Parkinson's Disease Progression Research Articles

Predictors associated with the rate of progression of nigrostriatal degeneration in Parkinson's disease.

Parkinson's disease (PD) manifests as a wide variety of clinical phenotypes and its progression varies greatly. However, the factors associated with different disease progression remain largely unknown. In this retrospective cohort study, we enrolled 113 patients who underwent 18F-FP-CIT PET scan twice. Given the negative exponential progression pattern of dopamine loss in PD, we applied the natural logarithm to the specific binding ratio (SBR) of two consecutive 18F-FP-CIT PET scans and conducted linear mixed model to calculate individual slope to define the progression rate of nigrostriatal degeneration. We investigated the clinical and dopamine transporter (DAT) availability patterns associated with the progression rate of dopamine depletion in each striatal sub-region. More symmetric parkinsonism, the presence of dyslipidemia, lower K-MMSE total score, and lower anteroposterior gradient of the mean putaminal SBR were associated with faster progression rate of dopamine depletion in the caudate nucleus. More symmetric parkinsonism and lower anteroposterior gradient of the mean putaminal SBR were associated with faster depletion of dopamine in the anterior putamen. Older age at onset, more symmetric parkinsonism, the presence of dyslipidemia, and lower anteroposterior gradient of the mean putaminal SBR were associated with faster progression rate of dopamine depletion in the posterior putamen. Lower striatal mean SBR predicted the development of LID, while lower mean SBR in the caudate nuclei predicted the development of dementia. Our results suggest that the evaluation of baseline clinical features and patterns of DAT availability can predict the progression of PD and its prognosis.

Aggregation-resistant alpha-synuclein tetramers are reduced in the blood of Parkinson’s patients

Synucleinopathies such as Parkinson’s disease (PD) are defined by the accumulation and aggregation of the α-synuclein protein in neurons, glia and other tissues. We have previously shown that destabilization of α-synuclein tetramers is associated with familial PD due to SNCA mutations and demonstrated brain-region specific alterations of α-synuclein multimers in sporadic PD patients following the classical Braak spreading theory. In this study, we assessed relative levels of disordered and higher-ordered multimeric forms of cytosolic α-synuclein in blood from familial PD with G51D mutations and sporadic PD patients. We used an adapted in vitro-cross-linking protocol for human EDTA-whole blood. The relative levels of higher-ordered α-synuclein tetramers were diminished in blood from familial PD and sporadic PD patients compared to controls. Interestingly, the relative amount of α-synuclein tetramers was already decreased in asymptomatic G51D carriers, supporting the hypothesis that α-synuclein multimer destabilization precedes the development of clinical PD. Our data, therefore suggest that measuring α-synuclein tetramers in blood may have potential as a facile biomarker assay for early detection and quantitative tracking of PD progression.

Immunophenotyping Tracks Motor Progression in Parkinson's Disease Associated with a TH Mutation.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, with genetic factors accounting for about 15% of cases. There is a significant challenge in tracking disease progression and treatment response, crucial for developing new therapies. Traditional methods like imaging, clinical monitoring, and biomarker analysis have not conclusively tracked disease progression or treatment response in PD. Our previous research indicated that PD patients with increased dopamine transporter (DAT) and tyrosine hydroxylase (TH) in peripheral blood mononuclear cells (PBMCs) might show disease progression and respond to levodopa treatment. This study evaluates whether DAT- and TH-expressing PBMCs can monitor motor progression in a PD patient with a heterozygous TH mutation. We conducted a longitudinal follow-up of a 46-year-old female PD patient with a TH mutation, assessing her clinical features over 18 months through DaT scans and PBMC immunophenotyping. This was compared with idiopathic PD patients (130 subjects) and healthy controls (80 age/sex-matched individuals). We found an increase in DAT+ immune cells concurrent with worsening motor scores (UPDRS-III). Following levodopa therapy, unlike idiopathic PD patients, TH+ immune cell levels in this patient remained high even as her motor scores improved. Longitudinal immunophenotyping in this PD patient suggests DAT+ and TH+ PBMCs as potential biomarkers for tracking PD progression and treatment efficacy, supporting further exploration of this approach in PD research.

