Parkinson's Disease Progression Research Articles

Targeting Glucose Metabolism: A Novel Therapeutic Approach for Parkinson’s Disease

Glucose metabolism is essential for the maintenance and function of the central nervous system. Although the brain constitutes only 2% of the body weight, it consumes approximately 20% of the body’s total energy, predominantly derived from glucose. This high energy demand of the brain underscores its reliance on glucose to fuel various functions, including neuronal activity, synaptic transmission, and the maintenance of ion gradients necessary for nerve impulse transmission. Increasing evidence shows that many neurodegenerative diseases, including Parkinson’s disease (PD), are associated with abnormalities in glucose metabolism. PD is characterized by the progressive loss of dopaminergic neurons in the substantia nigra, accompanied by the accumulation of α-synuclein protein aggregates. These pathological features are exacerbated by mitochondrial dysfunction, oxidative stress, and neuroinflammation, all of which are influenced by glucose metabolism disruptions. Emerging evidence suggests that targeting glucose metabolism could offer therapeutic benefits for PD. Several antidiabetic drugs have shown promise in animal models and clinical trials for mitigating the symptoms and progression of PD. This review explores the current understanding of the association between PD and glucose metabolism, emphasizing the potential of antidiabetic medications as a novel therapeutic approach. By improving glucose uptake and utilization, enhancing mitochondrial function, and reducing neuroinflammation, these drugs could address key pathophysiological mechanisms in PD, offering hope for more effective management of this debilitating disease.

Angiotensin type-1 receptor autoantibodies promote alpha-synuclein aggregation in dopaminergic neurons

Angiotensin, through its type-1 receptor (AT1), is a major inducer of inflammation and oxidative stress, contributing to several diseases. Autoimmune processes have been involved in neurodegeneration, including Parkinson’s disease (PD). AT1 autoantibodies (AT1-AA) enhance neurodegeneration and PD, which was related to increased neuronal oxidative stress and neuroinflammation. However, the effect of AT1-AA on α-synuclein aggregation, a major factor in PD progression, has not been studied. In cultures of dopaminergic neurons, we observed that AT1-AA promote aggregation of α-synuclein, as AT1-AA upregulated major mechanisms involved in the α-synuclein aggregation process such as NADPH-oxidase activation and intracellular calcium raising. The results further support the role of AT1 receptors in dopaminergic neuron degeneration, and several recent clinical studies observing the neuroprotective effects of AT1 receptor blockers.

Deciphering Parkinson's Disease Progression Using Deep Learning Techniques

Deciphering Parkinson's Disease Progression Using Deep Learning Techniques

M6A-related genes and their role in Parkinson’s disease: Insights from machine learning and consensus clustering

Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on various biological processes. Nevertheless, the specific contribution of m6A-related genes to the development and progression of PD remains uncertain. In this study, we performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected important m6A-related genes. Candidate m6A-related genes were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the candidate m6A-related genes. By employing a consensus clustering method, PD was divided into different m6A clusters based on the selected significant m6A-related genes. Finally, we performed immune cell infiltration analysis to explore the immune infiltration between different clusters. We performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected 11 important m6A-related genes. Four candidate m6A-related genes (YTH Domain Containing 2, heterogeneous nuclear ribonucleoprotein C, leucine-rich pentatricopeptide repeat motif containing protein and insulin-like growth factor binding protein-3) were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the 4 candidate m6A-related genes. The decision curve analysis indicated that patients can benefit from the nomogram model. By employing a consensus clustering method, PD was divided into 2 m6A clusters (cluster A and cluster B) based on the selected significant m6A-related genes. The immune cell infiltration analysis revealed that cluster A and cluster B exhibit distinct immune phenotypes. In conclusion, m6A-related genes play a significant role in the development of PD and our study on m6A clustering may potentially guide personalized treatment strategies for PD in the future.

Intersection of Sleep Disorders and Parkinson Disease: Unveiling the Bidirectional Relationship.

