Stages Of Parkinson's Disease Research Articles

Artificial intelligence for identification of candidates for device-aided therapy in Parkinson's disease: DELIST-PD study

IntroductionIn Parkinson's Disease (PD), despite available treatments focusing on symptom alleviation, the effectiveness of conventional therapies decreases over time. This study aims to enhance the identification of candidates for device-aided therapies (DAT) using artificial intelligence (AI), addressing the need for improved treatment selection in advanced PD stages. MethodsThis national, multicenter, cross-sectional, observational study involved 1086 PD patients across Spain. Machine learning (ML) algorithms, including CatBoost, support vector machine (SVM), and logistic regression (LR), were evaluated for their ability to identify potential DAT candidates based on clinical and demographic data. ResultsThe CatBoost algorithm demonstrated superior performance in identifying DAT candidates, with an area under the curve (AUC) of 0.95, sensitivity of 0.91, and specificity of 0.88. It outperformed other ML models in balanced accuracy and negative predictive value. The model identified 23 key features as predictors for suitability for DAT, highlighting the importance of daily "off" time, doses of oral levodopa/day, and PD duration. Considering the 5-2-1 criteria, the algorithm identified a decision threshold for DAT candidates as > 4 times levodopa tablets taken daily and/or ≥1.8 h in daily “off” time. ConclusionThe study developed a highly discriminative CatBoost model for identifying PD patients candidates for DAT, potentially improving timely and accurate treatment selection. This AI approach offers a promising tool for neurologists, particularly those less experienced with DAT, to optimize referral to Movement Disorder Units.

Modifications of DJ-1 in which pI shifts to acidic in red blood cells a potential biomarker for Parkinson’s disease at early stages

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases, the incidence of which increases with age. However, since there is no fundamental treatment or methods for early diagnosis, new methods of treatment and diagnosis are urgently needed. We focused on post-translational modifications of DJ-1, which is encoded by the familial PD-causative gene PARK7 in red blood cells (RBCs). DJ-1 has three cysteines (Cys46, Cys53, and Cys106), with Cys106 being preferentially oxidized. We previously reported that sulfinated/sulfonated Cys106 DJ-1 (oxDJ-1) is increased in the RBCs of PD patients. In this study, we analyzed RBC-derived DJ-1 from PD patients and control subjects by 2-dimensional electrophoresis. We found that the ratio of the spot of DJ-1 with a more acidic isoelectric point than oxDJ-1 was increased more significantly than that of oxDJ-1 in RBCs from patients at the early stage of unmedicated PD and decreased with the progression of PD stage and treatment. Furthermore, we revealed that this acidic spot of DJ-1 increased upon exposure to H2O2. However, when either Cys53 or Cys106 of DJ-1 was replaced with serine, there was no significant increase in the acidic spot caused by H2O2. In this study, we propose a new biomarker for early diagnosis of PD using both the ratios of oxDJ-1 to total DJ-1 and the acidic spot of DJ-1 to total DJ-1.

Substantia nigra alterations in mice modeling Parkinson’s disease

Substantia nigra alterations in mice modeling Parkinson’s disease

Interpersonal Psychotherapy for the Treatment of Depression in Parkinson's Disease: Results of a Randomized Controlled Trial.

Depression is a common nonmotor complication in Parkinson's disease (PD). However, few studies have evaluated the efficacy of first-line psychological therapies for depression in this patient population. This randomized controlled trial evaluated the efficacy of interpersonal psychotherapy (IPT), an empirically validated intervention for depression that focuses on the bidirectional relationship between mood disturbance and interpersonal and social stressors. A secondary aim was to assess maintenance of treatment gains at 6-month follow-up. Participants with PD stages I to III and a comorbid depressive disorder were randomly assigned to 12 sessions of IPT (n = 32) or supportive therapy (ST) (n = 31), our active control intervention. The primary outcome was the Hamilton Depression Rating Scale (HAM-D) administered blindly by telephone. Secondary outcomes included self-report depression and anxiety, quality of life, clinician-rated motor symptom, interpersonal relationships, and attachment style. IPT compared to ST resulted in a greater reduction in posttreatment HAM-D scores (least square mean difference = -3.77, 95% confidence interval [CI]: -6.19 to -1.34, P = 0.003) and was associated with a greater odds of meeting remission (odds ratio = 3.23, 95% CI: 1.10-9.51, P = 0.034). The advantage of IPT over ST on HAM-D scores and remission rates was not sustained at the 6-month follow-up. Both treatments improved self-report depression, anxiety, quality of life, and aspects of interpersonal functioning. This trial demonstrates the benefits of acute treatment with IPT in reducing depressive symptoms in PD. Clinicians should consider psychotherapy, alone or in combination with medication, as an important treatment option for PD depression. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism.