Association of Misfolded α-Synuclein Derived from Neuronal Exosomes in Blood with Parkinson's Disease Diagnosis and Duration.

Misfolded α-synuclein can be detected in blood samples of Parkinson's disease (PD) patients by a seed amplification assay (SAA), but the association with disease duration is not clear, yet. In the present study we aimed to elucidate whether seeding activity of misfolded α-synuclein derived from neuronal exosomes in blood is associated with PD diagnosis and disease duration. Cross-sectional samples of PD patients were analyzed and compared to samples of age- and gender-matched healthy controls using a blood-based SAA. Presence of α-synuclein seeding activity and differences in seeding parameters, including fluorescence response (in arbitrary units) at the end of the amplification assay (F60) were analyzed. Additionally, available PD samples collected longitudinally over 5-9 years were included. In the cross-sectional dataset, 79 of 80 PD patients (mean age 69 years, SD = 8; 56% male) and none of the healthy controls (n = 20, mean age 70 years, SD = 10; 55% male) showed seeding activity (sensitivity 98.8%). When comparing subgroups divided by disease duration, longer disease duration was associated with lower α-synuclein seeding activity (F60: p < 0.001). In the longitudinal analysis 10/11 patients showed a gradual decrease of α-synuclein seeding activity over time. This study confirms the high sensitivity of the blood-based α-synuclein SAA applied here. The negative association of α-synuclein seeding activity in blood with disease duration makes this parameter potentially interesting as biomarker for future studies on the pathophysiology of disease progression in PD, and for biologically oriented trials in this field.

Occupational Pesticide Exposure in Parkinson's Disease Related to GBA and LRRK2 Variants.

The penetrance of common genetic risk variants for Parkinson's disease (PD) is low. Pesticide exposure increases PD risk, but how exposure affects penetrance is not well understood. To determine the relationship between occupational pesticide exposure and PD in people with LRRK2 and GBA risk variants. Participants of the Parkinson's Progression Markers Initiative (PPMI) with a LRRK2-G2019 S or GBA risk variant provided information about occupational pesticide exposure. We compared exposure in carriers with and without PD. Among carriers with PD, we used Cox proportional hazard models to compare time-to impairment in balance, cognition, and activities of daily living (ADLs) between participants with and without prior occupational pesticide exposure. 378 participants with a risk variant provided exposure information; 176 with LRRK2-G2019 S (54 with and 122 without PD) and 202 with GBA variants (47 with and 155 without PD). Twenty-six participants reported pesticide exposure. People with a GBA variant and occupational pesticide exposure had much higher odds of PD (aOR: 5.4, 95% CI 1.7-18.5, p < 0.01). People with a LRRK2 variant and a history of occupational pesticide exposure had non-significantly elevated odds of PD (aOR 1.3, 95% CI 0.4-4.6, p = 0.7). Among those with PD, pesticide exposure was associated with a higher risk of balance problems and cognitive impairment in LRRK2-PD and functional impairment in GBA-PD, although associations were not statistically significant. Occupational pesticide exposure may increase penetrance of GBA-PD and may be associated with faster symptom progression. Further studies in larger cohorts are necessary.

Motor dysfunction in Parkinson's patients: depression differences in a latent growth model.

