Importance of low diagnostic Accuracy for early Parkinson's disease

Serum Oligomeric α-Synuclein and p-tau181 in Progressive Supranuclear Palsy and Parkinson's Disease

Clinical differentiation of progressive supranuclear palsy (PSP) from Parkinson's disease (PD) is challenging due to overlapping phenotypes and the late onset of specific atypical signs. Therefore, easily assessable diagnostic biomarkers are highly needed. Since PD is a synucleopathy while PSP is a tauopathy, here, we investigated the clinical usefulness of serum oligomeric-α-synuclein (o-α-synuclein) and 181Thr-phosphorylated tau (p-tau181), which are considered as the most important pathological protein forms in distinguishing between these two parkinsonisms. We assessed serum o-α-synuclein and p-tau181 by ELISA and SIMOA, respectively, in 27 PSP patients, 43 PD patients, and 39 healthy controls (HC). Moreover, we evaluated the correlation between serum biomarkers and biological and clinical features of these subjects. We did not find any difference in serum concentrations of p-tau181 and o-α-synuclein nor in the o-α-synuclein/p-tau181 ratio between groups. However, we observed that serum p-tau181 positively correlated with age in HC and PD, while serum o-α-synuclein correlated positively with disease severity in PD and negatively with age in PSP. Finally, the o-α-synuclein/p-tau181 ratio showed a negative correlation with age in PD.

Potential Exosome Biomarkers for Parkinson's Disease Diagnosis: A Systematic Review and Meta-Analysis

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Given its prevalence, reliable biomarkers for early diagnosis are required. Exosomal proteins within extracellular nanovesicles are promising candidates for diagnostic, screening, prognostic, and disease monitoring purposes in neurological diseases such as PD. This review aims to evaluate the potential of extracellular vesicle proteins or miRNAs as biomarkers for PD. A comprehensive literature search until January 2024 was conducted across multiple databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, to identify relevant studies reporting exosome biomarkers in blood samples from PD patients. Out of 417 articles screened, 47 studies were selected for analysis. Among exosomal protein biomarkers, α-synuclein, tau, Amyloid β 1-42, and C-X-C motif chemokine ligand 12 (CXCL12) were identified as significant markers for PD. Concerning miRNA biomarkers, miRNA-24, miR-23b-3p, miR-195-3p, miR-29c, and mir-331-5p are promising across studies. α-synuclein exhibited increased levels in PD patients compared to control groups in twenty-one studies, while a decrease was observed in three studies. Our meta-analysis revealed a significant difference in total exosomal α-synuclein levels between PD patients and healthy controls (standardized mean difference [SMD] = 1.369, 95% confidence interval [CI] = 0.893 to 1.846, p < 0.001), although these results are limited by data availability. Furthermore, α-synuclein levels significantly differ between PD patients and healthy controls (SMD = 1.471, 95% CI = 0.941 to 2.002, p < 0.001). In conclusion, certain exosomal proteins and multiple miRNAs could serve as potential biomarkers for diagnosis, prognosis prediction, and assessment of disease progression in PD.

Identification and validation of N-acetylputrescine in combination with non-canonical clinical features as a Parkinson's disease biomarker panel

Parkinson's disease is a progressive neurodegenerative disorder in which loss of dopaminergic neurons in the substantia nigra results in a clinically heterogeneous group with variable motor and non-motor symptoms with a degree of misdiagnosis. Only 3-25% of sporadic Parkinson's patients present with genetic abnormalities that could represent a risk factor, thus environmental, metabolic, and other unknown causes contribute to the pathogenesis of Parkinson's disease, which highlights the critical need for biomarkers. In the present study, we prospectively collected and analyzed plasma samples from 194 Parkinson's disease patients and 197 age-matched non-diseased controls. N-acetyl putrescine (NAP) in combination with sense of smell (B-SIT), depression/anxiety (HADS), and acting out dreams (RBD1Q) clinical measurements demonstrated combined diagnostic utility. NAP was increased by 28% in Parkinsons disease patients and exhibited an AUC of 0.72 as well as an OR of 4.79. The clinical and NAP panel demonstrated an area under the curve, AUC = 0.9 and an OR of 20.4. The assessed diagnostic panel demonstrates combinatorial utility in diagnosing Parkinson's disease, allowing for an integrated interpretation of disease pathophysiology and highlighting the use of multi-tiered panels in neurological disease diagnosis.

Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data

Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Synaptic loss and its association with symptom severity in Parkinson's disease

Parkinson's disease (PD) is the fastest growing neurodegenerative disease, but at present there is no cure, nor any disease-modifying treatments. Synaptic biomarkers from in vivo imaging have shown promise in imaging loss of synapses in PD and other neurodegenerative disorders. Here, we provide new clinical insights from a cross-sectional, high-resolution positron emission tomography (PET) study of 30 PD individuals and 30 age- and sex-matched healthy controls (HC) with the radiotracer [11C]UCB-J, which binds to synaptic vesicle glycoprotein 2A (SV2A), and is therefore, a biomarker of synaptic density in the living brain. We also examined a measure of relative brain perfusion from the early part of the same PET scan. Our results provide evidence for synaptic density loss in the substantia nigra that had been previously reported, but also extend this to other early-Braak stage regions known to be affected in PD (brainstem, caudate, olfactory cortex). Importantly, we also found a direct association between synaptic density loss in the nigra and severity of symptoms in patients. A greater extent and wider distribution of synaptic density loss in PD patients with longer illness duration suggests that [11C]UCB-J PET can be used to measure synapse loss with disease progression. We also demonstrate lower brain perfusion in PD vs. HC groups, with a greater extent of abnormalities in those with longer duration of illness, suggesting that [11C]UCB-J PET can simultaneously provide information on changes in brain perfusion. These results implicate synaptic imaging as a useful PD biomarker for future disease-modifying interventions.

A systematic review of the challenges, emerging solutions and applications, and future directions of PET/MRI in Parkinson's disease

PET/MRI is a hybrid imaging modality that boasts the simultaneous acquisition of high-resolution anatomical data and metabolic information. Having these exceptional capabilities, it is often implicated in clinical research for diagnosing and grading, as well as tracking disease progression and response to interventions. Despite this, its low level of clinical widespread use is questioned. This is especially the case with Parkinson's disease (PD), the fastest progressively disabling and neurodegenerative cause of death. To optimise the clinical applicability of PET/MRI for diagnosing, differentiating, and tracking PD progression, the emerging novel uses, and current challenges must be identified. This systematic review aimed to present the specific challenges of PET/MRI use in PD. Further, this review aimed to highlight the possible resolution of these challenges, the emerging applications and future direction of PET/MRI use in PD. EBSCOHost (indexing CINAHL Plus, PsycINFO) Ovid (Medline, EMBASE) PubMed, Web of Science, and Scopus from 2006 (the year of first integrated PET/MRI hybrid system) to 30 September 2022 were used to search for relevant primary articles. A total of 933 studies were retrieved and following the screening procedure, 18 peer-reviewed articles were included in this review. This present study is of great clinical relevance and significance, as it informs the reasoning behind hindered widespread clinical use of PET/MRI for PD. Despite this, the emerging applications of image reconstruction developed by PET/MRI research data to the use of fully automated systems show promising and desirable utility. Furthermore, many of the current challenges and limitations can be resolved by using much larger-sampled and longitudinal studies. Meanwhile, the development of new fast-binding tracers that have specific affinity to PD pathological processes is warranted.

Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging

Parkinson's disease is the world's fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson's disease and automate diagnostics would greatly improve the treatment of patients with Parkinson's disease. Current diagnostic methods are expensive and have limited availability. Considering the insidious and preclinical onset and progression of the disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical interventions. We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson's disease. We conducted a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson's disease from UK Biobank fundus imaging. Our results suggest Parkinson's disease individuals can be differentiated from age and gender-matched healthy subjects with 68% accuracy. This accuracy is maintained when predicting either prevalent or incident Parkinson's disease. Explainability and trustworthiness are enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations.

Using 18F-AV-133 VMAT2 PET Imaging to Monitor Progressive Nigrostriatal Degeneration in Parkinson Disease

BACKGROUND AND OBJECTIVES

There are limited validated biomarkers in Parkinson disease (PD) which substantially hinders the ability to monitor disease progression and consequently measure the efficacy of disease-modifying treatments. Imaging biomarkers, such as vesicular monoamine transporter type 2 (VMAT2) PET, enable enhanced diagnostic accuracy and detect early neurodegenerative changes associated with prodromal PD. This study sought to assess whether 18F-AV-133 VMAT2 PET is sensitive enough to monitor and quantify disease progression over a 2-year window.

METHODS

18F-AV-133 PET scans were performed on participants with PD and REM sleep behavior disorder (RBD) and neurologic controls (NC). All participants were scanned twice ∼26 months apart. Regional tracer retention was calculated with a primary visual cortex reference region and expressed as the standard uptake volume ratio. Regions of interest included caudate, anterior, and posterior putamen. At the time of scanning, participants underwent clinical evaluation including UPDRSMOTOR test, Sniffin' Sticks, and Hospital Anxiety and Depression Score.

RESULTS

Over the 26-month interval, a significant decline in PET signal was observed in all 3 regions in participants with PD (N = 26) compared with NC (N = 12), consistent with a decrease in VMAT2 level and ongoing neurodegeneration. Imaging trajectory calculations suggest that the neurodegeneration in PD occurs over ∼33 years [CI: 27.2-39.5], with ∼10.5 years [CI: 9.1-11.3] of degeneration in the posterior putamen before it becomes detectable on a VMAT2 PET scan, a further ∼6.5 years [CI: 1.6-12.7] until symptom onset, and a further ∼3 years [CI: 0.3-8.7] until clinical diagnosis.

DISCUSSION

Over a 2-year period, 18F-AV-133 VMAT2 PET was able to detect progression of nigrostriatal degeneration in participants with PD, and it represents a sensitive tool to identify individuals at risk of progression to PD, which are currently lacking using clinical readouts. Trajectory models propose that there is nigrostriatal degeneration occurring for 20 years before clinical diagnosis. These data demonstrate that VMAT2 PET provides a sensitive measure to monitor neurodegenerative progression of PD which has implications for PD diagnostics and subsequently clinical trial patient stratification and monitoring.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that VMAT2 PET can detect patients with Parkinson disease and quantify progression over a 2-year window.

Genetic risk factor clustering within and across neurodegenerative diseases

Overlapping symptoms and co-pathologies are common in closely related neurodegenerative diseases (NDDs). Investigating genetic risk variants across these NDDs can give further insight into disease manifestations. In this study we have leveraged genome-wide single nucleotide polymorphisms and genome-wide association study summary statistics to cluster patients based on their genetic status across identified risk variants for five NDDs (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Lewy body dementia and frontotemporal dementia). The multi-disease and disease-specific clustering results presented here provide evidence that NDDs have more overlapping genetic aetiology than previously expected and how neurodegeneration should be viewed as a spectrum of symptomology. These clustering analyses also show potential subsets of patients with these diseases that are significantly depleted for any known common genetic risk factors suggesting environmental or other factors at work. Establishing that NDDs with overlapping pathologies share genetic risk loci, future research into how these variants might have different effects on downstream protein expression, pathology and NDD manifestation in general is important for refining and treating NDDs.

Kinetic parameters of alpha-synuclein seed amplification assay correlate with cognitive impairment in patients with Lewy body disorders

The alpha-synuclein (aSyn) seed amplification assay (SAA) can identify aSyn aggregates as indicator for Lewy body pathology in biomaterials of living patients and help in diagnosing Parkinson´s disease and dementia syndromes. Our objective was to confirm that qualitative results of aSyn SAA are reproducible across laboratories and to determine whether quantitative findings correlate with patient clinical characteristics. Therefore cerebrospinal fluid samples were re-analysed by aSyn SAA in a second laboratory with four technical replicates for each sample. Kinetic parameters derived from each aggregation curve were summarized and correlated with patient characteristics. We found that qualitative findings were identical between the two laboratories for 54 of 55 patient samples. The number of positive replicates for each sample also showed good agreement between laboratories. Moreover, specific kinetic parameters of the SAA showed a strong correlation with clinical parameters, notably with cognitive performance evaluated by the Montreal Cognitive Assessment. We concluded that SAA findings are highly reproducible across laboratories following the same protocol. SAA reports not only the presence of Lewy pathology but is also associated with clinical characteristics. Thus, aSyn SAA can potentially be used for patient stratification and determining the target engagement of aSyn targeting treatments.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

Biomarkers for parkinsonian disorders in CNS-originating EVs: promise and challenges

Extracellular vesicles (EVs), including exosomes, microvesicles, and oncosomes, are nano-sized particles enclosed by a lipid bilayer. EVs are released by virtually all eukaryotic cells and have been shown to contribute to intercellular communication by transporting proteins, lipids, and nucleic acids. In the context of neurodegenerative diseases, EVs may carry toxic, misfolded forms of amyloidogenic proteins and facilitate their spread to recipient cells in the central nervous system (CNS). CNS-originating EVs can cross the blood-brain barrier into the bloodstream and may be found in other body fluids, including saliva, tears, and urine. EVs originating in the CNS represent an attractive source of biomarkers for neurodegenerative diseases, because they contain cell- and cell state-specific biological materials. In recent years, multiple papers have reported the use of this strategy for identification and quantitation of biomarkers for neurodegenerative diseases, including Parkinson's disease and atypical parkinsonian disorders. However, certain technical issues have yet to be standardized, such as the best surface markers for isolation of cell type-specific EVs and validating the cellular origin of the EVs. Here, we review recent research using CNS-originating EVs for biomarker studies, primarily in parkinsonian disorders, highlight technical challenges, and propose strategies for overcoming them.

Clinical Diagnostic Accuracy of Parkinson's Disease: Where Do We Stand?

BACKGROUND

Clinical diagnostic accuracy of Parkinson's disease (PD) remains suboptimal. Changes in disease concept may have improved clinical diagnostic accuracy in the past decade. However, current clinical diagnostic criteria have not been validated against neuropathological confirmation.