Patients with Parkinson's Disease (PD) frequently exhibit non-motor symptoms, particularly sleep disturbances. Sleep disorders in PD patients are intricately linked to the pathogenesis and progression of PD itself, exacerbating neurodegenerative processes and worsening patient quality of life. This review underscores the significance of sleep disorders in PD, highlighting their prevalence, impact on disease progression, and the bidirectional relationship between sleep disruption and neurodegeneration. It aims to enhance clinician awareness for better diagnosis and management of sleep-related comorbidities in PD. A comprehensive literature search was conducted in PubMed and Scopus using key terms such as "sleep disorders", "Parkinson's disease", "REM sleep behavior disorder", "restless legs syndrome", "insomnia", "obstructive sleep apnea", "excessive daytime sleepiness", "circadian rhythm disorders", "sleep and neurodegeneration". Sleep disorders are prevalent in PD affecting up to 90% of patients. Conditions such as insomnia, REM sleep behavior disorder, restless legs syndrome, obstructive sleep apnea, excessive daytime sleepiness, and circadian rhythm disorders are commonly reported. These disorders are linked to multifactorial biological mechanisms and are associated with more severe disease phenotypes. Of note, several evidence shows that sleep abnormalities may contribute to neuroinflammation and neurodegeneration, further accelerating the disease course. Sleep disturbances are critical non-motor symptoms in PD. Early diagnosis and tailored management of sleep disorders are essential for improving clinical outcomes and potentially offering neuroprotective benefits.

Microbiome-based therapies for Parkinson’s disease

The human gut microbiome dysbiosis plays an important role in the pathogenesis of Parkinson’s disease (PD). The bidirectional relationship between the enteric nervous system (ENS) and central nervous system (CNS) under the mediation of the gut-brain axis control the gastrointestinal functioning. This review article discusses key mechanisms by which modifications in the composition and function of the gut microbiota (GM) influence PD progression and motor control loss. Increased intestinal permeability, chronic inflammation, oxidative stress, α-synuclein aggregation, and neurotransmitter imbalances are some key factors that govern gastrointestinal pathology and PD progression. The bacterial taxa of the gut associated with PD development are discussed with emphasis on the enteric nervous system (ENS), as well as the impact of gut bacteria on dopamine production and levodopa metabolism. The pathophysiology and course of the disease are associated with several inflammatory markers, including TNF-α, IL-1β, and IL-6. Emerging therapeutic strategies targeting the gut microbiome include probiotics, prebiotics, synbiotics, postbiotics, and fecal microbiota transplantation (FMT). The article explored how dietary changes may affect the gut microbiota (GM) and the ways that can affect Parkinson’s disease (PD), with a focus on nutrition-based, Mediterranean, and ketogenic diets. This comprehensive review synthesizes current evidence on the role of the gut microbiome in PD pathogenesis and explores its potential as a therapeutic target. Understanding these complex interactions may assist in the development of novel diagnostic tools and treatment options for this neurodegenerative disorder.

Hsa_Circ_0105596/FTO Inhibits Progression of Parkinson's disease by Sponging miR-187-3p and Regulating eEF2

Backgroud:Parkinson's disease (PD) characterized by inflammation and protein erroneous deposition, whose pathological mechanisms have not been elucidated. NcRNA plays important role in PD, especially when circRNA sponges miRNA, which leads to the breakdown of downstream regulation. The aim of this study is to investigate the dynamic changes between upregulated circRNA and downregulated miRNA during the pathogenesis of PD and their impact on downstream miRNA targets. MethodWe conducted bioinformatics on sequencing data of substantia nigra (SN) and striatum, and intersected differentially expressed genes (Degs) to determine core role of circFTO-miR-187-3p-EEF2 axes in the progression of PD. Firstly, therapeutic effect of knock-down circFTO in PD and its impact on EEF2 were determined in mouse, using immunohistochemistry, HE, Nissl, TUNEL staining and western blot (WB). Targeted binding relationship between circFTO, miR-187-3p, and EEF2 was determined through RNA binding protein immunoprecipitation assays (RIP) and dual luciferase reporter assay. The significance of gene in apoptosis was confirmed through flow cytometry, lentiviral transduction, quantitative real-time PCR, and WB. ResultCircFTO is upregulated and miR-187-3p is downregulated in SN of PD. EEF2 was the core of neural repair related modules in both SN and striatum using Weighted Correlation Network Analysis (WGCNA) and protein-protein interaction (PPI). The binding regulation relationship between circFTO-miR-187-3p-EEF2 was determined through structural analysis, RIP, and dual luciferase reporter assay. After knocking-down circFTO, animals showed alleviated symptoms, decreased levels of oxidative stress and EEF2. Upregulation of miR-187-3p or si-circFTO in SH-SY5Y cells can reduce cell apoptosis, EEF2, and oxidative stress. Moreover, individual interference with EEF2 can partially counteract the induction of 6-OHDA on apoptosis. ConclusionExcessive circFTO sponging miR-187-3p leads to the inability of miR-187-3p to effectively regulate expression of EEF2, resulting in the progression of PD. Moreover, interference with circFTO can effectively reduce brain inflammation and oxidative stress.

