The prediagnostic phase of Parkinson's disease

Web Application to Enable Online Social Interactions in a Parkinson Disease Risk Cohort: Feasibility Study and Social Network Analysis

BACKGROUND

There is evidence that social interaction has an inverse association with the development of neurodegenerative diseases. PREDICT-Parkinson Disease (PREDICT-PD) is an online UK cohort study that stratifies participants for risk of future Parkinson disease (PD).

OBJECTIVE

This study aims to explore the methodological approach and feasibility of assessing the digital social characteristics of people at risk of developing PD and their social capital within the PREDICT-PD platform, making hypotheses about the relationship between web-based social engagement and potential predictive risk indicators of PD.

METHODS

A web-based application was built to enable social interaction through the PREDICT-PD portal. Feedback from existing members of the cohort was sought and informed the design of the pilot. Dedicated staff used weekly engagement activities, consisting of PD-related research, facts, and queries, to stimulate discussion. Data were collected by the hosting platform. We examined the pattern of connections generated over time through the cumulative number of posts and replies and ego networks using social network analysis. We used network metrics to describe the bonding, bridging, and linking of social capital among participants on the platform. Relevant demographic data and Parkinson risk scores (expressed as an odd 1:x) were analyzed using descriptive statistics. Regression analysis was conducted to estimate the relationship between risk scores (after log transformation) and network measures.

RESULTS

Overall, 219 participants took part in a 4-month pilot forum embedded in the study website. In it, 200 people (n=80, 40% male and n=113, 57% female) connected in a large group, where most pairs of users could reach one another either directly or indirectly through other users. A total of 59% (20/34) of discussions were spontaneously started by participants. Participation was asynchronous, with some individuals acting as "brokers" between groups of discussions. As more participants joined the forum and connected to one another through online posts, distinct groups of connected users started to emerge. This pilot showed that a forum application within the cohort web platform was feasible and acceptable and fostered digital social interaction. Matching participants' web-based social engagement with previously collected data at individual level in the PREDICT-PD study was feasible, showing potential for future analyses correlating online network characteristics with the risk of PD over time, as well as testing digital social engagement as an intervention to modify the risk of developing neurodegenerative diseases.

CONCLUSIONS

The results from the pilot suggest that an online forum can serve as an intervention to enhance social connectedness and investigate whether patterns of online engagement can impact the risk of developing PD through long-term follow-up. This highlights the potential of leveraging online platforms to study the role of social capital in moderating PD risk and underscores the feasibility of such approaches in future research or interventions.

Single-molecule characterization of salivary protein aggregates from Parkinson's disease patients: a pilot study

Saliva is a convenient and accessible biofluid that has potential as a future diagnostic tool for Parkinson's disease. Candidate diagnostic tests for Parkinson's disease to date have predominantly focused on measurements of α-synuclein in CSF, but there is a need for accurate tests utilizing more easily accessible sample types. Prior studies utilizing saliva have used bulk measurements of salivary α-synuclein to provide diagnostic insight. Aggregate structure may influence the contribution of α-synuclein to disease pathology. Single-molecule approaches can characterize the structure of individual aggregates present in the biofluid and may, therefore, provide greater insight than bulk measurements. We have employed an antibody-based single-molecule pulldown assay to quantify salivary α-synuclein and amyloid-β peptide aggregate numbers and subsequently super-resolved captured aggregates using direct Stochastic Optical Reconstruction Microscopy to describe their morphological features. We show that the salivary α-synuclein aggregate/amyloid-β aggregate ratio is increased almost 2-fold in patients with Parkinson's disease (n = 20) compared with controls (n = 20, P < 0.05). Morphological information also provides insight, with saliva from patients with Parkinson's disease containing a greater proportion of larger and more fibrillar amyloid-β aggregates than control saliva (P < 0.05). Furthermore, the combination of count and morphology data provides greater diagnostic value than either measure alone, distinguishing between patients with Parkinson's disease (n = 17) and controls (n = 18) with a high degree of accuracy (area under the curve = 0.87, P < 0.001) and a larger dynamic range. We, therefore, demonstrate for the first time the application of highly sensitive single-molecule imaging techniques to saliva. In addition, we show that aggregates present within saliva retain relevant structural information, further expanding the potential utility of saliva-based diagnostic methods.

Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression

Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.

Depressive Symptoms and the Subsequent Risk of Parkinson's Disease: A Nationwide Cohort Study

OBJECTIVE

Only a few studies have focused on depressive symptoms and Parkinson's disease (PD) risk. As a time lag exists from the onset of depressive symptoms to the diagnosis of depression, elucidating the association between depressive symptoms and PD development might be helpful for the early prediction of PD. We investigate the association between depressive symptoms and subsequent PD risk using nationwide population-based cohort database.

DESIGN AND SETTING

Cohort study using the Korean National Health Insurance Service data between 2007 and 2017, with longitudinal follow-up until 2019.

PARTICIPANTS

A total of 98,296 elderly people responded to a self-reported questionnaire from the National Health Screening Program on depressive symptoms.

MEASUREMENTS

The association between depressive symptoms such as 1) decreased activity or motivation, 2) worthlessness, and 3) hopelessness and PD risk was analyzed.

RESULTS

During median 5.06-year follow-up, 839 PD cases occurred: 230 in individuals with depressive symptoms and 609 in those without symptoms. Results showed an increased risk of PD development in those with depressive symptoms (HR = 1.47, 95% CI, 1.26-1.71), with dose-response association between the number of depressive symptoms and PD risk. Even in those already diagnosed with depression, combined depressive symptoms were linked to a higher risk compared to those without symptoms (with symptoms, HR = 2.71, 95% CI, 2.00-3.68; without symptoms, HR = 1.84, 95% CI, 1.43-2.36).

CONCLUSION

Individuals with depressive symptoms were at an increased risk of developing PD, and there was a dose-response association between the number of depressive symptoms and PD risk.

Dealing with the diagnosis of Parkinson's disease and its implications for couple functioning in the early stage: An interpretative phenomenological analysis

BACKGROUND

For couples facing Parkinson's disease, marital relationships are significantly impacted, even at the early stages of the disease. However, very few studies have explicitly explored the functioning of the couple and how both partners deal with Parkinson's disease. The aim of this study was to explore the experiences and strategies of couples facing Parkinson's disease in the early stage using dyadic interpretative phenomenological analysis.

METHODS

Fifteen couples agreed to participate in the study. Semistructured interviews were conducted with each partner separately regarding his or her individual experience with Parkinson's disease, the couple's history, the impact of the diagnosis on the functioning of the couple, and his or her projections for the future.

RESULTS

Three higher-order themes emerged from the analyses. The first theme, "Being tested by the diagnosis", highlights 4 dyadic configurations according to the individual's and the couple's capacity for adjustment following the diagnosis: "noncongruent", "collapsed", "relieved" and "avoidant". The second theme, "Talking about everything except the disease", underlines that communication about the disease is often avoided both within the couple and with relatives to protect the persons with Parkinson's disease or respect their wishes. The third theme, "Supporting each other", describes the different levels of harmony between the two partners in the management of daily life and symptoms and their relational impacts.

CONCLUSION

These results allow us to better understand the experiences of both partners and to highlight the importance of promoting better acceptance of the diagnosis by persons with Parkinson's disease to allow better communication between partners and with relatives. Such support prevents disease-specific distress and facilitates better adjustment in the later stages of the disease.