OBJECTIVES

This study aims to provide up-to-date clinical diagnostic accuracy data and validate current clinical diagnostic criteria for PD against neuropathology.

METHODS

A retrospective review of medical records of consecutive patients with parkinsonism from the Queen Square Brain Bank was performed between 2009 and 2019. Clinical diagnosis was documented at early (within 5 years of motor symptom onset) and final stages and categorized by movement disorder experts or regular clinicians. Movement Disorder Society Parkinson's disease (MDS-PD) diagnostic criteria were retrospectively applied. Diagnostic accuracy parameters (sensitivity, specificity, positive/negative predictive value, and accuracy) were calculated using neuropathological diagnosis as the gold standard.

RESULTS

A total of 267 patients (141 PD and 126 non-PD parkinsonism) were included. Clinical diagnostic accuracy was 97.2% for experts, 92.5% for the MDS clinically probable PD criteria, and 90.3% for clinicians. Similar figures were obtained when applied at an early stage (91.5%, 89.5%, and 84.2% diagnostic accuracy, respectively). MDS clinically established early PD criteria demonstrated very high specificity (98.4%) at early stages.

CONCLUSIONS

Our results showed an important improvement in PD clinical diagnostic accuracy in clinical practice over the past decade, more marked at early stages of the disease. MDS-PD diagnostic criteria is a valid tool in clinical practice and research for the identification of PD patients showing excellent sensitivity and specificity, although movement disorder experts' diagnosis remains the gold standard PD diagnosis during life. © 2023 International Parkinson and Movement Disorder Society.

Accurate Detection of α-Synuclein Seeds in Cerebrospinal Fluid from Isolated Rapid Eye Movement Sleep Behavior Disorder and Patients with Parkinson's Disease in the DeNovo Parkinson (DeNoPa) Cohort

BACKGROUND

Misfolded α-synuclein (αSyn) aggregates (αSyn-seeds) in cerebrospinal fluid (CSF) are biomarkers for synucleinopathies such as Parkinson's disease (PD). αSyn-seeds have been detected in prodromal cases with isolated rapid eye movement sleep behavior disorder (iRBD).

OBJECTIVES

The objective of this study was to determine the accuracy of the αSyn-seed amplification assay (αS-SAA) in a comprehensively characterized cohort with a high proportion of PD and iRBD CSF samples collected at baseline.

METHODS

We used a high-throughput αS-SAA to analyze 233 blinded CSF samples from 206 participants of the DeNovo Parkinson Cohort (DeNoPa) (113 de novo PD, 64 healthy controls, 29 iRBD confirmed by video polysomnography). Results were compared with the final diagnosis, which was determined after up to 10 years of longitudinal clinical evaluations, including dopamine-transporter-single-photon emission computed tomography (DAT-SPECT) at baseline, CSF proteins, Movement Disorder Society-Unified Parkinson's Disease Rating Scale, and various cognitive and nonmotor scales.

RESULTS

αS-SAA detected αSyn-seeds in baseline PD-CSF with 98% accuracy. αSyn-seeds were detected in 93% of the iRBD cases. αS-SAA results showed higher agreement with the final than the initial diagnosis, as 14 patients were rediagnosed as non-αSyn aggregation disorder. For synucleinopathies, αS-SAA showed higher concordance with the final diagnosis than DAT-SPECT. Statistically significant correlations were found between assay parameters and disease progression.

CONCLUSIONS

Our results confirm αS-SAA accuracy at the first clinical evaluation when a definite diagnosis is most consequential. αS-SAA conditions reported here are highly sensitive, enabling the detection of αSyn-seeds in CSF from iRBD just months after the first symptoms, suggesting that αSyn-seeds are present in the very early prodromal phase of synucleinopathies. Therefore, αSyn-seeds are clear risk markers for synuclein-related disorders, but not for time of phenoconversion. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

α-Synuclein conformers reveal link to clinical heterogeneity of α-synucleinopathies

α-Synucleinopathies, such as Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy, are a class of neurodegenerative diseases exhibiting intracellular inclusions of misfolded α-synuclein (αSyn), referred to as Lewy bodies or oligodendroglial cytoplasmic inclusions (Papp-Lantos bodies). Even though the specific cellular distribution of aggregated αSyn differs in PD and DLB patients, both groups show a significant pathological overlap, raising the discussion of whether PD and DLB are the same or different diseases. Besides clinical investigation, we will focus in addition on methodologies, such as protein seeding assays (real-time quaking-induced conversion), to discriminate between different types of α-synucleinopathies. This approach relies on the seeding conversion properties of misfolded αSyn, supporting the hypothesis that different conformers of misfolded αSyn may occur in different types of α-synucleinopathies. Understanding the pathological processes influencing the disease progression and phenotype, provoked by different αSyn conformers, will be important for a personalized medical treatment in future.

Quantitative Seed Amplification Assay: A Proof-of-Principle Study

Amyloid fibrils of the protein α-synuclein (αS) have recently been identified as a biomarker for Parkinson's disease (PD). To detect the presence of these amyloid fibrils, seed amplification assays (SAAs) have been developed. SAAs allow for the detection of αS amyloid fibrils in biomatrices such as cerebral spinal fluid and are promising for PD diagnosis by providing a dichotomous (yes/no) response. The additional quantification of the number of αS amyloid fibrils may enable clinicians to evaluate and follow the disease progression and severity. Developing quantitative SAAs has been shown to be challenging. Here, we report on a proof-of-principle study on the quantification of αS fibrils in fibril-spiked model solutions of increasing compositional complexity including blood serum. We show that parameters derived from standard SAAs can be used for fibril quantification in these solutions. However, interactions between the monomeric αS reactant that is used for amplification and biomatrix components such as human serum albumin have to be taken into account. We demonstrate that quantification of fibrils is possible even down to the single fibril level in a model sample consisting of fibril-spiked diluted blood serum.

Referral criteria to palliative care for patients with Parkinson's disease: a systematic review

OBJECTIVE

This systematic review aimed to identify the referral criteria for palliative care in patients with Parkinson's disease.

METHODS

We conducted an electronic search for publications on referral criteria for palliative care in patients with Parkinson's disease in six electronic databases. The articles were thoroughly reviewed by two independent reviewers for inclusion using a predefined data extraction list. The referral criteria were thematically classified using a coding methodology.

RESULTS

This systematic review included 36 publications. We identified 14 referral criteria themes. The most common referral indicators were functional decline (n = 11 [31%]), needs assessment tools (n = 11 [31%]), physical or emotional symptoms (n = 10[28%]), need for palliative care (n = 10 [28%]), decision support (n = 9 [25%]), advanced Parkinson's disease (n = 7[19%]), and diagnosis of Parkinson's disease (n = 7 [19%]). However, there was a lack of consensus on symptom assessment tools. In addition, there were no agreed cut-offs or defined time for palliative care referral for patients with Parkinson's disease.

CONCLUSIONS

The 14 themes identified in this systematic review were categorized into disease- and needs-based criteria. These themes show the wide range of referral timing and procedures. Further studies should be conducted to reveal standardized referral criteria.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Exploring the Complex Phenotypes of Impaired Finger Dexterity in Mild-to-moderate Stage Parkinson's Disease: A Time-Series Analysis

BACKGROUND

Impaired dexterity is an early motor symptom in Parkinson's disease (PD) that significantly impacts the daily activity of patients; however, what constitutes complex dexterous movements remains controversial.