AVE0991 ameliorates dopaminergic neuronal damage in Parkinson’s disease through HOTAIRM1/miR-223-3p/α-synuclein axis

Parkinson’s disease (PD) is a prevalent type of neurodegenerative disorder. AVE0991, a non-peptide analogue of Ang-(1–7), by which the progression of PD has been discovered to be ameliorated, but the specific mechanism whereby AVE0991 modulates the progression of PD re-mains unclear. The mice overexpressing human α-syn (A53T) were established to simulate PD pathology, and we also constructed an in vitro model of mouse dopaminergic neurons overexpressing hα-syn (A53T). The [18F] FDG-PET/CT method was employed to assess FDG uptake in human α-syn (A53T) overexpressing mice. Levels of lnc HOTAIRM1 and miR-223-3p were detected via qRT-PCR. Flow cytometry was deployed to assay cell apoptosis. Here, we found that AVE0991 improved behaviour disorders and decreased α-syn expression in the substantia nigra of mice with Parkinson’s disease. AVE0991 inhibited the apoptosis of dopaminergic neurons overexpressing hα-syn (A53T) via lncRNA HOTAIRM1. MiR-223-3p binds to HOTAIRM1 as a ceRNA and directly targets α-syn. Moreover, miR-223-3p level in peripheral blood was found negatively correlated with the α-syn. Our present study shows that the angiotensin-(1–7) analogue AVE0991 targeted at the HOTAIRM1/miR-223-3p axis to degrade α-synuclein in PD mice, and showed neuroprotection in vitro.

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.

Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techniques to identify cognitive progression in individuals with and without PD. Using data from the Parkinson's Progression Marker Initiative, shallow Markov, deep recurrent (long short-term memory [LSTM]), and nonrecurrent (temporal fusion transformer [TFT]) models were compared to predict cognitive status over time. Cognitive status was categorized into normal cognition (NC), mild cognitive impairment (MCI), and dementia. Predictions were made annually for up to 3 years using clinical data, including demographics, cognitive assessments, PD severity, and medical history. Each approach was evaluated using inverse probability weighted (IPW-) F1 scores. An ensemble method combined outputs from the Markov, LSTM, and TFT models. The dataset included 917 individuals (53% PD; 30% at risk for PD; 17% Healthy Controls). The TFT model outperformed others across all annual periods (IPW-F1 = 0.468) compared to the Markov (IPW-F1 = 0.349) and LSTM (IPW-F1 = 0.414) models, with improved performance using an ensemble approach (IPW-F1 = 0.502). For MCI and dementia predictions, which were rarer occurrences compared to NC status (ratios: 50:8:1), the TFT model consistently outperformed competing models, achieving IPW-F1 scores of 0.496 and 0.533 for MCI and dementia, respectively. In conclusion, sequential deep learning models like TFT, which mitigate long-term memory loss and can interpret complex, high-dimensional data, perform best overall in predicting clinically important cognitive transitions. These methods should be further explored for predicting degenerative conditions.

Revisiting PD: Unraveling the brain-first and body-first perspectives

Parkinson’s disease (PD) has undergone significant advancements in diagnosis and treatment over the past century, with apparent dopaminergic cell degeneration on dopamine transporter scans and a strong response to medication being key features. However, the etiology remains complex, involving various pathogenic mechanisms beyond alpha-synuclein accumulation. The recent brain-first versus body-first hypothesis, emerging from advances in functional imaging and clinical symptom clustering, suggests distinct starting points of alpha-synuclein pathology-either in the brain or the body, with subsequent spread via neural connections. This theory, exemplified by the alpha-synuclein origin site and connectome (SOC) model, proposes that body-first PD may originate in the enteric nervous system and spread to the brain, while brain-first PD starts within the central nervous system, such as the olfactory bulb or amygdala. While the SOC model offers valuable insights into the progression of PD, it raises several controversies. Critics argue that the model may oversimplify the disease’s complexity, failing to account for overlapping symptoms and the varying progression rates observed in different subtypes. Furthermore, there are concerns about the lack of longitudinal data and the potential for reclassification of PD subtypes over time. Despite these challenges, the ongoing development of imaging techniques that reflect in-vivo pathology holds promise for resolving these controversies and advancing the selection of patients for disease-modifying therapies.