Animal Approaches to Studying Risk Factors for Parkinson's Disease: A Narrative Review

Despite recent efforts to search for biomarkers for the pre-symptomatic diagnosis of Parkinson's disease (PD), the presence of risk factors, prodromal signs, and family history still support the classification of individuals at risk for this disease. Human epidemiological studies are useful in this search but fail to provide causality. The study of well-known risk factors for PD in animal models can help elucidate mechanisms related to the disease's etiology and contribute to future prevention or treatment approaches. This narrative review aims to discuss animal studies that investigated four of the main risk factors and/or prodromal signs related to PD: advanced age, male sex, sleep alterations, and depression. Different databases were used to search the studies, which were included based on their relevance to the topic. Although still in a reduced number, such studies are of great relevance in the search for evidence that leads to a possible early diagnosis and improvements in methods of prevention and treatment.

Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here?

Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.

Advancing nutrition science to meet evolving global health needs

Populations in crisis!A global overview of health challenges and policy efforts within the scope of current nutrition issues, from persistent forms of undernutrition, including micronutrient deficiency, to diet-related chronic diseases. Nutrition science has evolved from a therapeutic and prevention emphasis to include a focus on diets and food systems. Working and consensus definitions are needed, as well as guidance related to healthy diets and the emerging issues that require further research and consensus building. Between nutrient deficiency and chronic disease, nutrition has evolved from focusing exclusively on the extremes of overt nutrient deficiency and chronic disease prevention, to equipping bodies with the ability to cope with physiologic, metabolic, and psychological stress. Just what is 'optimal nutrition', is that a valid public health goal, and what terminology is being provided by the nutrition science community? Nutrition research on 'healthspan', resilience, and intrinsic capacity may provide evidence to support optimal nutrition. Finally, experts provide views on ongoing challenges of achieving consensus or acceptance of the various definitions and interventions for health promotion, and how these can inform government health policies.Nutrition topics that receive particular focus in these proceedings include choline, NAD-replenishment in neurodegenerative diseases, and xanthophyll carotenoids. Choline is a crucial nutrient essential for cellular metabolism, requiring consumption from foods or supplements due to inadequate endogenous synthesis. Maternal choline intake is vital for fetal and infant development to prevent neural tube defects. Neurodegenerative diseases pose a growing health challenge, lacking effective therapies. Nutrition, including NAD-replenishing nutrients, might aid prevention. Emerging research indicates xanthophyll carotenoids enhance vision and cognition, potentially impacting age-related diseases.

P. mirabilis-derived pore-forming haemolysin, HpmA drives intestinal alpha-synuclein aggregation in a mouse model of neurodegeneration

BACKGROUND

Recent studies suggesting the importance of the gut-microbiome in intestinal aggregated alpha synuclein (α-syn) have led to the exploration of the possible role of the gut-brain axis in central nervous system degeneration. Proteus mirabilis (P. mirabilis), a gram-negative facultative anaerobic bacterium, has been linked to brain neurodegeneration in animal studies. We hypothesised that P. mirabilis-derived virulence factors aggregate intestinal α-synuclein and could prompt the pathogenesis of dopaminergic neurodegeneration in the brain.

METHODS

We used vagotomised- and antibiotic-treated male murine models to determine the pathogenesis of P. mirabilis during brain neurodegeneration. The neurodegenerative factor that is driven by P. mirabilis was determined using genetically mutated P. mirabilis. The pathological functions and interactions of the virulence factors were determined in vitro.

FINDINGS

The results showed that P. mirabilis-induced motor dysfunction and neurodegeneration are regulated by intestinal α-syn aggregation in vagotomised- or antibiotic-treated murine models. We deduced that the specific virulence factor, haemolysin A (HpmA), plays a role in the pathogenesis of P. mirabilis. HpmA is involved in α-synuclein oligomerisation and membrane pore formation, resulting in the activation of mTOR-mediated autophagy signalling in intestinal neuroendocrine cells.

INTERPRETATION

Taken together, the results of the present study suggest that HpmA can interact with α-syn and act as a possible indicator of brain neurodegenerative diseases that are induced by P. mirabilis.

FUNDING

This study was supported by a grant from the National Research Foundation of Korea.

Developing a Staging Scheme for Essential Tremor: A Discussion of Organizing Principles

Essential tremor (ET) is a chronic, progressive neurological disease that may negatively affect patients' lives. While there has been considerable progress in ET research, some fundamental issues remain unaddressed. One such issue is disease staging. Staging schemes have inherent value and are part of the dialogue that clinicians have with other movement disorders patients. We highlight the value of and challenges with developing a staging system for ET and organize a discussion around the potential steps in developing such a system. Diseases for which there are staging schemes generally have a number of shared characteristics. ET has numerous features that would lend themselves to a staging scheme: emerging evidence supporting the existence of a premotor phase of disease, insidious onset, progressive worsening of arm tremor, spread of tremor to other body regions, the observation that patients seem to be at increased risk for other conditions within the same organ (i.e., emergence of Parkinson's disease and Alzheimer's disease in excessive numbers of ET patients), pathological changes in the cerebellum whose evolution can be ordered from (i) those that compromise the physical integrity and physiological function of Purkinje cells, (ii) subsequent changes that are reparative and regenerative, and (iii) eventual cell death. Challenges to formulating a staging scheme are the absence of both a biological marker and an "end stage" of disease. The sum of combined evidence suggests that a staging scheme would be of value. We provide initial thoughts as to how to begin to structure such a staging scheme.

Follow-up of pre-motor symptoms of Parkinson's disease in adult patients with Gaucher disease type 1 and analysis of their lysosomal enzyme profiles in the CSF

BACKGROUND

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Its classic motor symptoms may be preceded by non-motor symptoms (NMS). Population studies have identified GBA variants as risk factors for idiopathic PD. The increased risk of PD has also been suggested in other Lysosomal Storage Disorders (LSDs).

OBJECTIVE

To assess the evolution of the prevalence of NMS compatible with PD in a cohort of South Brazilian adult patients with Gaucher Disease (GD) type 1, already evaluated 3 years ago (2018). Cerebrospinal Fluid (CSF) was collected to assess the levels of LSD enzymes (beta-hexosaminidases, beta-glucuronidase) and biomarker of macrophage activation (chitotriosidase, ChT), compared to controls (metachromatic leukodystrophy, MLD). Cognition was evaluated by the Montreal Cognitive Assessment (MoCA) questionnaire, daytime sleepiness by the Epworth Sleepiness Scale (ESS), depression by Beck´s Inventory, constipation by the Unified Multiple System Atrophy Rating Scale (UMSARS) scale, and REM sleep behavior disorder by the single-question screen. Hyposmia was assessed with Sniffin' Sticks (SST).

RESULTS

Nineteen patients completed the follow-up (mean age of the sample was 44 years; range, 26-71). The patient with the highest number of NMS at the baseline (4 including the lowest SST score) was diagnosed with PD four years later. Apart from an improvement in the ESS score, no other statistical significance was found between the number of NMS between the first and second evaluation, nor between patients with one L444P variant (n = 5) and the rest of the cohort. CSF was collected in five patients (mean age of the sample was 40 years, range 30-53. A significant difference was found in the mean CSF activity levels of beta-hexosaminidases and beta-glucuronidase between GD1 and MLD patients. Mean ChT (CSF) was 62 nmol/h/mL in GD patients and 142 in MLD (n = 6) patients.