OBJECTIVE

To explore the characteristics of finger dexterity in mild-to-moderate stage PD.

METHODS

We quantitatively assessed finger dexterity in 48 mild-to-moderate stage PD patients and 49 age-matched controls using a simple alternating two-finger typing test for 15 seconds. Time-series analyses of various kinematic parameters with machine learning were compared between sides and groups.

RESULTS

Both the more and less affected hands of patients with PD had significantly lower typing frequency and slower typing velocity than the non-dominant and the dominant hands of controls (p = 0.019, p = 0.016, p < 0.001, p < 0.001). The slope of the typing velocity decreased with time, indicating a sequence effect in the PD group. A typing duration of 6 seconds was determined sufficient to discriminate PD patients from controls. Typing error, repetition, and repetition rate were significantly higher in the more affected hands of patients with PD than in the non-dominant hand of controls (p < 0.001, p = 0.03, p < 0.001). The error rate was constant, whereas the repetition rate was steep during the initiation of typing. A predictive model of the more affected hand demonstrated an accuracy of 70% in differentiating PD patients from controls.

CONCLUSION

Our study demonstrated complex components of impaired finger dexterity in mild-to-moderate stage PD, namely bradykinesia with sequence effects, error, and repetition at the initiation of movement, suggesting that multiple neural networks may be involved in dexterity deficits in PD.

Oligomeric α-synuclein and tau aggregates in NDEVs differentiate Parkinson's disease from atypical parkinsonisms

The early differential diagnosis of Parkinson's disease (PD) and atypical Parkinsonian syndromes (APS), including corticobasal degeneration (CBD) and progressive supranuclear palsy (PSP), is challenging because of an overlap of clinical features and the lack of reliable biomarkers. Neural-derived extracellular vesicles (NDEVs) isolated from blood provide a window into the brain's biochemistry and may assist in distinguishing between PD and APS. We verified in a case-control study whether oligomeric α-Synuclein and Tau aggregates isolated from NDEVs could allow the differential diagnosis of these conditions. Blood sampling and clinical data, including disease duration, motor severity, global cognition, and levodopa equivalent daily dose (LEDD), were collected from patients with a diagnosis of either PD (n = 70), PSP (n = 21), or CBD (n = 19). NDEVs were isolated from serum by immunocapture using an antibody against the neuronal surface marker L1CAM; oligomeric α-Synuclein and aggregated Tau were measured by ELISA. NDEVs analyses showed that oligomeric α-Synuclein is significantly augmented in PD compared to APS, whereas Tau aggregates are significantly increased in APS compared to PD (p < 0.0001). ROC analyses showed that these two biomarkers have a "good" power of classification (p < 0.0001 for both proteins), with high sensitivity and specificity, with NDEVs concentration of Tau aggregates and oligomeric α-Synuclein being respectively the best biomarker for PD/PSP and PD/CBD diagnostic differentiation. Logistic and multiple regression analysis confirmed that NDEVs-derived oligomeric α-Synuclein and Tau aggregates differentiate PD from CBD and PSP (p < 0.001). Notably, a positive correlation between NDEVs oligomeric α-Synuclein and disease severity (disease duration, p = 0.023; Modified H&Y, p = 0.015; UPDRS motor scores, p = 0.004) was found in PD patients and, in these same patients, NDEVs Tau aggregates concentration inversely correlated with global cognitive scores (p = 0.043). A minimally invasive blood test measuring the concentration of α-synuclein and Tau aggregates in NDEVs can represent a promising tool to distinguish with high sensitivity and specificity PD from CBD or PSP patients. Optimization and validation of these data will be needed to confirm the diagnostic value of these biomarkers in distinguishing synucleinopathies from taupathies.

Gamma-Aminobutyric Acid A Receptor Subunit Expression and Cellular Localization in the Human Parkinsonian Globus Pallidus

BACKGROUND

The gamma-aminobutyric acid A (GABA_A) receptor is an important mediator of cellular signaling in the globus pallidus and might be implicated in the pathophysiology of Parkinson disease (PD). The goal of the present study was to characterize GABA_A receptor subunit expression in the normal and parkinsonian human globus pallidus.

METHODS

Postmortem brain specimens were obtained from 8 patients with pathological evidence of PD at autopsy and from 4 control patients without such evidence. These tissues were exposed to primary antibodies directed against the α1 and α3 subunits of the GABA_A receptor and were visualized and quantified using fluorescence microscopy.

RESULTS

No differences were found in the pallidal neuronal density in the control versus PD tissues. Projection neurons strongly expressed the α1, α3, and β2 GABA_A receptor subunits. After normalizing the immunofluorescence intensities in the globus pallidus to those in the adjacent structures, no significant differences were found in GABA_A receptor subunit expression in the globus pallidus between the PD specimens and the control specimens.

CONCLUSIONS

Compensatory changes in GABA_A receptor α1 and α3 subunit expression in response to PD-related signaling abnormalities in the globus pallidus did not occur in our PD cohort.

Planning for Prevention of Parkinson Disease: Now Is the Time

Parkinson disease (PD) is a chronic progressive neurodegenerative disease with increasing worldwide prevalence. Despite many trials of neuroprotective therapies in manifest PD, no disease-modifying therapy has been established. Over the past several decades, a series of breakthroughs have identified discrete populations at substantially increased risk of developing PD. Based on this knowledge, now is the time to design and implement PD prevention trials. This endeavor builds on experience gained from early prevention trials in Alzheimer disease and Huntington disease. This article first reviews prevention trial precedents in these other neurodegenerative diseases before focusing on the critical design elements for PD prevention trials, including whom to enroll for these trials, what therapeutics to test, and how to measure outcomes in prevention trials. Our perspective reflects progress and remaining challenges that motivated a 2021 conference, "Planning for Prevention of Parkinson: A Trial Design Symposium and Workshop."

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.

Performance of αSynuclein RT-QuIC in relation to neuropathological staging of Lewy body disease

Currently, there is a need for diagnostic markers in Lewy body disorders (LBD). α-synuclein (αSyn) RT-QuIC has emerged as a promising assay to detect misfolded αSyn in clinically or neuropathologically established patients with various synucleinopathies. In this study, αSyn RT-QuIC was used to analyze lumbar CSF in a clinical cohort from the Swedish BioFINDER study and postmortem ventricular CSF in a neuropathological cohort from the Arizona Study of Aging and Neurodegenerative Disorders/Brain and Body Donation Program (AZSAND/BBDP). The BioFINDER cohort included 64 PD/PDD, 15 MSA, 15 PSP, 47 controls and two controls who later converted to PD/DLB. The neuropathological cohort included 101 cases with different brain disorders, including LBD and controls. In the BioFINDER cohort αSyn RT-QuIC identified LBD (i.e. PD, PDD and converters) vs. controls with a sensitivity of 95% and a specificity of 83%. The two controls that converted to LBD were αSyn RT-QuIC positive. Within the AZSAND/BBDP cohort, αSyn RT-QuIC identified neuropathologically verified "standard LBD" (i.e. PD, PD with AD and DLB; n = 25) vs. no LB pathology (n = 53) with high sensitivity (100%) and specificity (94%). Only 57% were αSyn RT-QuIC positive in the subgroup with "non-standard" LBD (i.e., AD with Lewy Bodies not meeting criteria for DLB or PD, and incidental LBD, n = 23). Furthermore, αSyn RT-QuIC reliably identified cases with LB pathology in the cortex (97% sensitivity) vs. cases with no LBs or LBs present only in the olfactory bulb (93% specificity). However, the sensitivity was low, only 50%, for cases with LB pathology restricted to the brainstem or amygdala, not affecting the allocortex or neocortex. In conclusion, αSyn RT-QuIC of CSF samples is highly sensitive and specific for identifying cases with clinicopathologically-defined Lewy body disorders and shows a lower sensitivity for non-standard LBD or asymptomatic LBD or in cases with modest LB pathology not affecting the cortex.