The increasing prevalence of neurodegenerative diseases has recently heightened interest in research on early diagnosis of these diseases. Parkinson's disease (PD), among the most prominent of these conditions, is a neurological disorder causing the loss of nerve cells and significantly affecting movement control. Detection of PD in early stages is of critical importance to prevent the progression of the disease and improve treatment processes. The aim of the current study is to develop a deep learning model that can perform accurate classification for early diagnosis of PD from MRI images. In this study, a densely connected feature fusion network with residual learning is designed to diagnose PD patients. The designed network consists of a serial dense block with skip connections and efficient attention mechanisms. In this architecture, squeeze-excitation (SE) blocks with ResNeXt (SE-ResNeXt block) modules are utilized to extract distinctive and high-level features. In the experiments, a publicly available T2-weighted MRI dataset is used, and an offline augmentation process is applied to limited data to increase the generalization ability and classification performance. The proposed method is evaluated and compared with current state-of-the-art deep learning methods. The obtained results show that the proposed model gives higher classification performance with an overall accuracy of 94.44%, precision of 91.67%, sensitivity of 91.67%, specificity of 95.83%, F1-score of 91.67%, and Matthew's correlation coefficient of 87.50% for the PD and healthy control subjects.

Parkinson's Disease: Unravelling the Medicinal Perspectives and Recent Developments of Heterocyclic Monoamine Oxidase-B Inhibitors.

Parkinson's disease is a neurodegenerative condition characterized by slow movement (bradykinesia), tremors, and muscle stiffness. These symptoms occur due to the degeneration of dopamine- producing neurons in the substantia nigra region of the brain, leading to reduced dopamine levels. The development of Parkinson's Disease (PD) involves a combination of genetic and environmental factors. PD is associated with abnormal regulation of the monoamine oxidase (MAO) enzyme. Monoamine oxidase inhibitors (MAOIs) are an important class of drugs used to treat PD and other neurological disorders. In the early stages of PD, monotherapy with MAO-B inhibitors has been shown to be both safe and effective. These inhibitors are also commonly used as adjuncts in long-term disease management, as they can improve both motor and non-motor symptoms, reduce "OFF" periods, and potentially slow disease progression. However, current MAO-B inhibitors come with side effects like dizziness, nausea, vomiting, light-headedness, and fainting. Therefore, accelerating the development of new MAO-B inhibitors with fewer side effects is crucial. This review explores natural compounds that may inhibit monoamine oxidase B (MAO-B), focusing on key findings from the past seven years. It highlights the most effective heterocyclic compounds against MAO-B, including thiazolyl hydrazone, pyridoxine-resveratrol, pyridazine, isoxazole, oxadiazole, benzothiazole, benzoxazole, coumarin, caffeine, pyrazoline, piperazine, piperidine, pyrrolidine, and morpholine derivatives. The review covers in vitro, in silico, and in vivo data, along with the structure- activity relationship of these compounds. These findings offer valuable insights for the development of more effective MAO-B inhibitors and advancements in Parkinson's disease research.

The Prevalence of Depressive Symptoms in Patients with Idiopathic Parkinson's Disease: Cross-Sectional Study from Somalia.