CONCLUSIONS

The patient with the highest number of NMS in our 2018 cohort was the one that developed PD, corroborating with the importance of this longitudinal follow-up. CSF and plasma analysis might allow a better understanding of the neurodegenerative processes connecting PD and the lysosomal environment. Further analysis is needed to understand this relationship.

Detecting Minor Symptoms of Parkinson's Disease in the Wild Using Bi-LSTM with Attention Mechanism

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients' quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.

Stimulus Onset Asynchrony Affects Weighting-related Event-related Spectral Power in Self-motion Perception

Self-motion perception relies primarily on the integration of the visual, vestibular, proprioceptive, and somatosensory systems. There is a gap in understanding how a temporal lag between visual and vestibular motion cues affects visual-vestibular weighting during self-motion perception. The beta band is an index of visual-vestibular weighting, in that robust beta event-related synchronization (ERS) is associated with visual weighting bias, and robust beta event-related desynchronization is associated with vestibular weighting bias. The present study examined modulation of event-related spectral power during a heading judgment task in which participants attended to either visual (optic flow) or physical (inertial cues stimulating the vestibular, proprioceptive and somatosensory systems) motion cues from a motion simulator mounted on a MOOG Stewart Platform. The temporal lag between the onset of visual and physical motion cues was manipulated to produce three lag conditions: simultaneous onset, visual before physical motion onset, and physical before visual motion onset. There were two main findings. First, we demonstrated that when the attended motion cue was presented before an ignored cue, the power of beta associated with the attended modality was greater than when visual-vestibular cues were presented simultaneously or when the ignored cue was presented first. This was the case for beta ERS when the visual-motion cue was attended to, and beta event-related desynchronization when the physical-motion cue was attended to. Second, we tested whether the power of feature-binding gamma ERS (demonstrated in audiovisual and visual-tactile integration studies) increased when the visual-vestibular cues were presented simultaneously versus with temporal asynchrony. We did not observe an increase in gamma ERS when cues were presented simultaneously, suggesting that electrophysiological markers of visual-vestibular binding differ from markers of audiovisual and visual-tactile integration. All event-related spectral power reported in this study were generated from dipoles projecting from the left and right motor areas, based on the results of Measure Projection Analysis.

AJ76 and UH232 as potential agents for diagnosing early-stage Parkinson's disease

We recently reported that the "Dopamine Neuron Challenge Test" (DNC Test), a diagnostic method that measures the levels of dopamine metabolites in cerebrospinal fluid (CSF) and plasma samples after pharmacologically inducing a transient dopamine release, can detect early-stage Parkinson's disease (PD) with high sensitivity and selectivity in mouse models. The use of haloperidol in the original DNC test to challenge dopamine neurons was less than ideal, as it may cause extrapyramidal motor symptoms. Here we report an improved DNC Test, in which the original challenging agents, haloperidol and methylphenidate, are replaced by a single challenging agent, a dopamine autoreceptor preferring antagonist AJ76 or UH232. We show that the improved DNC Test can achieve the same level of sensitivity and selectivity in detecting early PD in a mouse model without causing motor side effects. These findings significantly improve the practicality of using the DNC Test as a screening or diagnostic test for detecting early-stage PD in the high-risk population in humans.

Functionally Validating Evolutionary Conserved Risk Genes for Parkinson's Disease in Drosophila melanogaster

Parkinson's disease (PD) is a heterogeneous and complex neurodegenerative disorder and large-scale genetic studies have identified >130 genes associated with PD. Although genomic studies have been decisive for our understanding of the genetic contributions underlying PD, these associations remain as statistical associations. Lack of functional validation limits the biological interpretation; however, it is labour extensive, expensive, and time consuming. Therefore, the ideal biological system for functionally validating genetic findings must be simple. The study aim was to assess systematically evolutionary conserved PD-associated genes using Drosophila melanogaster. From a literature review, a total of 136 genes have found to be associated with PD in GWAS studies, of which 11 are strongly evolutionary conserved between Homo sapiens and D. melanogaster. By ubiquitous gene expression knockdown of the PD-genes in D. melanogaster, the flies' escape response was investigated by assessing their negative geotaxis response, a phenotype that has previously been used to investigate PD in D. melanogaster. Gene expression knockdown was successful in 9/11 lines, and phenotypic consequences were observed in 8/9 lines. The results provide evidence that genetically modifying expression levels of PD genes in D. melanogaster caused reduced climbing ability of the flies, potentially supporting their role in dysfunctional locomotion, a hallmark of PD.

Computational Studies Applied to Linalool and Citronellal Derivatives Against Alzheimer's and Parkinson's Disorders: A Review with Experimental Approach

Alzheimer's and Parkinson's are neurodegenerative disorders that affect a great number of people around the world, seriously compromising the quality of life of individuals, due to motor and cognitive damage. In these diseases, pharmacological treatment is used only to alleviate symptoms. This emphasizes the need to discover alternative molecules for use in prevention. Using Molecular Docking, this review aimed to evaluate the anti-Alzheimer's and anti-Parkinson's activity of linalool and citronellal, as well as their derivatives. Before performing Molecular Docking simulations, the compounds' pharmacokinetic characteristics were evaluated. For Molecular Docking, 7 chemical compounds derived from citronellal, and 10 compounds derived from linalool, and molecular targets involved in Alzheimer's and Parkinson's pathophysiology were selected. According to the Lipinski rules, the compounds under study presented good oral absorption and bioavailability. For toxicity, some tissue irritability was observed. For Parkinson-related targets, the citronellal and linalool derived compounds revealed excellent energetic affinity for α-Synuclein, Adenosine Receptors, Monoamine Oxidase (MAO), and Dopamine D1 receptor proteins. For Alzheimer disease targets, only linalool and its derivatives presented promise against BACE enzyme activity. The compounds studied presented high probability of modulatory activity against the disease targets under study, and are potential candidates for future drugs.

High resolution 16S rRNA gene Next Generation Sequencing study of brain areas associated with Alzheimer's and Parkinson's disease

Introduction

Alzheimer's (AD) and Parkinson's disease (PD) are neurodegenerative conditions characterized by incremental deposition of β-amyloid (Aβ) and α-synuclein in AD and PD brain, respectively, in relatively conserved patterns. Both are associated with neuroinflammation, with a proposed microbial component for disease initiation and/or progression. Notably, Aβ and α-synuclein have been shown to possess antimicrobial properties. There is evidence for bacterial presence within the brain, including the oral pathobiont Porphyromonas gingivalis, with cognitive impairment and brain pathology being linked to periodontal (gum) disease and gut dysbiosis.

Methods

Here, we use high resolution 16S rRNA PCR-based Next Generation Sequencing (16SNGS) to characterize bacterial composition in brain areas associated with the early, intermediate and late-stage of the diseases.

Results and discussion

This study reveals the widespread presence of bacteria in areas of the brain associated with AD and PD pathology, with distinctly different bacterial profiles in blood and brain. Brain area profiles were overall somewhat similar, predominantly oral, with some bacteria subgingival and oronasal in origin, and relatively comparable profiles in AD and PD brain. However, brain areas associated with early disease development, such as the locus coeruleus, were substantially different in bacterial DNA content compared to areas affected later in disease etiology.