Factors associated with mortality in early stages of parkinsonism

Prognosis of patients with parkinsonism varies greatly between the various parkinsonian syndromes. However, it is often difficult to distinguish the different forms, particularly in early stages. We examined predictors of mortality and functional outcome in patients with recent-onset parkinsonism with an initially uncertain diagnosis (n = 156). Patients were recruited between 2003 and 2006, comprehensively investigated, and followed prospectively (up to 15 years, mean 7 years). A final clinical diagnosis was established after follow-up. Independent predictors of mortality were investigated with multivariable Cox regression and combined into a simple prediction model. Model performance to predict 5- and 10-year mortality and functional outcome after 3 years was evaluated and externally validated in a second cohort of 62 patients with parkinsonism with an initially uncertain diagnosis. Ninety-one patients died (58%). Orthostatic hypotension, impaired cognition, abnormal tandem gait, and elevated neurofilament light chain concentration in serum or CSF were associated with mortality. A simple model that combined these factors showed excellent performance for prediction of functional outcome after 3 years and mortality after 5 and 10 years (c-statistic ~0.90 for all models). Model performance was confirmed after external validation: prediction of functional outcome after 3 years (c-statistic 0.89, 95% CI 0.80–0.98) and mortality after 5 years (c-statistic 0.91, 95% CI 0.84–0.99) were comparable to the results in the discovery cohort. These findings help clinicians to estimate a patient’s prognosis, irrespective of the specific diagnosis.

Deep phenotyping for precision medicine in Parkinson's disease

A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.

Synuclein-One study: skin biopsy detection of phosphorylated α-synuclein for diagnosis of synucleinopathies

Finding an easily accessible and reliable tool to diagnose the diseases collectively defined as 'synucleinopathies' is an urgent, unmet priority. The synucleinopathies include Parkinson's disease, multiple system atrophy, pure autonomic failure and dementia with Lewy bodies. There are millions of people who have a diagnosis of a synucleinopathy, with more diagnosed every year. With accessibility, ease of implementation, consistently high sensitivity (>80%) and specificity approaching 100%, skin biopsy has great potential as the clinical test of choice for the diagnosis of synucleinopathies. The large, multi-center Synuclein-One study will determine the sensitivity, specificity, accuracy and precision of α-synuclein detection within punch skin biopsies in patients with clinically established synucleinopathies using standardized, robust methods suitable for large-scale analysis. Clinical Trial Registration: NCT04700722 (ClinicalTrials.gov).

Presence of Skin α-Synuclein Deposits Discriminates Parkinson's Disease from Progressive Supranuclear Palsy and Corticobasal Syndrome

BACKGROUND

Previous studies reported skin phosphorylated α-synuclein (p-syn) deposits in Parkinson's disease (PD) patients but not in patients with parkinsonism due to tauopathies, although data on the latter are limited.

OBJECTIVE

We aimed to assess the presence of skin p-syn deposits in patients with clinical diagnosis of parkinsonism usually due to tauopathy and PD.

METHODS

We consecutively recruited 26 patients, 18 fulfilling clinical diagnostic criteria of progressive supranuclear palsy (PSP) and 8 of corticobasal syndrome (CBS), 26 patients with PD, and 26 healthy controls (HC). All subjects underwent skin biopsy to study p-syn deposits in skin nerves by immunofluorescence.

RESULTS

Skin p-syn deposits were present in only two of the PSP/CBS patients and none of the HC. Conversely, all PD patients showed p-syn deposition (p < 0.001, Chi-square). The two p-syn positive patients were diagnosed with PSP and CBS, respectively. Although clinical and MRI findings supported these diagnoses, both patients had some atypical features more typical of synucleinopathies.

CONCLUSION

The detection of skin p-syn deposits may help in the differential diagnosis of parkinsonism. Indeed, in this study, all PD patients and only two out of 26 with a clinical diagnosis of PSP/CBS had skin p-syn deposits. Furthermore, these two patients showed clinical features that could suggest an atypical synucleinopathy presentation or a mixed pathology.

Autopsy Validation of the Diagnostic Accuracy of 123I-Metaiodobenzylguanidine Myocardial Scintigraphy for Lewy Body Disease

BACKGROUND AND OBJECTIVES

¹²³I-meta-iodobenzyl-guanidine (¹²³I-MIBG) myocardial scintigraphy is employed as a diagnostic imaging test to differentiate Lewy body diseases (LBDs), including Parkinson's disease and dementia with Lewy bodies, from other similar diseases. However, its diagnostic accuracy lacks validation compared with that of the gold standard. We aimed to validate the diagnostic accuracy of ¹²³I-MIBG myocardial scintigraphy for LBD against autopsy, the gold standard.

METHODS

This retrospective, cross-sectional study included consecutive autopsy patients from the Brain Bank for Aging Research who had undergone ¹²³I-MIBG myocardial scintigraphy. We compared the ¹²³I-MIBG myocardial scintigraphy findings with autopsy findings. Furthermore, the proportion of residual tyrosine hydroxylase (TH)-immunoreactive sympathetic fibers in the anterior wall of the left ventricle was investigated to assess the condition of the cardiac sympathetic nerves assumed to cause reduced ¹²³I-MIBG uptake in LBDs.

RESULTS

We analyzed the data of 56 patients (30 with pathologically confirmed LBDs and 26 without LBD pathology). Compared with the neuropathological diagnosis, the early heart-to-mediastinum (H/M) ratio had a sensitivity and specificity of 70.0% (95% confidence interval [CI]: 50.6-85.3%) and 96.2% (95% CI: 80.4-99.9%), respectively. The delayed H/M ratio had a sensitivity and specificity of 80.0% (95% CI: 61.4-92.3%) and 92.3% (95% CI: 74.9-99.1%), respectively. The washout rate had a sensitivity and specificity of 80.0% (95% CI: 61.4-92.3%) and 84.6% (95% CI: 65.1-95.6%), respectively. The proportion of residual TH-immunoreactive cardiac sympathetic fibers strongly correlated with the amount of cardiac ¹²³I-MIBG uptake when assessed with early and delayed H/M ratio values (correlation coefficient: 0.75 and 0.81, respectively; p < 0.001).