Depression is one of the most common non-motor symptom of Parkinson's disease (PD), with an increasing prevalence in recent years. It causes significant psychological consequences that affect the disease course, overall quality of life, and functioning. The objective of this study was to determine the prevalence of depression in outpatients with Parkinson's disease evaluated in the neurology clinic at tertiary hospital in Mogadishu, Somalia. A cross-sectional study was conducted among 50 PD patients without dementia to determine the prevalence of depression at the neurology clinic of the Mogadishu Somalia Türkiye Training and Research Hospital between February and November 2022. All eligible participants were interviewed by a team of doctors using a structured questionnaire that consisted of sociodemographics and clinical characteristics, the Beck Depression Inventory-II (BDI-II) for depression assessment, and the Modified Hoehn and Yahr Scale for PD staging. Of the 50 PD participants, 60% were male and 58% were older than 60 years. 20% of them had a family history of PD and HTN as comorbid conditions. The prevalence of depression among the participants was 46% (95% CI 31.8-60.7). Of the patients with depression, 22% and 24% had mild and moderate depressive symptoms, respectively. The Mann-Whitney U-test revealed a statistically significant association between depression symptoms and the presence of comorbidity (χ2 = 136.50, p<0.01). The Kruskal-Wallis test revealed a statistically significant association between depression symptoms and a longer duration of PD (χ2 (2) = 18.21, p<0.01) and advanced stages of PD (χ2 (2) = 13.74, p<0.01). This is the first study conducted on patients with PD in Somalia and found that a significant proportion of these patients experienced depressive symptoms. We also highlighted that factors such the presence of medical comorbidities, high monthly income, advanced PD stage, longer duration of PD, and use of multiple medications for PD were significantly associated with the presence of depressive symptoms.

Proximity Elongation Assay and ELISA for the Identification of Serum Diagnostic Biomarkers in Parkinson's Disease and Progressive Supranuclear Palsy.

Clinical differentiation of progressive supranuclear palsy (PSP) from Parkinson's disease (PD) is challenging due to overlapping phenotypes and late onset of PSP specific symptoms, highlighting the need for easily assessable biomarkers. We used proximity elongation assay (PEA) to analyze 460 proteins in serum samples from 46 PD, 30 PSP patients, and 24 healthy controls. ANCOVA was used to identify the most promising proteins and machine learning (ML) XGBoost and random forest algorithms to assess their classification performance. Promising proteins were also quantified by ELISA. Moreover, correlations between serum biomarkers and biological and clinical features were investigated. We identified five proteins (TFF3, CPB1, OPG, CNTN1, TIMP4) showing different levels between PSP and PD, which achieved good performance (AUC: 0.892) when combined by ML. On the other hand, when the three most significant biomarkers (TFF3, CPB1 and OPG) were analyzed by ELISA, there was no difference between groups. Serum levels of TFF3 positively correlated with age in all subjects' groups, while for OPG and CPB1 such a correlation occurred in PSP patients only. Moreover, CPB1 positively correlated with disease severity in PD, while no correlations were observed in the PSP group. Overall, we identified CPB1 correlating with PD severity, which may support clinical staging of PD. In addition, our results showing discrepancy between PEA and ELISA technology suggest that caution should be used when translating proteomic findings into clinical practice.

The Prevalence and Clinical Significance of Skin Manifestations in Parkinson's Disease Patients.

Parkinson's disease (PD) is primarily characterized by motor symptoms, but non-motor symptoms, including skin manifestations, are increasingly recognized. These remain underexplored despite their potential impact on quality of life. This study aimed to evaluate the prevalence and clinical features of skin findings in PD patients, with a focus on identifying potential pathogenetic links between dermatological conditions and PD. A total of 215 PD patients were included. Comprehensive dermatological examinations were performed, and demographic and clinical data were collected. Statistical analysis was conducted using SPSS 23.0, with significance set at P < 0.05. Skin conditions were found in 92.1% of PD patients. Xerosis, seborrheic dermatitis, and hyperhidrosis were the most common findings. Pre-PD xerosis was associated with an earlier stage of PD (P = 0.001). Use of PD medications, such as levodopa/carbidopa/entacapone, was linked to a lower incidence of seborrheic dermatitis (P = 0.040). A significant correlation was also noted between rosacea and cherry angioma (P = 0.01). Dermatological conditions are prevalent in PD and may precede its motor symptoms. Skin assessments could aid early diagnosis and management of PD, highlighting the need for further research on their pathogenetic mechanisms.

Identification of nitric oxide-mediated necroptosis as the predominant death route in Parkinson’s disease