Evaluation of the PREDIGT score’s performance in identifying newly diagnosed Parkinson’s patients without motor examination

Several recent publications described algorithms to identify subjects with Parkinson’s disease (PD). In creating the “PREDIGT Score”, we previously developed a hypothesis-driven, simple-to-use formula to potentially calculate the incidence of PD. Here, we tested its performance in the ‘De Novo Parkinson Study’ (DeNoPa) and ‘Parkinson’s Progression Marker Initiative’ (PPMI); the latter included participants from the ‘FOllow Up persons with Neurologic Disease’ (FOUND) cohort. Baseline data from 563 newly diagnosed PD patients and 306 healthy control subjects were evaluated. Based on 13 variables, the original PREDIGT Score identified recently diagnosed PD patients in the DeNoPa, PPMI + FOUND and the pooled cohorts with area-under-the-curve (AUC) values of 0.88 (95% CI 0.83–0.92), 0.79 (95% CI 0.72–0.85), and 0.84 (95% CI 0.8–0.88), respectively. A simplified version (8 variables) generated AUC values of 0.92 (95% CI 0.89–0.95), 0.84 (95% CI 0.81–0.87), and 0.87 (0.84–0.89) in the DeNoPa, PPMI, and the pooled cohorts, respectively. In a two-step, screening-type approach, self-reported answers to a questionnaire (step 1) distinguished PD patients from controls with an AUC of 0.81 (95% CI 0.75–0.86). Adding a single, objective test (Step 2) further improved classification. Among seven biological markers explored, hyposmia was the most informative. The composite AUC value measured 0.9 (95% CI 0.88–0.91) in DeNoPa and 0.89 (95% CI 0.84–0.94) in PPMI. These results reveal a robust performance of the original PREDIGT Score to distinguish newly diagnosed PD patients from controls in two established cohorts. We also demonstrate the formula’s potential applicability to enriching for PD subjects in a population screening-type approach.

Machine learning based risk prediction for Parkinson's disease with nationwide health screening data

Although many studies have been conducted on machine learning (ML) models for Parkinson's disease (PD) prediction using neuroimaging and movement analyses, studies with large population-based datasets are limited. We aimed to propose PD prediction models using ML algorithms based on the National Health Insurance Service-Health Screening datasets. We selected individuals who participated in national health-screening programs > 5 times between 2002 and 2015. PD was defined based on the ICD-code (G20), and a matched cohort of individuals without PD was selected using a 1:1 random sampling method. Various ML algorithms were applied for PD prediction, and the performance of the prediction models was compared. Neural networks, gradient boosting machines, and random forest algorithms exhibited the best average prediction accuracy (average area under the receiver operating characteristic curve (AUC): 0.779, 0.766, and 0.731, respectively) among the algorithms validated in this study. The overall model performance metrics were higher in men than in women (AUC: 0.742 and 0.729, respectively). The most important factor for predicting PD occurrence was body mass index, followed by total cholesterol, glucose, hemoglobin, and blood pressure levels. Smoking and alcohol consumption (in men) and socioeconomic status, physical activity, and diabetes mellitus (in women) were highly correlated with the occurrence of PD. The proposed health-screening dataset-based PD prediction model using ML algorithms is readily applicable, produces validated results, and could be a useful option for PD prediction models.

Effect Modification between Genes and Environment and Parkinson's Disease Risk

Parkinson's disease (PD) is a complex neurodegenerative condition in which genetic and environmental factors interact to contribute to its etiology. Remarkable progress has been made in deciphering disease etiology through genetic approaches, but there is limited data about how environmental and genetic factors interact to modify penetrance, risk, and disease severity. Here, we provide insights into environmental modifiers of PD, discussing precedents from other neurological and non-neurological conditions. Based on these examples, we outline genetic and environmental factors contributing to PD and review potential environmental modifiers of penetrance and clinical variability in monogenic and idiopathic PD. We also highlight the potential challenges and propose how future studies might tackle these important questions. ANN NEUROL 2022;92:715-724.

Multifunctional role of natural products for the treatment of Parkinson's disease: At a glance

Natural substances originating from plants have long been used to treat neurodegenerative disorders (NDs). Parkinson's disease (PD) is a ND. The deterioration and subsequent cognitive impairments of the midbrain nigral dopaminergic neurons distinguish by this characteristic. Various pathogenic mechanisms and critical components have been reported, despite the fact that the origin is unknown, such as protein aggregation, iron buildup, mitochondrial dysfunction, neuroinflammation and oxidative stress. Anti-Parkinson drugs like dopamine (DA) agonists, levodopa, carbidopa, monoamine oxidase type B inhibitors and anticholinergics are used to replace DA in the current treatment model. Surgery is advised in cases where drug therapy is ineffective. Unfortunately, the current conventional treatments for PD have a number of harmful side effects and are expensive. As a result, new therapeutic strategies that control the mechanisms that contribute to neuronal death and dysfunction must be addressed. Natural resources have long been a useful source of possible treatments. PD can be treated with a variety of natural therapies made from medicinal herbs, fruits, and vegetables. In addition to their well-known anti-oxidative and anti-inflammatory capabilities, these natural products also play inhibitory roles in iron buildup, protein misfolding, the maintenance of proteasomal breakdown, mitochondrial homeostasis, and other neuroprotective processes. The goal of this research is to systematically characterize the currently available medications for Parkinson's and their therapeutic effects, which target diverse pathways. Overall, this analysis looks at the kinds of natural things that could be used in the future to treat PD in new ways or as supplements to existing treatments. We looked at the medicinal plants that can be used to treat PD. The use of natural remedies, especially those derived from plants, to treat PD has been on the rise. This article examines the fundamental characteristics of medicinal plants and the bioactive substances found in them that may be utilized to treat PD.

Parkinson's Disease: Risk Factor Modification and Prevention

The global burden of Parkinson's disease (PD) has increased from 2.5 to 6.1 million since the 1990s. This is expected to rise as the world population ages and lives longer. With the current consensus on the existence of a prediagnostic phase of PD, which can be divided into a preclinical stage and a prodromal stage, we can better define the risk markers and prodromal markers of PD in the broader context of PD pathogenesis. Here, we review this pathogenetic process, and discuss the evidence behind various heritability factors, exposure to pesticides and farming, high dairy consumption, and traumatic brain injuries that have been known to raise PD risk. Physical activity, early active lifestyle, high serum uric acid, caffeine consumption, exposure to tobacco, nonsteroidal anti-inflammatory drugs, and calcium channel blockers, as well as the Mediterranean and the MIND diets are observed to lower PD risk. This knowledge, when combined with ways to identify at-risk populations and early prodromal PD patients, can help the clinician make practical recommendations. Most importantly, it helps us set the parameters for epidemiological studies and create the paradigms for clinical trials.