Circadian rhythm disruption: a potential trigger in Parkinson’s disease pathogenesis

Parkinson’s disease (PD) is a neurodegenerative disease characterized by the gradual loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc), abnormal accumulation of α-synuclein (α-syn), and activation of microglia leading to neuroinflammation. Disturbances in circadian rhythm play a significant role in PD, with most non-motor symptoms associated with disruptions in circadian rhythm. These disturbances can be observed years before motor symptoms appear and are marked by the emergence of non-motor symptoms related to PD, such as rapid eye movement sleep behavior disorder (RBD), restless leg syndrome (RLS), excessive daytime sleepiness (EDS), depression and anxiety, changes in blood pressure, gastrointestinal dysfunction, and urinary problems. Circadian rhythm disruption precedes the onset of motor symptoms and contributes to the progression of PD. In brief, this article outlines the role of circadian rhythm disruption in triggering PD at cellular and molecular levels, as well as its clinical manifestations. It also explores how circadian rhythm research can contribute to preventing the onset and progression of PD from current and future perspectives.

Cholesterol Accelerates Aggregation of α-Synuclein Simultaneously Increasing the Toxicity of Amyloid Fibrils.

A hallmark of Parkinson disease (PD) is a progressive degeneration of neurons in the substantia nigra pars compacta, hypothalamus, and thalamus. Although the exact etiology of irreversible neuronal degeneration is unclear, a growing body of experimental evidence indicates that PD could be triggered by the abrupt aggregation of α-synuclein (α-Syn), a small membrane protein that is responsible for cell vesicle trafficking. Phospholipids uniquely alter the rate of α-Syn aggregation and, consequently, change the cytotoxicity of α-Syn oligomers and fibrils. However, the role of cholesterol in the aggregation of α-Syn remains unclear. In this study, we used Caenorhabditis elegans that overexpressed α-Syn to investigate the effect of low (15%), normal (30%), and high (60%) concentrations of cholesterol on α-Syn aggregation. We found that an increase in the concentration of cholesterol in diets substantially shortened the lifespan of C. elegans. Using biophysical methods, we also investigated the extent to which large unilamellar vesicles (LUVs) with low, normal, and high concentrations of cholesterol altered the rate of α-Syn aggregation. We found that only lipid membranes with a 60% concentration of cholesterol substantially accelerated the rate of protein aggregation. Cell assays revealed that α-Syn fibrils formed in the presence of LUVs with different concentrations of cholesterol exerted very similar levels of cytotoxicity to rat dopaminergic neurons. These results suggest that changes in the concentration of cholesterol in the plasma membrane, which in turn could be caused by nutritional preferences, could accelerate the onset and progression of PD.

Parkinson’s disease models and death signaling: what do we know until now?

Parkinson’s disease (PD) is the second neurodegenerative disorder most prevalent in the world, characterized by the loss of dopaminergic neurons in the Substantia Nigra (SN). It is well known for its motor and non-motor symptoms including bradykinesia, resting tremor, psychiatric, cardiorespiratory, and other dysfunctions. Pathological apoptosis contributes to a wide variety of diseases including PD. Various insults and/or cellular phenotypes have been shown to trigger distinct signaling events leading to cell death in neurons affected by PD. The intrinsic or mitochondrial pathway, inflammatory or oxidative stress-induced extrinsic pathways are the main events associated with apoptosis in PD-related neuronal loss. Although SN is the main brain area studied so far, other brain nuclei are also affected by the disease leading to non-classical motor symptoms as well as non-motor symptoms. Among these, the respiratory symptoms are often overlooked, yet they can cause discomfort and may contribute to patients shortened lifespan after disease diagnosis. While animal and in vitro models are frequently used to investigate the mechanisms involved in the pathogenesis of PD in both the SN and other brain regions, these models provide only a limited understanding of the disease’s actual progression. This review offers a comprehensive overview of some of the most studied forms of cell death, including recent research on potential treatment targets for these pathways. It highlights key findings and milestones in the field, shedding light on the potential role of understanding cell death in the prevention and treatment of the PD. Therefore, unraveling the connection between these pathways and the notable pathological mechanisms observed during PD progression could enhance our comprehension of the disease’s origin and provide valuable insights into potential molecular targets for the developing therapeutic interventions.