Parkinson's disease (PD) involves multiple forms of neuronal cell death, but the dominant pathway involved in disease progression remains unclear. This study employed RNA sequencing (RNA-seq) of brain tissue to explore gene expression patterns across different stages of PD. Using the Scaden deep learning algorithm, we predicted neurocyte subtypes and modelled dynamic interactions for five classic cell death pathways to identify the predominant routes of neuronal death during PD progression. Our cell type-specific analysis revealed an increasing shift towards necroptosis, which was strongly correlated with nitric oxide synthase (NOS) expression across most neuronal subtypes. In vitro experiments confirmed that nitric oxide (NO) is a key mediator of necroptosis, leading to nuclear shrinkage and decreased mitochondrial membrane potential via phosphorylation of the PIP1/PIP3/MLKL signalling cascade. Importantly, specific necroptosis inhibitors significantly mitigated neuronal damage in both in vitro and in vivo PD models. Further analysis revealed that NO-mediated necroptosis is prevalent in early-onset Alzheimer's disease (AD) and amyotrophic lateral sclerosis (ALS) and across multiple brain regions but not in brain tumours. Our findings suggest that NO-mediated necroptosis is a critical pathway in PD and other neurodegenerative disorders, providing potential targets for therapeutic intervention.

Multiomics approach discloses lipids and metabolites profiles associated to Parkinson's disease stages and applied therapies

Profiling circulating lipids and metabolites in Parkinson's disease (PD) patients could be useful not only to highlight new pathways affected in PD condition but also to identify sensitive and effective biomarkers for early disease detection and potentially effective therapeutic interventions. In this study we adopted an untargeted omics approach in three groups of patients (No L-Dopa, L-Dopa and DBS) to disclose whether long-term levodopa treatment with or without deep brain stimulation (DBS) could reflect a characteristic lipidomic and metabolomic signature at circulating level. Our findings disclosed a wide up regulation of the majority of differentially regulated lipid species that increase with disease progression and severity. We found a relevant modulation of triacylglycerols and acyl-carnitines, together with an altered profile in adiponectin and leptin, that can differentiate the DBS treated group from the others PD patients. We found a highly significant increase of exosyl ceramides (Hex2Cer) and sphingoid bases (SPB) in PD patients mainly in DBS group (p < 0.0001), which also resulted in a highly accurate diagnostic performance. At metabolomic level, we found a wide dysregulation of pathways involved in the biosynthesis and metabolism of several amino acids. The most interesting finding was the identification of a specific modulation of L-glutamic acid in the three groups of patients. L-glutamate levels increased slightly in No L-Dopa and highly in L-Dopa patients while decreased in DBS, suggesting that DBS therapy might have a beneficial effect on the glutamatergic cascade. All together, these data provide novel insights into the molecular and metabolic alterations underlying PD therapy and might be relevant for PD prediction, diagnosis and treatment.

Brain-Region-Specific Differences in Protein Citrullination/Deimination in a Pre-Motor Parkinson's Disease Rat Model.

The detection of early molecular mechanisms and potential biomarkers in Parkinson's disease (PD) remains a challenge. Recent research has pointed to novel roles for post-translational citrullination/deimination caused by peptidylarginine deiminases (PADs), a family of calcium-activated enzymes, in the early stages of the disease. The current study assessed brain-region-specific citrullinated protein targets and their associated protein-protein interaction networks alongside PAD isozymes in the 6-hydroxydopamine (6-OHDA) induced rat model of pre-motor PD. Six brain regions (cortex, hippocampus, striatum, midbrain, cerebellum and olfactory bulb) were compared between controls/shams and the pre-motor PD model. For all brain regions, there was a significant difference in citrullinated protein IDs between the PD model and the controls. Citrullinated protein hits were most abundant in cortex and hippocampus, followed by cerebellum, midbrain, olfactory bulb and striatum. Citrullinome-associated pathway enrichment analysis showed correspondingly considerable differences between the six brain regions; some were overlapping for controls and PD, some were identified for the PD model only, and some were identified in control brains only. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways identified in PD brains only were associated with neurological, metabolic, immune and hormonal functions and included the following: "Axon guidance"; "Spinocerebellar ataxia"; "Hippo signalling pathway"; "NOD-like receptor signalling pathway"; "Phosphatidylinositol signalling system"; "Rap1 signalling pathway"; "Platelet activation"; "Yersinia infection"; "Fc gamma R-mediated phagocytosis"; "Human cytomegalovirus infection"; "Inositol phosphate metabolism"; "Thyroid hormone signalling pathway"; "Progesterone-mediated oocyte maturation"; "Oocyte meiosis"; and "Choline metabolism in cancer". Some brain-region-specific differences were furthermore observed for the five PAD isozymes (PADs 1, 2, 3, 4 and 6), with most changes in PAD 2, 3 and 4 when comparing control and PD brain regions. Our findings indicate that PAD-mediated protein citrullination plays roles in metabolic, immune, cell signalling and neurodegenerative disease-related pathways across brain regions in early pre-motor stages of PD, highlighting PADs as targets for future therapeutic avenues.