Lipid Nanoparticles: Promising Treatment Approach for Parkinson's Disease

Parkinson’s disease (PD), a neurodegenerative disorder, is a life-altering, debilitating disease exhibiting a severe physical, psychological, and financial burden on patients. Globally, approximately 7–10 million people are afflicted with this disease, with the number of cases estimated to increase to 12.9 million by 2040. PD is a progressive movement disorder with nonmotor symptoms, including insomnia, depression, anxiety, and anosmia. While current therapeutics are available to PD patients, this treatment remains palliative, necessitating alternative treatment approaches. A major hurdle in treating PD is the protective nature of the blood–brain barrier (BBB) and its ability to limit access to foreign molecules, including therapeutics. Drugs utilized presently are nonspecific and administered at dosages that result in numerous adverse side effects. Nanomedicine has emerged as a potential strategy for treating many diseases. From the array of nanomaterials available, lipid nanoparticles (LNPs) possess various advantages, including enhanced permeability to the brain via passive diffusion and specific and nonspecific transporters. Their bioavailability, nontoxic nature, ability to be conjugated to drugs, and targeting moieties catapult LNPs as a promising therapeutic nanocarriers for PD. While PD-related studies are limited, their potential as therapeutics is evident in their formulations as vaccines. This review is aimed at examining the roles and properties of LNPs that make them efficient therapeutic nanodelivery vehicles for the treatment of PD, including therapeutic advances made to date.

Wearable sensor device-based detection of decreased heart rate variability in Parkinson's disease

The evidence that heart rate variability (HRV) decreases during early Parkinson's disease (PD) largely depends on electrocardiogram data. In this study, we examined HRV in PD using wearable sensors and assessed various evaluation methods for detecting disease-related alterations. We evaluated 27 patients with PD and 23 disease controls. The wearable sensors POLAR V800 HR and POLAR H10 were used for the HRV measurements. The participants wore the two sensors for approximately 24 h, and long-term HRV data were acquired. We analyzed the standard deviation of normal R-R intervals (SDNN) and coefficient of variation of R-R intervals (CVRR) for every 100 consecutive beats. Focusing on the fluctuation of SDNN and CVRR, we extracted the minimum, first decile, first quartile, and median values of SDNN and CVRR. The area under the receiver operating characteristic curve (AUC) for each HRV parameter was calculated to differentiate PD from the disease controls. The minimum values of SDNN and CVRR had the highest AUC (SDNN: AUC 0.90, 95% confidence interval [CI] 0.78-0.96; CVRR: AUC 0.90, CI 0.76-0.96) among the evaluation methods tested. The minimum values of SDNN and CVRR were significantly decreased in PD (SDNN: 9.5 ± 4.0 ms vs. 4.4 ± 2.0 ms, p < 0.0001; CVRR: 1.15 ± 0.33% vs. 0.65 ± 0.24%, p < 0.0001). We detected decreased HRV in PD using wearable sensors. Analyzing the minimum values of the HRV parameter in long-term recordings appears to be appropriate for detecting the decrease in HRV in PD.

The emerging postural instability phenotype in idiopathic Parkinson disease

Identification of individuals at high risk for rapid progression of motor and cognitive signs in Parkinson disease (PD) is clinically significant. Postural instability and gait dysfunction (PIGD) are associated with greater motor and cognitive deterioration. We examined the relationship between baseline clinical factors and the development of postural instability using 5-year longitudinal de-novo idiopathic data (n = 301) from the Parkinson’s Progressive Markers Initiative (PPMI). Logistic regression analysis revealed baseline features associated with future postural instability, and we designated this cohort the emerging postural instability (ePI) phenotype. We evaluated the resulting ePI phenotype rating scale validity in two held-out populations which showed a significantly higher risk of postural instability. Emerging PI phenotype was identified before onset of postural instability in 289 of 301 paired comparisons, with a median progression time of 972 days. Baseline cognitive performance was similar but declined more rapidly in ePI phenotype. We provide an ePI phenotype rating scale (ePIRS) for evaluation of individual risk at baseline for progression to postural instability.

α-Synuclein Radiotracer Development and In Vivo Imaging: Recent Advancements and New Perspectives

α-Synucleinopathies including idiopathic Parkinson's disease, dementia with Lewy bodies and multiple systems atrophy share overlapping symptoms and pathological hallmarks. Selective neurodegeneration and Lewy pathology are the main hallmarks of α-synucleinopathies. Currently, there is no imaging biomarker suitable for a definitive early diagnosis of α-synucleinopathies. Although dopaminergic deficits detected with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) radiotracers can support clinical diagnosis by confirming the presence of dopaminergic neurodegeneration, dopaminergic imaging cannot visualize the preceding disease process, nor distinguish α-synucleinopathies from tauopathies with dopaminergic neurodegeneration, especially at early symptomatic disease stage when clinical presentation is often overlapping. Aggregated α-synuclein (αSyn) could be a suitable imaging biomarker in α-synucleinopathies, because αSyn aggregation and therefore, Lewy pathology is evidently an early driver of α-synucleinopathies pathogenesis. Additionally, several antibodies and small molecule compounds targeting aggregated αSyn are in development for therapy. However, there is no way to directly measure if or how much they lower the levels of aggregated αSyn in the brain. There is clearly a paramount diagnostic and therapeutic unmet medical need. To date, aggregated αSyn and Lewy pathology inclusion bodies cannot be assessed ante-mortem with SPECT or PET imaging because of the suboptimal binding characteristics and/or physicochemical properties of current radiotracers. The aim of this narrative review is to highlight the suitability of aggregated αSyn as an imaging biomarker in α-synucleinopathies, the current limitations with and lessons learned from αSyn radiotracer development, and finally to propose antibody-based ligands for imaging αSyn aggregates as a complementary tool rather than an alternative to small molecule ligands. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society.

The lived experience during the peri-diagnostic period of breast cancer: A scoping review

OBJECTIVES

The aim of this scoping review is to provide an overview of the existing research that investigates the lived experience during the peri-diagnostic period of breast cancer.

METHODS

Nine databases were searched for relevant literature between January 2007 and April 2019. Data were extracted and categorized using deductive and inductive approaches.

RESULTS

A majority of the 66 studies included used qualitative methods to retrospectively explore the treatment decision making process of female breast cancer patients. Patients experienced uncertainty, emotional distress, and a need for more information from providers and relied on social support and family guidance during this period.

CONCLUSIONS

The results of this review show that the burdens experienced during the peri-diagnostic period parallel those in later periods of cancer care. However, these burdens are prompted by different circumstances. More research is needed to explore the lived experience during this period through the use of mixed-methods and by recruiting a diverse sample with regards to role in the breast cancer experience, age, gender, race, and ethnicity.

PRACTICE IMPLICATIONS

Interventions positioned at earlier points in the breast cancer experience should provide informational support, which could be delivered through shared decision making models. Additional support could be facilitated by patient navigation programs and health information technology.

Can detection and prediction models for Alzheimer's Disease be applied to Prodromal Parkinson's Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures

Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

Dopamine Neuron Challenge Test for early detection of Parkinson's disease

Diagnosing Parkinson's disease (PD) before the clinical onset proves difficult because the hallmark PD symptoms do not manifest until more than 60% of dopamine neurons in the substantia nigra pars compacta have been lost. Here we show that, by evoking a transient dopamine release and subsequently measuring the levels of dopamine metabolites in the cerebrospinal fluid and plasma, a hypodopaminergic state can be revealed when less than 30% of dopamine neurons are lost in mouse PD models. These findings may lead to sensitive and practical screening and diagnostic tests for detecting early PD in the high-risk population.

Investigation of the Possible Correlation between Idiopathic Parkinson's Disease and Diabetes Mellitus in Egyptian Patients: A Pilot Study

OBJECTIVES

To study the diabetes-Parkinson's disease (PD) linkage.