This study aims to utilize latent growth model (LGM) to explore the developmental trajectory of motor dysfunction in Parkinson's disease (PD) patients and investigate the relationship between depression and motor dysfunction. Four-year follow-up data from 389 PD patients were collected through the Parkinson's Progression Marker Initiative (PPMI). Firstly, a univariate LGM was employed to examine the developmental trajectory of motor dysfunction in PD patients. Subsequently, depression levels were introduced as covariates into the model, and depression was further treated as a parallel growth latent variable to study the longitudinal relationship between motor dysfunction and depression. In the trajectory analysis of motor dysfunction, the fit indices for the quadratic growth LGM model were χ2 = 7.419, df = 6, CFI = 0.998, TLI = 0.997, SRMR = 0.019, and RMSEA = 0.025, indicating that the growth trend of motor dysfunction follows a quadratic curve rather than a simple linear pattern. Introducing depression symptoms as time-varying covariates to explore their effect on motor dysfunction revealed significant positive correlations (β = 0.383, p = 0.026; β = 0.675, p < 0.001; β = 0.385, p = 0.019; β = 0.415, p = 0.014; β = 0.614, p = 0.003), suggesting that as depression levels increase, motor dysfunction scores also increase. Treating depression as a parallel developmental process in the LGM, the regression coefficients for depression intercept on motor dysfunction intercept, depression slope on motor dysfunction slope, and depression quadratic factor on motor dysfunction quadratic factor were 0.448 (p = 0.046), 1.316 (p = 0.003), and 1.496 (p = 0.038), respectively. These significant regression coefficients indicate a complex relationship between depression and motor dysfunction, involving not only initial level associations but also growth trends over time and possible quadratic effects. This study indicates a quadratic growth trajectory for motor dysfunction in PD, suggesting a continuous increase in severity with a gradual deceleration in growth rate. The relationship between depression and motor dysfunction is complex, involving initial associations, evolving trends over time, and potential quadratic effects. Exacerbation of depressive symptoms may coincide with motor function deterioration.

An interpretable deep learning Bayesian optimized random forest framework for the diagnosis of Parkinson's disease in structural magnetic resonance images

AbstractAs one of the fastest‐growing neurological disorders, Parkinson's disease is a neurodegenerative, cardinally motor ailment. Associated with the late onset of symptoms and often misdiagnosed, it inflicts massive life handicaps. To aid in its early detection, the research community is seeking alternative in‐vivo biomarkers, mainly in the field of neuroimaging. This study aimed to build a computer‐aided diagnosis system to distinguish between healthy controls (HC) and early Parkinsonian patients (PD). Our classification paradigm encompasses a Convolutional Neural Network built from scratch to extract discriminatory features, followed by a Bayesian‐optimized Random Forest classifier (CNN_RF). We trained and validated our customized model on the MRI images of 178 PD and 124 HC, taken from the benchmark Parkinson's Progression Markers Initiative (PPMI) dataset, as well as their brain tissue segments and Jacobian determinant maps. The classification was evaluated using seven performance metrics. For visual interpretation, we employed Grad‐CAM to generate saliency maps. Our findings demonstrate the robustness of the proposed method, as it outperformed deep‐tuned ResNet18, VGG16, and SqueezeNet, implemented for comparison. Furthermore, it resulted in 98.9% accuracy, 98.6% specificity, 99.0% precision, 99.5% F1‐score, a 97.7% Matthews correlation coefficient (MCC), and an AUC of 99.6% for the classification of fused gray matter and Jacobian map features. This indicates that the tailored CNN_RF possesses superior adaptability to identify intrinsic characteristics linked to the disease, as highlighted in the salient maps showcasing pertinent regions in the striatum, thalamus, frontal, temporal, and insular cortices for Parkinson's disease classification. Moreover, it offers a single modality and cost‐effective CAD, which could pave the way for a feasible early PD diagnosis. As data availability and AI robustness increase, PD diagnosis will no longer be a challenging task in the future.

Brain Connectivity Networks Constructed Using MRI for Predicting Patterns of Atrophy Progression in Parkinson Disease.