Brain single-cell transcriptomics highlights comorbidity-related cell type-specific changes of Parkinson’s disease with major depressive disorder after paraquat exposure

Paraquat (PQ), a commonly used herbicide, is a potent environmental neurotoxin associated with Parkinson’s disease (PD) and major depressive disorder (MDD). While the involvement of various brain cell types in the etiology of each disorder is well recognized, the specific cell subtypes implicated in the comorbidity of PD and MDD, especially under PQ neurotoxicity, remain poorly understood. In this study, we used single-cell RNA sequencing (scRNA-seq) to analyze brain tissues from mice with PQ-induced PD with MDD. By integrating genomic data with scRNA-seq profiles, we identified differences in cellular heterogeneity related to the pathogenesis of PD and MDD under PQ exposure. Our analysis of risk enrichment in genes with cell type-specific expression patterns revealed that astrocytes are predominantly linked to the comorbidity of PQ-induced PD and MDD. Furthermore, we identified a specific astrocyte subtype that plays a major role in the comorbidity-related changes observed in PQ-induced PD and MDD. This subtype appears to interact with and potentially transform into MDD-specific and PD-specific subtypes. Additionally, pathways related to chemical synaptic function and neuro-projection development were involved in all key stages of PD and MDD co-occurrence. We also identified RNF7 and MTCH2 as shared diagnostic hub genes for PD and MDD, which changed significantly in astrocytes following PQ exposure. These genes may serve as potential markers for astrocyte-specific prognostic diagnosis of PQ-induced PD with MDD. In summary, this study provides the first scRNA-seq profile of comorbidity in a PQ-exposed model. It highlights the heterogeneity of astrocytes in comorbidity and elucidates potential mechanisms underlying the co-occurrence of PD and MDD. These findings emphasize the need for further research into the pathogenesis of PD comorbid with MDD and offer novel insights into PQ neurotoxicity.

Identification of key genes and diagnostic model associated with circadian rhythms and Parkinson's disease by bioinformatics analysis.

Circadian rhythm disruption is typical in Parkinson's disease (PD) early stage, and it plays an important role in the prognosis of the treatment effect in the advanced stage of PD. There is growing evidence that circadian rhythm genes can influence development of PD. Therefore, this study explored specific regulatory mechanism of circadian genes (C-genes) in PD through bioinformatic approaches. Differentially expressed genes (DEGs) between PD and control samples were identified from GSE22491 using differential expression analysis. The key model showing the highest correlation with PD was derived through WGCNA analysis. Then, DEGs, 1,288 C-genes and genes in key module were overlapped for yielding differentially expressed C-genes (DECGs), and they were analyzed for LASSO and SVM-RFE for yielding critical genes. Meanwhile, from GSE22491 and GSE100054, receiver operating characteristic (ROC) was implemented on critical genes to identify biomarkers, and Gene Set Enrichment Analysis (GSEA) was applied for the purpose of exploring pathways involved in biomarkers. Eventually, immune infiltrative analysis was applied for understanding effect of biomarkers on immune microenvironment, and therapeutic drugs which could affect biomarkers expressions were also predicted. Finally, we verified the expression of the genes by q-PCR. Totally 634 DEGs were yielded between PD and control samples, and MEgreen module had the highest correlation with PD, thus it was defined as key model. Four critical genes (AK3, RTN3, CYP4F2, and LEPR) were identified after performing LASSO and SVM-RFE on 18 DECGs. Through ROC analysis, AK3, RTN3, and LEPR were identified as biomarkers due to their excellent ability to distinguish PD from control samples. Besides, biomarkers were associated with Parkinson's disease and other functional pathways. Through bioinformatic analysis, the circadian rhythm related biomarkers were identified (AK3, RTN3 and LEPR) in PD, contributing to studies related to PD treatment.

Parkinson biomarker determination with an Au microflower-enhanced electrochemical immunosensor using non-Faradaic capacitancemeasurements.