Unveiling early signs of Parkinson’s disease via a longitudinal analysis of celebrity speech recordings

Numerous studies proposed methods to detect Parkinson’s disease (PD) via speech analysis. However, existing corpora often lack prodromal recordings, have small sample sizes, and lack longitudinal data. Speech samples from celebrities who publicly disclosed their PD diagnosis provide longitudinal data, allowing the creation of a new corpus, ParkCeleb. We collected videos from 40 subjects with PD and 40 controls and analyzed evolving speech features from 10 years before to 20 years after diagnosis. Our longitudinal analysis, focused on 15 subjects with PD and 15 controls, revealed features like pitch variability, pause duration, speech rate, and syllable duration, indicating PD progression. Early dysarthria patterns were detectable in the prodromal phase, with the best classifiers achieving AUCs of 0.72 and 0.75 for data collected ten and five years before diagnosis, respectively, and 0.93 post-diagnosis. This study highlights the potential for early detection methods, aiding treatment response identification and screening in clinical trials.

Gut-first Parkinson's disease is encoded by gut dysbiome.

In Parkinson's patients, intestinal dysbiosis can occur years before clinical diagnosis, implicating the gut and its microbiota in the disease. Recent evidence suggests the gut microbiota may trigger body-first Parkinson Disease (PD), yet the underlying mechanisms remain unclear. This study aims to elucidate how a dysbiotic microbiome through intestinal immune alterations triggers PD-related neurodegeneration. To determine the impact of gut dysbiosis on the development and progression of PD pathology, wild-type male C57BL/6 mice were transplanted with fecal material from PD patients and age-matched healthy donors to challenge the gut-immune-brain axis. This study demonstrates that patient-derived intestinal microbiota caused midbrain tyrosine hydroxylase positive (TH +) cell loss and motor dysfunction. Ileum-associated microbiota remodeling correlates with a decrease in Th17 homeostatic cells. This event led to an increase in gut inflammation and intestinal barrier disruption. In this regard, we found a decrease in CD4 + cells and an increase in pro-inflammatory cytokines in the blood of PD transplanted mice that could contribute to an increase in the permeabilization of the blood-brain-barrier, observed by an increase in mesencephalic Ig-G-positive microvascular leaks and by an increase of mesencephalic IL-17 levels, compatible with systemic inflammation. Furthermore, alpha-synuclein aggregates can spread caudo-rostrally, causing fragmentation of neuronal mitochondria. This mitochondrial damage subsequently activates innate immune responses in neurons and triggers microglial activation. We propose that the dysbiotic gut microbiome (dysbiome) in PD can disrupt a healthy microbiome and Th17 homeostatic immunity in the ileum mucosa, leading to a cascade effect that propagates to the brain, ultimately contributing to PD pathophysiology. Our landmark study has successfully identified new peripheral biomarkers that could be used to develop highly effective strategies to prevent the progression of PD into the brain.

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.

Effects of microgravity on human iPSC-derived neural organoids on the International Space Station.

Research conducted on the International Space Station (ISS) in low-Earth orbit (LEO) has shown the effects of microgravity on multiple organs. To investigate the effects of microgravity on the central nervous system, we developed a unique organoid strategy for modeling specific regions of the brain that are affected by neurodegenerative diseases. We generated 3-dimensional human neural organoids from induced pluripotent stem cells (iPSCs) derived from individuals affected by primary progressive multiple sclerosis (PPMS)or Parkinson's disease (PD) and non-symptomatic controls, by differentiating them toward cortical and dopaminergic fates, respectively, and combined them with isogenic microglia. The organoids were cultured for a month using a novel sealed cryovial culture method on the International Space Station (ISS) and a parallel set that remained on Earth. Live samples were returned to Earth for analysis by RNA expression and histology and were attached to culture dishes to enable neurite outgrowth. Our results show that both cortical and dopaminergic organoids cultured in LEO had lower levels of genes associated with cell proliferation and higher levels of maturation-associated genes, suggesting that the cells matured more quickly in LEO. This study is continuing with several more missions in order to understand the mechanisms underlying accelerated maturation and to investigate other neurological diseases. Our goal is to make use of the opportunity to study neural cells in LEO to better understand and treat neurodegenerative disease on Earth and to help ameliorate potentially adverse neurological effects of space travel.