Background Whether connectome mapping of structural and functional connectivity across the brain could be used to predict patterns of atrophy progression in patients with mild Parkinson disease (PD) has not been well studied. Purpose To assess the structural and functional connectivity of brain regions in healthy controls and its relationship with the spread of gray matter (GM) atrophy in patients with mild PD. Materials and Methods This prospective study included participants with mild PD and controls recruited from a single center between January 2012 and December 2023. Participants with PD underwent three-dimensional T1-weighted brain MRI, and the extent of regional GM atrophy was determined at baseline and every year for 3 years. The structural and functional brain connectome was constructed using diffusion tensor imaging and resting-state functional MRI in healthy controls. Disease exposure (DE) indexes-indexes of the pathology of each brain region-were defined as a function of the structural or functional connectivity of all the connected regions in the healthy connectome and the severity of atrophy of the connected regions in participants with PD. Partial correlations were tested between structural and functional DE indexes of each GM region at 1- or 2-year follow-up and atrophy progression at 2- or 3-year follow-up. Prediction models of atrophy at 2- or 3-year follow-up were constructed using exhaustive feature selection. Results A total of 86 participants with mild PD (mean age at MRI, 60 years ± 8 [SD]; 48 male) and 60 healthy controls (mean age at MRI, 62 years ± 9; 31 female) were included. DE indexes at 1 and 2 years were correlated with atrophy at 2 and 3 years (r range, 0.22-0.33; P value range, .002-.04). Models including DE indexes predicted GM atrophy accumulation over 3 years in the right caudate nucleus and some frontal, parietal, and temporal brain regions (R2 range, 0.40-0.61; all P < .001). Conclusion The structural and functional organization of the brain connectome plays a role in atrophy progression in the early stages of PD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yamada in this issue.

Mechanistic Evaluation of miRNAs and Their Targeted Genes in the Pathogenesis and Therapeutics of Parkinson's Disease.

MicroRNA (miRNA) are usually 18-25 nucleotides long non-coding RNA targeting post-transcriptional regulation of genes involved in various biological processes. The function of miRNA is essential for maintaining a homeostatic cellular condition, regulating autophagy, cellular motility, and inflammation. Dysregulation of miRNA is responsible for multiple disorders, including neurodegeneration, which has emerged as a severe problem in recent times and has verified itself as a life-threatening condition that can be understood by the continuous destruction of neurons affecting various cognitive and motor functions. Parkinson's disease (PD) is the second most common, permanently debilitating neurodegenerative disorder after Alzheimer's, mainly characterized by uncontrolled tremor, stiffness, bradykinesia or akinesia (slowness in movement), and post-traumatic stress disorder. PD is mainly caused by the demolition of the primary dopamine neurotransmitter secretory cells and dopaminergic or dopamine secretory neurons in the substantia nigra pars compacta of the midbrain, which are majorly responsible for motor functions. In this study, a systematic evaluation of research articles from year 2017 to 2022 was performed on multiple search engines, and lists of miRNA being dysregulated in PD in different body components were generated. This study highlighted miR-7, miR-124, miR-29 family, and miR-425, showing altered expression levels during PD's progression, further regulating the expression of multiple genes responsible for PD.

A Machine Learning Approach for Early Identification of Prodromal Parkinson's Disease.

Parkinson's disease (PD) affects approximately 6 million people worldwide. Data analysis of early PD symptoms using machine learning (ML) models may provide an inexpensive, non-invasive, and simple method for the remote diagnosis of early PD. The aim of this project was to analyze voice, computer keystrokes, spiral drawings, and gait data involving PD patients and controls available in public databases using ML models and identify early PD characteristics that are more pronounced than others. An ML model was developed using Random Forest to analyze existing clinical data for PD patients, prodromal PD patients with REM (rapid eye movement) sleep behavior disorder (RBD) symptoms, and non-PD healthy controls. We reviewed and collected data from the UCI (University of California Irvine) Machine Learning Repository, PPMI (Parkinson's Progression Markers Initiative), and Kaggle databases. ML analysis was carried out on voice samples in PD and RBD patients, computer keystroke data, spiral drawings, and gait datasets. The ML prediction model developed may be helpful in improving risk prediction for PD, enabling early intervention and resource prioritization. The ML study suggests that voice analysis is the most robust test, followed by computer keystroke data, spiral drawings, and gait analysis, in that order. Voice is affected even in RBD patients, revealing that it is a sensitive and early measure of prodromal PD. The low accuracy of the analysis indicates that several PD-positive samples may remain undetected and unclassified. Combining all four features, that is, voice analysis, computer keystroke data, spiral drawings, and gait analysis, may improve the overall accuracy.