This work comprehends the development and characterization of a carbon black-based electrode modified with Au microflowers to increase its effect as a capacitance biosensor for the determination of PARK7/DJ-1. Due to its high surface-to-volume ratio and biocompatibility, Au particles are suitable for antibody binding, and by monitoring surface capacitance, it is possible to identify the immune-pair interaction. Au microflowers allowed the adequate immobilization of Parkinsonian-related proteins: PARK7/DJ-1 and its antibody. The protein is associated with several antioxidant mechanisms, but its abnormal concentrations or mutations can be the cause of the loss of dopaminergic neurons, leading to Parkinson's disease. The device was characterized by scanning electron microscopy and cyclic voltammetry, revealing the flower-like structures and the electrochemically-interest enhancements they provide, such as increased heterogeneous electron transfer rate coefficient and electroactive area. The self-assembled monolayers of different molecules were optimized with the aid of 22 central composite experiments and a linear calibration curve was obtained between 0.700 and 120ngmL-1 of PARK7/DJ-1, with a limit of detection of 0.207ngmL-1. The data confirms that the addition of Au microflowers enhanced the electrochemical signal of the device, as well as allowed for the determination of an early stage Parkinson's disease biomarker with appreciable analytical performance.

Balancing the prescription of virtual reality balance training for Parkinson disease: a critically appraised topic

Clinical Scenario Individuals with Parkinson Disease (PD) often present with impaired balance, resulting in increased fall risk. Evidence suggests that balance can be improved with physical therapy balance training interventions applied through video game play, or ‘exergaming’. However, best practices regarding the prescription of exergaming for persons with PD are unknown. The goal of this appraisal was to identify the Nintendo Wii exergame (NWE) parameters that have been shown to improve dynamic balance in individuals with PD, specifically Hoehn and Yahr (HY) stages 1-3, and make recommendations for the prescription of NWE balance interventions for this population. Methods The literature was searched to identify randomized controlled trials (RCT) that utilized NWE as a balance intervention for individuals with PD within HY stages 1-3. The search was limited to articles published within the last 10 years that assessed dynamic balance outcomes via the Dynamic Gait Index (DGI) or Functional Gait Assessment (FGA). Participants had no other known comorbidities that would impact dynamic balance and were on a stable dopaminergic prescription. 3 RCTs were identified and included in this appraisal. Conclusion and Implications for Practice NWE may improve dynamic balance in individuals with PD, HY stages 1-3. Recommended parameters include a variety of NWE modes, performed for 45-50 min per session, at a frequency of 2-3 times a week for 6-8 weeks. Clinicians should utilize a skilled approach when prescribing NWE including ongoing assessment to determine progress and to maintain safety. Future research is needed to determine the necessary intensity of NWE to improve dynamic balance in individuals with PD.

Monocyte to high-density lipoprotein cholesterol ratio reflects the peripheral inflammatory state in parkinsonian disorders

BackgroundIn Parkinson’s disease (PD) and Parkinson plus syndrome (PPS), inflammation is recognized as a relevant or contributing factor in the advancement of the diseases. For this reason, numerous biomarkers signaling immune alteration in both the central and peripheral nervous systems have been evaluated in PD and PPS. Nonetheless, the comprehensive inflammatory indices derived from readily available standard blood tests in PD, PPS, and healthy controls (HC) were rarely evaluated especially in the early stage of the diseases. ObjectiveThe aim of this study is to explore the serum level of peripheral inflammatory markers among the patients and investigate whether these markers contribute to symptoms. MethodClinical data and blood test results from drug naïve, early-stage 139 PD and 87 PPS patients, along with 139 age- and sex-matched healthy controls (HC) to PD were enrolled, with exclusion criteria applied to conditions potentially affecting inflammation. The study examined the disparities in peripheral inflammation among the groups, using total and subpopulation of white blood cells (WBCs), platelet count, red cell distribution width (RDW), high-density lipoprotein cholesterol (HDL-C), and other composite values reflecting inflammation including RDW to platelet ratio (RPR), neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), platelet to lymphocyte ratio (PLR), neutrophil to HDL-C ratio (NHR), monocyte to HDL-C ratio (MHR), lymphocyte to HDL-C ratio (LHR), platelet to HDL-C ratio (PHR), systemic inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI). ResultThe MHR values were significantly higher in both PD and PPS groups compared to HC (p < 0.001), and NHR was significantly higher in the PPS group only compared to the HC group (p < 0.001). However, no significant differences in all the inflammatory markers were observed between PPS and PD (p > 0.05). Subgroup analysis of progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) patients revealed significantly higher NHR and MHR levels compared to the HC group (p = 0.025, p = 0.050, respectively), with no significant difference among PSP, MSA, and PD groups. After adjustment for age, sex, and disease duration, MHR was positively associated with H&Y in the total population (β = 0.288, p < 0.001), negatively associated with MMSE in the PD group (β = −0.245, p = 0.017), and positively associated with both H&Y (β = 0.432, p < 0.001) and UPDRS part II (β = 0.295, p = 0.018) in PPS group. ConclusionNHR and MHR values are not effective as reliable diagnostic markers due to overlap among groups and their limited discriminative capacity in ROC analyses. However, MHR may potentially serve as an indicator reflecting peripheral inflammation in the early stage of PD and PPS compared to HC.