DISCUSSION

This clinicopathological validation study revealed that ¹²³I-MIBG myocardial scintigraphy could robustly differentiate LBDs from similar diseases. Abnormal ¹²³I-MIBG myocardial scintigraphy findings strongly support the presence of LBD and cardiac sympathetic denervation. However, LBD pathology should not necessarily be excluded by normal myocardial scintigraphy results, especially when other biomarkers suggest the presence of comorbid Alzheimer's disease pathology.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that ¹²³I-MIBG myocardial scintigraphy accurately identifies patients with LBD.

A Critical Analysis of Intestinal Enteric Neuron Loss and Constipation in Parkinson's Disease

Constipation afflicts many patients with Parkinson's disease (PD) and significantly impacts on patient quality of life. PD-related constipation is caused by intestinal dysfunction, but the etiology of this dysfunction in patients is unknown. One possible cause is neuron loss within the enteric nervous system (ENS) of the intestine. This review aims to 1) Critically evaluate the evidence for and against intestinal enteric neuron loss in PD patients, 2) Justify why PD-related constipation must be objectively measured, 3) Explore the potential link between loss of enteric neurons in the intestine and constipation in PD, 4) Provide potential explanations for disparities in the literature, and 5) Outline data and study design considerations to improve future research. Before the connection between intestinal enteric neuron loss and PD-related constipation can be confidently described, future research must use sufficiently large samples representative of the patient population (majority diagnosed with idiopathic PD for at least 5 years), implement a consistent neuronal quantification method and study design, including standardized patient recruitment criteria, objectively quantify intestinal dysfunctions, publish with a high degree of data transparency and account for potential PD heterogeneity. Further investigation into other potential influencers of PD-related constipation is also required, including changes in the function, connectivity, mitochondria and/or α-synuclein proteins of enteric neurons and their extrinsic innervation. The connection between enteric neuron loss and other PD-related gastrointestinal (GI) issues, including gastroparesis and dysphagia, as well as changes in nutrient absorption and the microbiome, should be explored in future research.

Clinical Diagnostic Accuracy of Early/Advanced Parkinson Disease: An Updated Clinicopathologic Study

Objective

To update data for diagnostic accuracy of a clinical diagnosis of Parkinson disease (PD) using neuropathologic diagnosis as the gold standard.

Methods

Data from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) were used to determine the predictive value of a clinical PD diagnosis. Two clinical diagnostic confidence levels were used, possible PD (PossPD, never treated or not responsive) and probable PD (ProbPD, 2/3 cardinal clinical signs and responsive to dopaminergic medications). Neuropathologic diagnosis was the gold standard.

Results

Based on the first visit to AZSAND, 15/54 (27.8%) PossPD participants and 138/163 (84.7%) ProbPD participants had confirmed PD. PD was confirmed in 24/34 (70.6%) ProbPD with <5 years and 114/128 (89.1%) with ≥5 years disease duration. Using the consensus final clinical diagnosis following death, 161/187 (86.1%) ProbPD had neuropathologically confirmed PD. Diagnostic accuracy for ProbPD improved if included motor fluctuations, dyskinesias, and hyposmia, and hyposmia for PossPD.

Conclusions

This updated study confirmed lower clinical diagnostic accuracy for elderly, untreated or poorly responsive PossPD participants and for ProbPD with <5 years of disease duration, even when medication responsive. Caution continues to be needed when interpreting clinical studies of PD, especially studies of early disease, that do not have autopsy confirmation.

Classification of Evidence

This study provides Class II evidence that a clinical diagnosis of ProbPD at the first visit identifies participants who will have pathologically confirmed PD with a sensitivity of 82.6% and a specificity of 86.0%.

α-Synuclein in blood exosomes immunoprecipitated using neuronal and oligodendroglial markers distinguishes Parkinson's disease from multiple system atrophy

The diagnosis of Parkinson’s disease (PD) and atypical parkinsonian syndromes is difficult due to the lack of reliable, easily accessible biomarkers. Multiple system atrophy (MSA) is a synucleinopathy whose symptoms often overlap with PD. Exosomes isolated from blood by immunoprecipitation using CNS markers provide a window into the brain’s biochemistry and may assist in distinguishing between PD and MSA. Thus, we asked whether α-synuclein (α-syn) in such exosomes could distinguish among healthy individuals, patients with PD, and patients with MSA. We isolated exosomes from the serum or plasma of these three groups by immunoprecipitation using neuronal and oligodendroglial markers in two independent cohorts and measured α-syn in these exosomes using an electrochemiluminescence ELISA. In both cohorts, α-syn concentrations were significantly lower in the control group and significantly higher in the MSA group compared to the PD group. The ratio between α-syn concentrations in putative oligodendroglial exosomes compared to putative neuronal exosomes was a particularly sensitive biomarker for distinguishing between PD and MSA. Combining this ratio with the α-syn concentration itself and the total exosome concentration, a multinomial logistic model trained on the discovery cohort separated PD from MSA with an AUC = 0.902, corresponding to 89.8% sensitivity and 86.0% specificity when applied to the independent validation cohort. The data demonstrate that a minimally invasive blood test measuring α-syn in blood exosomes immunoprecipitated using CNS markers can distinguish between patients with PD and patients with MSA with high sensitivity and specificity. Future optimization and validation of the data by other groups would allow this strategy to become a viable diagnostic test for synucleinopathies.

GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.

Vagus Nerve and Stomach Synucleinopathy in Parkinson's Disease, Incidental Lewy Body Disease, and Normal Elderly Subjects: Evidence Against the "Body-First" Hypothesis

BACKGROUND

Braak and others have proposed that Lewy-type α-synucleinopathy in Parkinson's disease (PD) may arise from an exogenous pathogen that passes across the gastric mucosa and then is retrogradely transported up the vagus nerve to the medulla.

OBJECTIVE

We tested this hypothesis by immunohistochemically staining, with a method specific for p-serine 129 α-synuclein (pSyn), stomach and vagus nerve tissue from an autopsy series of 111 normal elderly subjects, 33 with incidental Lewy body disease (ILBD) and 53 with PD.

METHODS

Vagus nerve samples were taken adjacent to the carotid artery in the neck. Stomach samples were taken from the gastric body, midway along the greater curvature. Formalin-fixed paraffin-embedded sections were immunohistochemically stained for pSyn, shown to be highly specific and sensitive for α-synuclein pathology.

RESULTS

Median disease duration for the PD group was 13 years. In the vagus nerve none of the 111 normal subjects had pSyn in the vagus, while 12/26 ILBD (46%) and 32/36 PD (89%) subjects were pSyn-positive. In the stomach none of the 102 normal subjects had pSyn while 5/30 (17%) ILBD and 42/52 (81%) of PD subjects were pSyn-positive.

CONCLUSION

As there was no pSyn in the vagus nerve or stomach of subjects without brain pSyn, these results support initiation of pSyn in the brain. The presence of pSyn in the vagus nerve and stomach of a subset of ILBD cases indicates that synucleinopathy within the peripheral nervous system may occur, within a subset of individuals, at preclinical stages of Lewy body disease.