METHODS

The investigators recorded the rapid eye movement sleep behavior disorder screening questionnaire (RBDSQ) score for 60 diabetic patients: 30 patients were treated with metformin-inclusive sulfonylurea and 30 patients were treated with sulphonylurea(s) monotherapy and matched with 30 controls. We evaluated blood glucose kinetics during a 75 g oral glucose tolerance test for (22) nondiabetic parkinsonian patients and (10) controls. The motor complications scores were recorded for all parkinsonian patients using the relevant parts of the Unified Parkinson's Disease Rating Scale (UPDRS) part IV.

RESULTS

Diabetics recorded higher scores of RBDSQ than controls (p < 0.001), with no differences related to antidiabetic therapy. In nondiabetic PD patients, after oral glucose, blood glucose was significantly higher at T1 (p < 0.001) than controls. Moreover, the total area under the time curve for blood glucose levels was significantly higher in PD compared to controls (281.22 ± 52.25 vs. 245.65 ± 48.63 mg.hr./dL; p=0.013). Higher blood glucose levels were associated with motor abnormalities. Diabetic PD patients recorded higher scores of UPDRS (p < 0.001).

CONCLUSION

Diabetes mellitus and Parkinson's disease are linked, which raises concerns about either of them, probably increasing the risk of the other. This trial is registered with NCT03685357.

Pre-symptomatic intervention for autism spectrum disorder (ASD): defining a research agenda

Autism spectrum disorder (ASD) impacts an individual's ability to socialize, communicate, and interact with, and adapt to, the environment. Over the last two decades, research has focused on early identification of ASD with significant progress being made in understanding the early behavioral and biological markers that precede a diagnosis, providing a catalyst for pre-symptomatic identification and intervention. Evidence from preclinical trials suggest that intervention prior to the onset of ASD symptoms may yield more improved developmental outcomes, and clinical studies suggest that the earlier intervention is administered, the better the outcomes. This article brings together a multidisciplinary group of experts to develop a conceptual framework for behavioral intervention, during the pre-symptomatic period prior to the consolidation of symptoms into diagnosis, in infants at very-high-likelihood for developing ASD (VHL-ASD). The overarching goals of this paper are to promote the development of new intervention approaches, empirical research, and policy efforts aimed at VHL-ASD infants during the pre-symptomatic period (i.e., prior to the consolidation of the defining features of ASD).

A Disease Progression Model to Quantify the Nonmotor Symptoms of Parkinson's Disease in Participants With Leucine-Rich Repeat Kinase 2 Mutation

Leucine-rich repeat kinase 2 (LRRK2) inhibitors are currently in clinical development as interventions to slow progression of Parkinson's disease (PD). Understanding the rate of progression in PD as measured by both motor and nonmotor features is particularly important in assessing the potential therapeutic effect of LRRK2 inhibitors in clinical development. Using standardized data from the Critical Path for Parkinson's Unified Clinical Database, we quantified the rate of progression of the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I (nonmotor aspects of experiences of daily living) in 158 participants with PD who were carriers and 598 participants with PD who were noncarriers of at least one of three different LRRK2 gene mutations (G2019S, R1441C/G, or R1628P). Age and disease duration were found to predict baseline disease severity, while presence of at least one of these three LRRK2 mutations was a predictor of the rate of MDS-UPDRS Part I progression. The estimated progression rate in MDS-UPDRS Part I was 0.648 (95% confidence interval: 0.544, 0.739) points per year in noncarriers of a LRRK2 mutation and 0.259 (95% confidence interval: 0.217, 0.295) points per year in carriers of a LRRK2 mutation. This analysis demonstrates that the rate of progression based on MDS-UPDRS Part I is ~ 60% lower in carriers as compared with noncarriers of LRRK2 gene mutations.

Understanding the Pathogenesis Involved in Parkinson's Disease and Potential Therapeutic Treatment Strategies

A vast advancement has been made in the treatment related to central nervous system disorders especially Parkinson's disease. The development in therapeutics and a better understanding of the targets results in upsurge of many promising therapies for Parkinson's disease. Parkinson's disease is defined by neuronal degeneration and neuroinflammation and it is reported that the presence of the neurofibrillary aggregates such as Lewy bodies is considered as the marker. Along with this, it is also characterized by the presence of motor and non-motor symptoms, as seen in Parkinsonian patients. A lot of treatment options mainly focus on prophylactic measures or the symptomatic treatment of Parkinson's disease. Neuroinflammation and neurodegeneration are the point of interest which can be exploited as a new target to emphasis on Parkinson's disease. A thorough study of these targets helps in modifications of those molecules which are particularly involved in causing the neuronal degeneration and neuroinflammation in Parkinson's disease. A lot of drug regimens are available for the treatment of Parkinson's disease, although levodopa remains the choice of drug for controlling the symptoms, yet is accompanied with significant snags. It is always suggested to use other drug therapies concomitantly with levodopa. A number of significant causes and therapeutic targets for Parkinson's disease have been identified in the last decade, here an attempt was made to highlight the most significant of them. It was also found that the treatment regimen and involvement of therapies are totally dependent on individuals and can be tailored to the needs of each individual patient.

Transcranial sonography in differential diagnosis of Parkinson disease and other movement disorders

Background:

Reports evaluating the efficacy of transcranial sonography (TCS) for the differential diagnosis of Parkinson disease (PD) and other movement disorders in China are scarce. Therefore, this study aimed to assess the application of TCS for the differential diagnosis of PD, multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and essential tremor (ET) in Chinese individuals.

Methods:

From 2017 to 2019, 500 inpatients treated at the Department of Dyskinesia, Beijing Tiantan Hospital, Capital Medical University underwent routine transcranial ultrasound examination. The cross-sections at the midbrain and thalamus levels were scanned, and the incidence rates of substantia nigra (SN) positivity and the incidence rates of lenticular hyperechoic area were recorded. The echo of the SN was manually measured.

Results:

Of the 500 patients, 125 were excluded due to poor signal in temporal window sound transmission. Among the 375 individuals with good temporal window sound transmission, 200 were diagnosed with PD, 90 with ET, 50 with MSA, and 35 with PSP. The incidence rates of SN positivity differed significantly among the four patient groups (χ² = 121.061, P < 0.001). Between-group comparisons were performed, and the PD group showed a higher SN positivity rate than the ET (χ² = 94.898, P < 0.017), MSA (χ² = 57.619, P < 0.017), and PSP (χ² = 37.687, P < 0.017) groups. SN positivity showed a good diagnostic value for differentiating PD from the other three movement diseases, collectively or individually. The incidences of lenticular hyperechoic area significantly differed among the four patient groups (χ² = 38.904, P < 0.001). Next, between-group comparisons were performed. The lenticular hyperechoic area was higher in the PD group than in the ET (χ² = 6.714, P < 0.017) and MSA (χ² = 18.680, P < 0.017) groups but lower than that in the PSP group (χ² = 0.679, P > 0.017).

Conclusion:

SN positivity could effectively differentiate PD from ET, PSP, and MSA in a Chinese population.