Imaging Biomarkers in Prodromal and Earliest Phases of Parkinson's Disease.

Assessing imaging biomarker in the prodromal and early phases of Parkinson's disease (PD) is of great importance to ensure an early and safe diagnosis. In the last decades, imaging modalities advanced and are now able to assess many different aspects of neurodegeneration in PD. MRI sequences can measure iron content or neuromelanin. Apart from SPECT imaging with Ioflupane, more specific PET tracers to assess degeneration of the dopaminergic system are available. Furthermore, metabolic PET patterns can be used to anticipate a phenoconversion from prodromal PD to manifest PD. In this regard, it is worth mentioning that PET imaging of inflammation will gain significance. Molecular imaging of neurotransmitters like serotonin, noradrenaline and acetylcholine shed more light on non-motor symptoms. Outside of the brain, molecular imaging of the heart and gut is used to measure PD-related degeneration of the autonomous nervous system. Moreover, optical coherence tomography can noninvasively detect degeneration of retinal fibers as a potential biomarker in PD. In this review, we describe these state-of-the-art imaging modalities in early and prodromal PD and point out in how far these techniques can and will be used in the future to pave the way towards a biomarker-based staging of PD.

Designing the First Trials for Parkinson's Prevention.

For decades the greatest goal of Parkinson's disease (PD) research has often been distilled to the discovery of treatments that prevent the disease or its progression. However, until recently only the latter has been realistically pursued through randomized clinical trials of candidate disease-modifying therapy (DMT) conducted on individuals after they received traditional clinical diagnosis of PD (i.e., tertiary prevention trials). Now, in light of major advances in our understanding of the prodromal stages of PD, as well as its genetics and biomarkers, the first secondary prevention trials for PD are beginning. In this review, we take stock of DMT trials to date, summarize the breakthroughs that allow the identification of cohorts at high risk of developing a traditional diagnosis of PD, and describe key design elements of secondary prevention trials and how they depend on the prodromal stage being targeted. These elements address whom to enroll, what interventions to test, and how to measure secondary prevention (i.e., slowed progression during the prodromal stages of PD). Although these design strategies, along with the biological definition, subtype classification, and staging of the disease are evolving, all are driven by continued progress in the underlying science and integrated by a broad motivated community of stakeholders. While considerable methodological challenges remain, opportunities to move clinical trials of DMT to earlier points in the disease process than ever before have begun to unfold, and the prospects for PD prevention are nowtangible.

TMS-evoked potentials unveil occipital network involvement in patients diagnosed with Parkinson’s disease within 5 years of inclusion

Distinguishing Parkinson’s disease (PD) subgroups may be achieved by observing network responses to external stimuli. We compared TMS-evoked potential (TEP) measures from stimulation of bilateral motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and visual cortex (V1) between 62 PD patients (age: 69.9 ± 7.5) and 76 healthy controls (age: 69.2 ± 4.3) using a TMS–EEG protocol. TEP measures were analyzed using two-way ANCOVA adjusted for MOCA. PD patients were divided into tremor dominant (TD), non-tremor dominant (NTD) and rapid disease progression (RDP) subgroups. PD patients showed lower wide-waveform adherence (wWFA) (p = 0.025) and interhemispheric connectivity (IHCCONN) (p < 0.001) compared to healthy controls. Lower occipital IHCCONN correlated with advanced disease stage (r = −0.37, p = 0.0039). The RDP and NTD groups showed lower wWFA in response to occipital stimulation than the TD group (p = 0.005). Occipital TEP measures identified RDP patients with 85% accuracy. These findings demonstrate occipital network involvement in early PD stages, suggesting that TEP measures offer insights into altered networks in PD subgroups.