Parkinson's disease

Parkinson's disease is a recognisable clinical syndrome with a range of causes and clinical presentations. Parkinson's disease represents a fast-growing neurodegenerative condition; the rising prevalence worldwide resembles the many characteristics typically observed during a pandemic, except for an infectious cause. In most populations, 3-5% of Parkinson's disease is explained by genetic causes linked to known Parkinson's disease genes, thus representing monogenic Parkinson's disease, whereas 90 genetic risk variants collectively explain 16-36% of the heritable risk of non-monogenic Parkinson's disease. Additional causal associations include having a relative with Parkinson's disease or tremor, constipation, and being a non-smoker, each at least doubling the risk of Parkinson's disease. The diagnosis is clinically based; ancillary testing is reserved for people with an atypical presentation. Current criteria define Parkinson's disease as the presence of bradykinesia combined with either rest tremor, rigidity, or both. However, the clinical presentation is multifaceted and includes many non-motor symptoms. Prognostic counselling is guided by awareness of disease subtypes. Clinically manifest Parkinson's disease is preceded by a potentially long prodromal period. Presently, establishment of prodromal symptoms has no clinical implications other than symptom suppression, although recognition of prodromal parkinsonism will probably have consequences when disease-modifying treatments become available. Treatment goals vary from person to person, emphasising the need for personalised management. There is no reason to postpone symptomatic treatment in people developing disability due to Parkinson's disease. Levodopa is the most common medication used as first-line therapy. Optimal management should start at diagnosis and requires a multidisciplinary team approach, including a growing repertoire of non-pharmacological interventions. At present, no therapy can slow down or arrest the progression of Parkinson's disease, but informed by new insights in genetic causes and mechanisms of neuronal death, several promising strategies are being tested for disease-modifying potential. With the perspective of people with Parkinson's disease as a so-called red thread throughout this Seminar, we will show how personalised management of Parkinson's disease can be optimised.

Is Levodopa Response a Valid Indicator of Parkinson's Disease?

BACKGROUND

The clinical diagnosis of Parkinson's disease (PD) requires the presence of parkinsonism and supportive criteria that include a clear and dramatic beneficial response to dopaminergic therapy. Our aim was to test the diagnostic criterion of dopaminergic response by evaluating its association with pathologically confirmed diagnoses in a large population of parkinsonian patients.

METHODS

We reviewed clinical data maintained in an electronic medical record from all patients with autopsy data who had been seen in the Movement Disorders Center at Washington University, St. Louis, between 1996 and 2018. All patients with parkinsonism who underwent postmortem neuropathologic examination were included in this analysis.

RESULTS

There were 257 unique parkinsonian patients with autopsy-based diagnoses who had received dopaminergic therapy. Marked or moderate response to dopaminergic therapy occurred in 91.2% (166/182) of those with autopsy-confirmed PD, 52.0% (13/25) of those with autopsy-confirmed multiple systems atrophy, 44.4% (8/18) of those with autopsy-confirmed progressive supranuclear palsy, and 1 (1/8) with autopsy-confirmed corticobasal degeneration. Other diagnoses were responsible for the remaining 24 individuals, 9 of whom had a moderate response to dopaminergic therapy.

CONCLUSION

A substantial response to dopaminergic therapy is frequent but not universal in PD. An absent response does not exclude PD. In other neurodegenerative disorders associated with parkinsonism, a prominent response may also be evident, but this occurs less frequently than in PD. © 2020 International Parkinson and Movement Disorder Society.

Immunohistochemical Detection of Synuclein Pathology in Skin in Idiopathic Rapid Eye Movement Sleep Behavior Disorder and Parkinsonism

BACKGROUND

Recent studies reported abnormal alpha-synuclein deposition in biopsy-accessible sites of the peripheral nervous system in Parkinson's disease (PD). This has considerable implications for clinical diagnosis. Moreover, if deposition occurs early, it may enable tissue diagnosis of prodromal PD.

OBJECTIVE

The aim of this study was to develop and test an automated bright-field immunohistochemical assay of cutaneous pathological alpha-synuclein deposition in patients with idiopathic rapid eye movement sleep behavior disorder, PD, and atypical parkinsonism and in control subjects.

METHODS

For assay development, postmortem skin biopsies were taken from 28 patients with autopsy-confirmed Lewy body disease and 23 control subjects. Biopsies were stained for pathological alpha-synuclein in automated stainers using a novel dual-immunohistochemical assay for serine 129-phosphorylated alpha-synuclein and pan-neuronal marker protein gene product 9.5. After validation, single 3-mm punch skin biopsies were taken from the cervical 8 paravertebral area from 79 subjects (28 idiopathic rapid eye movement sleep behavior disorder, 20 PD, 10 atypical parkinsonism, and 21 control subjects). Raters blinded to clinical diagnosis assessed the biopsies.

RESULTS

The immunohistochemistry assay differentiated alpha-synuclein pathology from nonpathological-appearing alpha-synuclein using combined phosphatase and protease treatments. Among autopsy samples, 26 of 28 Lewy body samples and none of the 23 controls were positive. Among living subjects, punch biopsies were positive in 23 (82%) subjects with idiopathic rapid eye movement sleep behavior disorder, 14 (70%) subjects with PD, 2 (20%) subjects with atypical parkinsonism, and none (0%) of the control subjects. After a 3-year follow-up, eight idiopathic rapid eye movement sleep behavior disorder subjects phenoconverted to defined neurodegenerative syndromes, in accordance with baseline biopsy results.

CONCLUSION

Even with a single 3-mm punch biopsy, there is considerable promise for using pathological alpha-synuclein deposition in skin to diagnose both clinical and prodromal PD. © 2020 International Parkinson and Movement Disorder Society.

Genetic and Environmental Factors Influence the Pleomorphy of LRRK2 Parkinsonism

Missense mutations in the LRRK2 gene were first identified as a pathogenic cause of Parkinson's disease (PD) in 2004. Soon thereafter, a founder mutation in LRRK2, p.G2019S (rs34637584), was described, and it is now estimated that there are approximately 100,000 people worldwide carrying this risk variant. While the clinical presentation of LRRK2 parkinsonism has been largely indistinguishable from sporadic PD, disease penetrance and age at onset can be quite variable. In addition, its neuropathological features span a wide range from nigrostriatal loss with Lewy body pathology, lack thereof, or atypical neuropathology, including a large proportion of cases with concomitant Alzheimer's pathology, hailing LRRK2 parkinsonism as the "Rosetta stone" of parkinsonian disorders, which provides clues to an understanding of the different neuropathological trajectories. These differences may result from interactions between the LRRK2 mutant protein and other proteins or environmental factors that modify LRRK2 function and, thereby, influence pathobiology. This review explores how potential genetic and biochemical modifiers of LRRK2 function may contribute to the onset and clinical presentation of LRRK2 parkinsonism. We review which genetic modifiers of LRRK2 influence clinical symptoms, age at onset, and penetrance, what LRRK2 mutations are associated with pleomorphic LRRK2 neuropathology, and which environmental modifiers can augment LRRK2 mutant pathophysiology. Understanding how LRRK2 function is influenced and modulated by other interactors and environmental factors-either increasing toxicity or providing resilience-will inform targeted therapeutic development in the years to come. This will allow the development of disease-modifying therapies for PD- and LRRK2-related neurodegeneration.