A systematic review of associations between common SNCA variants and clinical heterogeneity in Parkinson's disease

There is great heterogeneity in both the clinical presentation and rate of disease progression among patients with Parkinson’s disease (PD). This can pose prognostic difficulties in a clinical setting, and a greater understanding of the risk factors that contribute to modify disease course is of clear importance for optimizing patient care and clinical trial design. Genetic variants in SNCA are an established risk factor for PD and are candidates to modify disease presentation and progression. This systematic review aimed to summarize all available primary research reporting the association of SNCA polymorphisms with features of PD. We systematically searched PubMed and Web of Science, from inception to 1 June 2020, for studies evaluating the association of common SNCA variants with age at onset (AAO) or any clinical feature attributed to PD in patients with idiopathic PD. Fifty-eight studies were included in the review that investigated the association between SNCA polymorphisms and a broad range of outcomes, including motor and cognitive impairment, sleep disorders, mental health, hyposmia, or AAO. The most reproducible findings were with the REP1 polymorphism or rs356219 and an earlier AAO, but no clear associations were identified with an SNCA polymorphism and any individual clinical outcome. The results of this comprehensive summary suggest that, while there is evidence that genetic variance in the SNCA region may have a small impact on clinical outcomes in PD, the mechanisms underlying the association of SNCA polymorphisms with PD risk may not be a major factor driving clinical heterogeneity in PD.

Probing the Pre-diagnostic Phase of Parkinson's Disease in Population-Based Studies

Parkinson's disease covers a wide spectrum of symptoms, ranging from early non-motor symptoms to the characteristic bradykinesia, tremor and rigidity. Although differences in the symptomatology of Parkinson's disease are increasingly recognized, there is still a lack of insight into the heterogeneity of the pre-diagnostic phase of Parkinson's disease. In this perspective, we highlight three aspects regarding the role of population-based studies in providing new insights into the heterogeneity of pre-diagnostic Parkinson's disease. First we describe several specific advantages of population-based cohort studies, including the design which overcomes some common biases, the broad data collection and the high external validity. Second, we draw a parallel with the field of Alzheimer's disease to provide future directions to uncover the heterogeneity of pre-diagnostic Parkinson's disease. Finally, we anticipate on the emergence of prevention and disease-modification trials and the potential role of population-based studies herein. In the coming years, bridging gaps between study designs will be essential to make vital advances in elucidating the heterogeneity of pre-diagnostic Parkinson's disease.

REM sleep without atonia and nocturnal body position in prediagnostic Parkinson's disease

BACKGROUND

Sleep disturbances are features of Parkinson's disease (PD), that can already occur before PD diagnosis. The most investigated prodromal PD sleep disorder is REM sleep behavior disorder (RBD). The relation between other polysomnographic (PSG) alterations and the prediagnostic stages of PD, however, is less clear.

METHODS

We performed a retrospective case-control study to characterize polysomnographic alterations in PD and prediagnostic PD. We included 63 PD subjects (33 subjects that underwent a video-PSG before PD diagnosis [13 with and 20 without RBD] and 30 subjects that underwent a PSG after PD diagnosis) and 30 controls. PSGs were analyzed for sleep stages, different RSWA variables, body position, arousals, periodic limb movements, and REM density.

RESULTS

Higher subscores of all RSWA variables were observed in subjects with PD and prediagnostic PD (with and without RBD). Total RSWA, tonic RSWA and chin RSWA severity were significant predictors for all PD and prediagnostic PD groups. Our study also shows a higher percentage of nocturnal supine body position in all PD and prediagnostic PD groups. Supine body position percentage is the highest in the PD group and has a positive correlation with time since diagnosis.

CONCLUSIONS

These findings suggest that increased total, tonic and chin RSWA as well as nocturnal supine body position are already present in prediagnostic PD, independently of RBD status. Prospective longitudinal studies are necessary to confirm the additional value of these PSG abnormalities as prodromal PD biomarkers.

No Detectable Effect on Visual Responses Using Functional MRI in a Rodent Model of α-Synuclein Expression

Parkinson's disease (PD) is a progressive neurodegenerative disease that is typically diagnosed late in its progression. There is a need for biomarkers suitable for monitoring the disease progression at earlier stages to guide the development of novel neuroprotective therapies. One potential biomarker, α-synuclein, has been found in both the familial cases of PD, as well as the sporadic cases and is considered a key feature of PD. α-synuclein is naturally present in the retina, and it has been suggested that early symptoms of the visual system may be used as a biomarker for PD. Here, we use a viral vector to induce a unilateral expression of human wild-type α-synuclein in rats as a mechanistic model of protein aggregation in PD. We employed functional magnetic resonance imaging (fMRI) to investigate whether adeno-associated virus (AAV) mediated expression of human wild-type α-synuclein alter functional activity in the visual system. A total of 16 rats were injected with either AAV-α-synuclein (n = 7) or AAV-null (n = 9) in the substantia nigra pars compacta (SNc) of the left hemisphere. The expression of α-synuclein was validated by a motor assay and postmortem immunohistochemistry. Five months after the introduction of the AAV-vector, fMRI showed robust blood oxygen level-dependent (BOLD) responses to light stimulation in the visual systems of both control and AAV-α-synuclein animals. However, our results demonstrate that the expression of AAV-α-synuclein does not affect functional activation of the visual system. This negative finding suggests that fMRI-based read-outs of visual responses may not be a sensitive biomarker for PD.

A novel capsule-based smell test fabricated via coaxial dripping

In this paper, we demonstrate that aromatic oil capsules, produced by dripping droplets, can offer a simple, yet effective, testing tool to aid in the diagnosis of various diseases, in which the loss of smell is a key symptom. These include chronic neurological conditions such as Parkinson's and Alzheimer's diseases, and acute respiratory infections such as that caused by COVID-19. The capsules were fabricated by concentrically dripping oil/alginate droplets, from a coaxial nozzle, into an oppositely charged ionic liquid. This fabrication technique enables full control over the capsule size, the shell thickness and the volume of the encapsulated oil. After formation, liquid capsules were left to dry and form a solid crust surrounding the oil. The prototype test consists of placing a standardized number of capsules between adhesive strips that users crush and pull apart to release the smell. In addition to the fabrication method, a simple mathematical model was developed to predict the volume of encapsulated oil within the capsule in terms of the flow rate ratio and the nozzle size. Tensile tests show that capsule strength is inversely proportional to its size owing to an increase in the shell thickness. By increasing the alginate concentration, the load required to rupture the capsule increases, to the point where capsules are too stiff to be broken by a fingertip grip. Results from a preliminary screening test, within a group of patients with Parkinson's disease, found that smells were detectable using a 'forced choice' paradigm.

microRNA signatures in prodromal REM sleep behavior disorder and early Parkinson's disease as noninvasive biomarkers

The flow of gene expression or "The central dogma of molecular biology": DNA - RNA - protein, proposed by Watson & Crick sixty years ago, is a tightly controlled cell process. In the middle of this journey, the mRNA molecule is regulated by "RNA interference" (RNAi), a posttranscriptional gene silencing mechanism. A microRNA is an endogenous short double-stranded RNA that down-regulates hundreds of mRNAs by RNAi, maintaining healthy cell physiology. In contrast, aberrant expressions of microRNAs play a role in Parkinson's disease (PD) pathogenesis. The damage may start at an early period of brain degeneration, in the non-motor or "prodromal" stage, where autonomic, mood and sleep changes are often manifested. REM-sleep behavior disorder (RBD) is the prodromal manifestation with the highest odds for conversion into PD, thereby a valuable phenotype for disease prediction. The present review focuses on microRNAs' role in the pathogenesis of PD and RBD, summarizing the state-of-the-art of these RNA molecules as noninvasive biomarkers for non-motor prodromal (RBD) and early PD.