Left corticospinal tract could be a biomarker to identify the dual prodromal LRRK2/GBA mutated Parkinson's disease.

Prodromal Parkinson's disease (PD) carriers of dual leucine-rich repeat kinase 2 (LRRK2) and glucosylceramidase β (GBA) variants are rare, and their biomarkers are less well developed. This study aimed to investigate the biomarkers for diagnosing the prodromal phase of LRRK2-GBA-PD (LRRK2-GBA-prodromal). We assessed the clinical and whole-brain white matter microstructural characteristics of 54 prodromal PD carriers of dual LRRK2 (100% M239T) and GBA (95% N409S) variants, along with 76 healthy controls (HCs) from the Parkinson's Progression Markers Initiative (PPMI) cohort. By analyzing the four values of 100 nodes on 20 fiber bundles, totaling 8000 data points, we identified the smallest p value in the fractional anisotropy (FA) value of the 38th segment of left corticospinal tract (L-CST) with differences between LRRK2-GBA-prodromal and HCs (p = 8.94 × 10-9). The FA value of the 38th node of the L-CST was significantly lower in LRRK2-GBA-prodromal (FA value, 0.65) compared with HCs (FA value, 0.71). The receiver-operating characteristic curve showed a cut-off value of 0.218 for the FA value of L-CST, providing sufficient sensitivity (79.2%) and specificity (72.2%) to distinguish double mutation prodromal PD from the healthy population. L-CST, especially the 38th node, may potentially serve as a biomarker for distinguishing individuals with double mutation prodromal PD from the healthy population.

Statin use and the risk of Parkinson's disease in persons with diabetes: A nested case-control study.

Persons with diabetes may have an elevated risk of Parkinson's disease (PD). Statin use could also modify the progression of PD. The aim was to study whether there is an association between statin exposure and risk of PD in persons with diabetes. A nationwide, nested case-control study restricted to people with diabetes was performed as part of nationwide register-based Finnish study on PD (FINPARK). Study included 2017 PD cases and their 7934 matched controls without PD. Persons with PD were diagnosed between 1999 and 2015, and statin use (1995-2015) was determined from Prescription Register. In the main analysis, exposure at least 3 years before outcome was considered. Cumulative exposure was categorized into tertiles, and associations were analysed with conditional logistic regression (adjusted with comorbidities and number of antidiabetic drugs). Prevalence of statin use was similar in PD cases and controls, with 54.2% of cases and 54.4% controls exposed before the lag time (adjusted odds ratio [aOR] =1.03; 95% confidence interval [CI]: 0.92-1.15). Those in the highest cumulative statin exposure tertile had higher risk of PD than statin nonusers (aOR =1.22; 95% CI: 1.04-1.43), or those in the lowest cumulative statin exposure tertile (aOR =1.29; 95% CI: 1.07-1.57). Our nationwide study that controlled for diabetes duration and used 3-year lag between exposure and outcome to account for reverse causality does not provide support for the hypothesis that statin use decreases the risk of PD.

Mechanisms Underlying the Propagation of α-Synuclein Seeds Derived from the Blood of Patients with α-Synucleinopathies

The aggregation of α-synuclein (α-syn), associated with Parkinson's disease (PD) extends from the peripheral autonomic nervous system to the cerebral cortex, indicating a neural circuit-based mechanism of spread. However, recent studies, have proposed alternative propagation routes beyond neural pathways, including transmission via bodily fluids, such as the blood. This notion expands our understanding of PD progression, underscoring the complexity of α-syn spread and its implications in disease management and therapeutic strategies.