Microbiota Orchestra in Parkinson’s Disease: The Nasal and Oral Maestros

Parkinson’s disease (PD) is characterized by the progressive degeneration of dopaminergic neurons, leading to a range of motor and non-motor symptoms. Background/Objectives: Over the past decade, studies have identified a potential link between the microbiome and PD pathophysiology. The literature suggests that specific bacterial communities from the gut, oral, and nasal microbiota may be involved in neuroinflammatory processes, which are hallmarks of PD. This review aims to comprehensively analyze the current research on the composition, diversity, and dysbiosis characteristics of the nasal and oral microbiota in PD. Methods: Through a comprehensive search across scientific databases, we identify twenty original studies investigating the nasal and oral microbiota in PD. Results: Most of these studies demonstrate the substantial roles of bacterial communities in neuroinflammatory pathways associated with PD progression. They also underscore the influences of microbiota-derived factors on key aspects of PD pathology, including alpha-synuclein aggregation and immune dysregulation. Conclusions: Finally, we discuss the potential diagnostic and therapeutic implications of modulating the nasal and oral microbiota in PD management. This analysis seeks to identify potential avenues for future research in order to clarify the complex relationships between these microorganisms and PD.

Impact of 6-OHDA injection and microtrauma in the rat substantia nigra on local brain amyloid beta protein concentrations in the affected area.

Amyloid beta peptides (Aβ) are key indicators of Alzheimer's disease and are also linked to cognitive decline in Parkinson's disease (PD) and other neurodegenerative disorders. This study explored the accumulation of Aβ in a standard 6-Hydroxydopamine (6-OHDA) model of PD. We unilaterally injected 6-OHDA into the substantia nigra of Wistar rats to induce dopaminergic cell degeneration and death, a characteristic of PD. The goal was to detect Aβ protein in tissues and blood vessels showing inflammation or degeneration from the 6-OHDA injection. Our results showed that 6-OHDA injection produced a statistically significant rise in Aβ concentration at the injection site 60 minutes after injection, which was slightly reduced 24 hours post-injection but still significantly higher than in controls. We also tried Gp120 injection in the same zone but it only produced effects comparable to control needle trauma. The presence of Aβ in tissues and blood vessel walls after injection was confirmed through ELISA tests and was supported by immunohistochemical staining of injection areas. We found that the increased Aβ concentration was visible in and around blood vessels and inside blood vessel walls, and also, to a lesser extent in some cells, most probably neurons, in the area. This research highlights the connection between dopaminergic cell poisoning and the accumulation of Aβ, offering insights into the progression of PD to cognitive disorders and dementia.

Temporal ordering of cognitive impairment in Parkinson's disease patients based on disease progression models

IntroductionIdentifying Parkinson's disease (PD) patients at risk of cognitive decline is crucial for enhancing clinical interventions. While several models predicting cognitive decline in PD exist, a new machine learning framework called disease progression models (DPMs) offers a data-driven approach to understand disease evolution. MethodsWe enrolled 423 PD patients and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI). Our study encompassed a range of biomarkers, including motor, neurocognitive, and neuroimaging evaluations at baseline and annually. A methodology was employed to select optimal combinations of biomarkers for constructing DPMs with superior predictive capabilities for both diagnosing and estimating conversion times toward cognitive decline. ResultsAt baseline, the approach showed excellent performance in identifying individuals at high risk of cognitive decline within the first five years. Furthermore, the proposed timeline from cognitive impairment to dementia was also used to explore clinical events such as the onset of cognitive impairment, the development of dementia or amyloid pathology. The presence of amyloid pathology did not alter the progression of cognitive impairment among PD patients. ConclusionsNeuropsychological measures and certain biomarkers, including cerebrospinal fluid (CSF) amyloid beta 42 (Aβ42) and dopamine transporter deficits, can be used to predict cognitive decline and estimate a timeline from cognitive impairment to dementia, with amyloid pathology preceding the onset of dementia in many cases. Our DPMs suggested that the profiles of CSF Aβ42 and phosphorylated tau in PD patients may differ from those in aging patients and those with Alzheimer's disease.