Extracellular Vesicles in Alzheimer's and Parkinson's Disease: Small Entities with Large Consequences

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are incurable, devastating neurodegenerative disorders characterized by the formation and spreading of protein aggregates throughout the brain. Although the exact spreading mechanism is not completely understood, extracellular vesicles (EVs) have been proposed as potential contributors. Indeed, EVs have emerged as potential carriers of disease-associated proteins and are therefore thought to play an important role in disease progression, although some beneficial functions have also been attributed to them. EVs can be isolated from a variety of sources, including biofluids, and the analysis of their content can provide a snapshot of ongoing pathological changes in the brain. This underlines their potential as biomarker candidates which is of specific relevance in AD and PD where symptoms only arise after considerable and irreversible neuronal damage has already occurred. In this review, we discuss the known beneficial and detrimental functions of EVs in AD and PD and we highlight their promising potential to be used as biomarkers in both diseases.

Seven Solutions for Neuroprotection in Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons in the substantia nigra and accumulation of iron and alpha-synuclein; it follows a characteristic pattern throughout the nervous system. Despite decades of successful preclinical neuroprotective studies, no drug has then shown efficacy in clinical trials. Considering this dilemma, we have reviewed and organized solutions of varying importance that can be exclusive or additive, and we outline approaches to help generate successful development of neuroprotective drugs for PD: (1) select patients in which the targeted mechanism is involved in the pathological process associated with the monitoring of target engagement, (2) combine treatments that target multiple pathways, (3) establish earliest interventions and develop better prodromal biomarkers, (4) adopt rigorous methodology and specific disease-relevant designs for disease-modifying clinical trials, (5) customize drug with better brain biodistribution, (6) prioritize repurposed drugs as a first line approach, and (7) adapt preclinical models to the targeted mechanisms with translational biomarkers to increase their predictive value. © 2020 International Parkinson and Movement Disorder Society.

Pharmacological Treatment of Early Motor Manifestations of Parkinson Disease (PD)

Parkinson disease (PD), as a slowly progressive neurodegenerative disorder, undergoes six neuropathological stages. The earliest clinical manifestation presents in the middle stage of the disorder pathologically, when 50% or more of the dopaminergic neurons have degenerated in the substantia nigra. This discrepancy between the early stage clinically and that pathologically has, in part, spurred the debate as to when it is best to initiate symptomatic therapy. The most well-studied monotherapeutic agents for PD in its early course include levodopa (the cornerstone of PD therapy), dopamine agonists, and monoamine oxidase inhibitors (MAOIs). With several options for initiating pharmacologic therapy, along with the heterogenous presentation of the disorder, an individualized approach is warranted. Careful deliberation must be done to optimize risk reduction while providing effective symptom control, taking the chronological age, comorbidities, social and financial disposition, work status, and both immediate- and long-term goals into consideration. Generally, treatment can be delayed in patients with mild symptoms and minimal functional impairment at any age. If treatment must be initiated, dopamine agonists and monoamine oxidase type B inhibitors can be used, especially in younger patients with milder disease. However, for older patients, those with moderate to severe PD symptoms, regardless of age, or for patients with greater comorbidities, levodopa generally remains the better choice. Eventually, regardless of initial therapy, studies have shown that most will eventually require levodopa therapy when symptoms become more disabling.

Classification of α-synuclein-induced changes in the AAV α-synuclein rat model of Parkinson's disease using electrophysiological measurements of visual processing

Biomarkers suitable for early diagnosis and monitoring disease progression are the cornerstone of developing disease-modifying treatments for neurodegenerative diseases such as Parkinson's disease (PD). Besides motor complications, PD is also characterized by deficits in visual processing. Here, we investigate how virally-mediated overexpression of α-synuclein in the substantia nigra pars compacta impacts visual processing in a well-established rodent model of PD. After a unilateral injection of vector, human α-synuclein was detected in the striatum and superior colliculus (SC). In parallel, there was a significant delay in the latency of the transient VEPs from the affected side of the SC in late stages of the disease. Inhibition of leucine-rich repeat kinase using PFE360 failed to rescue the VEP delay and instead increased the latency of the VEP waveform. A support vector machine classifier accurately classified rats according to their `disease state' using frequency-domain data from steady-state visual evoked potentials (SSVEP). Overall, these findings indicate that the latency of the rodent VEP is sensitive to changes mediated by the increased expression of α-synuclein and especially when full overexpression is obtained, whereas the SSVEP facilitated detection of α-synuclein across reflects all stages of PD model progression.

Perspective: Current Pitfalls in the Search for Future Treatments and Prevention of Parkinson's Disease

We are gradually becoming aware that there is more to Parkinson's disease (PD) than meets the eye. Accumulating evidence has unveiled a disease complexity that has not (yet) been incorporated into ongoing efforts aimed at slowing, halting or reversing the course of PD, likely underlying their lack of success. There is a substantial latency between the actual onset of PD pathology and our ability to confirm diagnosis, during which accumulating structural and functional damage might be too advanced for effective modification or protection. Identification at the earliest stages of the disease course in the absence of Parkinsonism is crucial if we are to intervene when it matters most. Prognostic and therapeutic inferences can only be successful if we are able to accurately predict who is at risk for developing PD and if we can differentiate amongst the considerable clinicopathologic diversity. Biomarkers can greatly improve our identification and differentiation abilities if we are able to disentangle cause and effect.

Subvoxel vascular imaging of the midbrain using USPIO-Enhanced MRI

There is an urgent need for better detection and understanding of vascular abnormalities at the micro-level, where critical vascular nourishment and cellular metabolic changes occur. This is especially the case for structures such as the midbrain where both the feeding and draining vessels are quite small. Being able to monitor and diagnose vascular changes earlier will aid in better understanding the etiology of the disease and in the development of therapeutics. In this work, thirteen healthy volunteers were scanned with a dual echo susceptibility weighted imaging (SWI) sequence, with a resolution of 0.22 ​× ​0.44 ​× ​1 ​mm³ at 3T. Ultra-small superparamagnetic iron oxides (USPIO) were used to induce an increase in susceptibility in both arteries and veins. Although the increased vascular susceptibility enhances the visibility of small subvoxel vessels, the accompanying strong signal loss of the large vessels deteriorates the local tissue contrast. To overcome this problem, the SWI data were acquired at different time points during a gradual administration (final concentration ​= ​4 ​mg/kg) of the USPIO agent, Ferumoxytol, and the data was processed to combine the SWI data dynamically, in order to see through these blooming artifacts. The major vessels and their tributaries (such as the collicular artery, peduncular artery, peduncular vein and the lateral mesencephalic vein) were identified on the combined SWI data using arterio-venous maps. Dynamically combined SWI data was then compared with previous histological work to validate that this protocol was able to detect small vessels on the order of 50 ​μm-100 ​μm. A complex division-based phase unwrapping was also employed to improve the quality of quantitative susceptibility maps by reducing the artifacts due to aliased voxels at the vessel boundaries. The smallest detectable vessel size was then evaluated by revisiting numerical simulations, using estimated true susceptibilities for the basal vein and the posterior cerebral artery in the presence of Ferumoxytol. These simulations suggest that vessels as small as 50 ​μm should be visible with the maximum dose of 4 ​mg/kg.