Immersive Virtual Reality High-Intensity Aerobic Training to Slow Parkinson’s Disease: The ReViPark Program

Intense exercise can be neuromodulatory and neuroprotective against Parkinson’s disease (PD). Indoor cycling is a good and safe option for this population, but some barriers (monotonous and repetitive environment, lack of monitoring, and an inability to compete with others) could limit its use. Immersive virtual reality (IVR) could be a possible solution to some of these drawbacks, increasing patient motivation and thus adherence to forced exercise programs using cycloergometers. A double-blind, randomized, controlled clinical trial was conducted to explore the feasibility and effects of the ReViPark program (high-intensity pedaling + IVR for 3 months with two sessions per week) on PD progression. In all, 52 people with PD were allocated to the ReViPark group (n = 30; 70.87 ± 6.67 years) and smart cycloergometer group (n = 22; 70.59 ± 6.67 years). This program was feasible, with no adverse effects (no significant Simulator Sickness Questionnaire symptoms and a low negative experience rating for the Game Experience Questionnaire (0.04/4)), no dropouts, high adherence, and good usability (System Usability Scale score of 82.90%). The ReViPark group showed significant improvements in functionality, quality of life, and disease progression as measured by their balance, gait and risk of falling (Timed Up and Go p = 0.028; Tinetti test p = 0.046), PDQ-39 scores (p = 0.035), and MDS-UPDRS scores (p = 0.001). This program is feasible and could lead to improvements in the functional parameters, quality of life, and symptomatology of the participants.

Levodopa-Induced Dyskinesias in Corticobasal Degeneration: A Case Report and Review of the Literature

Corticobasal degeneration (CBD) is a neurodegenerative disorder characterized by several clinical features, including progressive asymmetric parkinsonism, dystonia, myoclonus and cortical sensory signs. This clinical presentation is referred to as Corticobasal Syndrome (CBS), considering the diverse underlying pathologies associated CBS.

The interplay between gut microbiota and the brain-gut axis in Parkinson's disease treatment.

This study delves into the pivotal role of the gut microbiota and the brain-gut axis in Parkinson's Disease (PD), a neurodegenerative disorder with significant motor and non-motor implications. It posits that disruptions in gut microbiota-dysbiosis-and alterations in the brain-gut axis contribute to PD's pathogenesis. Our findings highlight the potential of the gastrointestinal system's early involvement in PD, suggested by the precedence of gastrointestinal symptoms before motor symptoms emerge. This observation implies a possible gut-originated disease pathway. The analysis demonstrates that dysbiosis in PD patients leads to increased intestinal permeability and systemic inflammation, which in turn exacerbates neuroinflammation and neurodegeneration. Such insights into the interaction between gut microbiota and the brain-gut axis not only elucidate PD's underlying mechanisms but also pave the way for novel therapeutic interventions. We propose targeted treatment strategies, including dietary modifications and fecal microbiota transplantation, aimed at modulating the gut microbiota. These approaches hold promise for augmenting current PD treatment modalities by alleviating both motor and non-motor symptoms, thereby potentially improving patient quality of life. This research underscores the significance of the gut microbiota in the progression and treatment of PD, advocating for an integrated, multidisciplinary approach to develop personalized, efficacious management strategies for PD patients, combining insights from neurology, microbiology, and nutritional science.

