploration of FMT in larger cohorts of patients with PD in various disease stages. Flemish PD patient organizations (VPL and Parkili), Research Foundation Flanders (FWO), Biocodex Microbiota Foundation.