Innovative Deep Learning Approach for Parkinson's Disease Prediction: Leveraging Convolutional Neural Networks for Early Detection

INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting movement control, highlighting the importance of timely detection and intervention to improve patient quality of life. However, accurate diagnosis remains challenging due to its similarity with other neurological conditions, leading to a 25% rate of inaccurate manual diagnoses. Convolutional Neural Networks (CNNs) offer a promising solution for medical image classification and analysis, capable of learning complex patterns in images. In this study, we introduce an innovative automated diagnostic model using CNN that gives an appropriate output about if the person is diagnosed with PD or not.OBJECTIVES: The study aims to develop an automated diagnostic model using CNNs to accurately diagnose PD. By leveraging the Parkinson Progression Markers Initiative (PPMI) dataset, which provides benchmarked MRI images of PD and healthy controls, the model seeks to differentiate between PD and non-PD cases.METHODS: A Convolutional Neural Network (CNN) is a deep learning algorithm that is suitable for medical image classification and analysis as they are able to learn complex patterns in images and identify the hidden patterns and trend of data. We have used VGG16 and ResNet50 pretrained CNN models to achieve high accuracy and prediction.RESULTS: These models collectively achieved an outstanding accuracy rate of 97%. To validate our model performance, we test our model by applying various algorithms and activation functions such as EfficientNetB0, EfficientNetB1 and softmax, sigmoid, and ReLu respectively.CONCLUSION: This research introduces an innovative framework for the early detection of Parkinson’s disease using convolutional neural networks. Our system demonstrates remarkable capability to identify subtle patterns indicative of PD in its early stages.

Incidence and predictors of postural abnormalities in Parkinson's disease: a PPMI cohort study.

Axial postural abnormalities (PA) are invalidating symptoms of Parkinson's disease (PD). Risk factors for PA are unknown. We sought to evaluate PA incidence and risk factors over the first 4-6years of PD. We included 441 PD patients from the Parkinson's Progression Markers Initiative (PPMI) cohort with data at diagnosis and after 4-year follow-up. PA was defined according to a posture item ≥ 2 at the Movement Disorder Society-sponsored-revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) in Off therapeutic condition. The Kruskal-Wallis test was used to compare characteristics of patients without PA ('no-PA'), with PA at disease onset ('baseline-PA'), and PA developed during follow-up ('develop-PA'). To identify predictors of PA development, univariate and multivariate Cox regression analyses were performed considering demographic, clinical and therapeutic variables. 10.9% of patients showed PA at baseline and 23.7% developed PA within the first 4-6years since diagnosis. Older age, malignant phenotype, higher MDS-UPDRS part III, Hoehn & Yahr, and dysautonomia (SCOPA-AUT) score, and lower levels of physical activity were predictors of PA development at the univariate analysis. Older age (Hazard ratio [HR] per year: 1.041) and higher MDS-UPDRS part III score (HR per point: 1.035) survived as PA development predictors in the multivariate analysis. PPMI cohort data show that > 30% of PD patients present PA within the first 4-6years of disease. Older age at onset and higher motor burden are associated with a higher risk for PA development. The protective role of physical activity merits to be further investigated.

Physical Exercise-Induced Activation of NRF2 and BDNF as a Promising Strategy for Ferroptosis Regulation in Parkinson's Disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Ferroptosis, an iron-dependent form of regulated cell death, may contribute to the progression of PD owing to an unbalanced brain redox status. Physical exercise is a complementary therapy that can modulate ferroptosis in PD by regulating the redox system through the activation of nuclear factor (erythroid-derived 2)-like 2 (NRF2) and brain-derived neurotrophic factor (BDNF) signaling. However, the precise effects of physical exercise on ferroptosis in PD remain unclear. In this review, we explored how physical exercise influences NRF2 and BDNF signaling and affects ferroptosis in PD. We further investigated relevant publications over the past two decades by searching the PubMed, Web of Science, and Google Scholar databases using keywords related to physical exercise, PD, ferroptosis, and neurotrophic factor antioxidant signaling. This review provides insights into current research gaps and demonstrates the necessity for future research to elucidate the specific mechanisms by which exercise regulates ferroptosis in PD, including the assessment of different exercise protocols and their long-term effects. Ultimately, exploring these aspects may lead to the development of improved exercise interventions for the better management of patients with PD.

Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning.

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson's disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson's Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson's disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.