Increased Risk of Autopsy-Proven Pneumonia with Sex, Season and Neurodegenerative Disease

There has been a markedly renewed interest in factors associated with pneumonia, a leading cause of death worldwide, due to its frequent concurrence with pandemics of influenza and Covid-19 disease. Reported predisposing factors to both bacterial pneumonia and pandemic viral lower respiratory infections are wintertime occurrence, older age, obesity, pre-existing cardiopulmonary conditions and diabetes. Also implicated are age-related neurodegenerative diseases that cause parkinsonism and dementia. We investigated the prevalence of autopsy-proven pneumonia in the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND), a longitudinal clinicopathological study, between the years 2006 and 2019 and before the beginning of the Covid-19 pandemic. Of 691 subjects dying at advanced ages (mean 83.4), pneumonia was diagnosed postmortem in 343 (49.6%). There were 185 subjects without dementia or parkinsonism while clinicopathological diagnoses for the other subjects included 319 with Alzheimer's disease dementia, 127 with idiopathic Parkinson's disease, 72 with dementia with Lewy bodies, 49 with progressive supranuclear palsy and 78 with vascular dementia. Subjects with one or more of these neurodegenerative diseases all had higher pneumonia rates, ranging between 50 and 61%, as compared to those without dementia or parkinsonism (40%). In multivariable logistic regression models, male sex and a non-summer death both had independent contributions (ORs of 1.67 and 1.53) towards the presence of pneumonia at autopsy while the absence of parkinsonism or dementia was a significant negative predictor of pneumonia (OR 0.54). Male sex, dementia and parkinsonism may also be risk factors for Covid-19 pneumonia. The apolipoprotein E4 allele, as well as obesity, chronic obstructive pulmonary disease, diabetes, hypertension, congestive heart failure, cardiomegaly and cigarette smoking history, were not significantly associated with pneumonia, in contradistinction to what has been reported for Covid-19 disease.

Alzheimer's Disease Neuropathological Comorbidities are Common in the Younger-Old

BACKGROUND

Clinicopathological studies have demonstrated that Alzheimer's disease dementia (ADD) is often accompanied by clinically undetectable comorbid neurodegenerative and cerebrovascular disease that alter the rate of cognitive decline. Aside from causing increased variability in clinical response, it is possible that the major ADD comorbidities may not respond to ADD-specific molecular therapeutics.

OBJECTIVE

As most reports have focused on comorbidity in the oldest-old, its extent in younger age groups that are more likely to be involved in clinical trials is largely unknown; our objective is to provide this information.

METHODS

We conducted a survey of neuropathological comorbidities in sporadic ADD using data from the US National Alzheimer's Coordinating Center. Subject data was restricted to those with dementia and meeting National Institute on Aging-Alzheimer's Association intermediate or high AD Neuropathological Change levels, excluding those with known autosomal dominant AD-related mutations.

RESULTS

Highly prevalent ADD comorbidities are not restricted to the oldest-old but are common even in early-onset ADD. The percentage of cases with ADD as the sole major neuropathological diagnosis is highest in the under-60 group, where "pure" ADD cases are still in the minority at 44%. After this AD as a sole major pathology in ADD declines to roughly 20%in the 70s and beyond. Lewy body disease is the most common comorbidity at younger ages but actually is less common at later ages, while for most others, their prevalence increases with age.

CONCLUSION

Alzheimer's disease neuropathological comorbidities are highly prevalent even in the younger-old.

Blinded RT-QuIC Analysis of α-Synuclein Biomarker in Skin Tissue From Parkinson's Disease Patients

BACKGROUND

An unmet clinical need in Parkinson's disease (PD) is to identify biomarkers for diagnosis, preferably in peripherally accessible tissues such as skin. Immunohistochemical studies have detected pathological α-synuclein (αSyn) in skin biopsies from PD patients albeit sensitivity needs to be improved.

OBJECTIVE

Our study provides the ultrasensitive detection of pathological αSyn present in the skin of PD patients, and thus, pathological αSyn in skin could be a potential biomarker for PD.

METHODS

The real-time quaking-induced conversion assay was used to detect pathological αSyn present in human skin tissues. Further, we optimized this ultra-sensitive and specific assay for both frozen and formalin-fixed paraffin-embedded sections of skin tissues. We determined the seeding kinetics of the αSyn present in the skin from autopsied subjects consisting of frozen skin tissues from 25 PD and 25 controls and formalin-fixed paraffin-embedded skin sections from 12 PD and 12 controls.

RESULTS

In a blinded study of skin tissues from autopsied subjects, we correctly identified 24/25 PD and 24/25 controls using frozen skin tissues (96% sensitivity and 96% specificity) compared to 9/12 PD and 10/12 controls using formalin-fixed paraffin-embedded skin sections (75% sensitivity and 83% specificity).

CONCLUSIONS

Our blinded study results clearly demonstrate the feasibility of using skin tissues for clinical diagnosis of PD by detecting pathological αSyn. Moreover, this peripheral biomarker discovery study may have broader translational value in detecting misfolded proteins in skin samples as a longitudinal progression marker. © 2020 International Parkinson and Movement Disorder Society.

In vivo distribution of α-synuclein in multiple tissues and biofluids in Parkinson disease

Objective

The Systemic Synuclein Sampling Study (S4) measured α-synuclein in multiple tissues and biofluids within the same patients with Parkinson disease (PD) vs healthy controls (HCs).

Methods

S4 was a 6-site cross-sectional observational study of participants with early, moderate, or advanced PD and HCs. Motor and nonmotor measures and dopamine transporter SPECT were obtained. Biopsies of skin, colon, submandibular gland (SMG), CSF, saliva, and blood were collected. Tissue biopsy sections were stained with 5C12 monoclonal antibody against pathologic α-synuclein; digital images were interpreted by neuropathologists blinded to diagnosis. Biofluid total α-synuclein was quantified using ELISA.

Results

The final cohort included 59 patients with PD and 21 HCs. CSF α-synuclein was lower in patients with PD vs HCs; sensitivity/specificity of CSF α-synuclein for PD diagnosis was 87.0%/63.2%, respectively. Sensitivity of α-synuclein immunoreactivity for PD diagnosis was 56.1% for SMG and 24.1% for skin; specificity was 92.9% and 100%, respectively. There were no significant relationships between different measures of α-synuclein within participants.

Conclusions

S4 confirms lower total α-synuclein levels in CSF in patients with PD compared to HCs, but specificity is low. In contrast, α-synuclein immunoreactivity in skin and SMG is specific for PD but sensitivity is low. Relationships within participants across different tissues and biofluids could not be demonstrated. Measures of pathologic forms of α-synuclein with higher accuracy are critically needed.

Classification of evidence

This study provides Class III evidence that total CSF α-synuclein does not accurately distinguish patients with PD from HCs, and that monoclonal antibody staining for SMG and skin total α-synuclein is specific but not sensitive for PD diagnosis.

Magnetic Resonance Imaging and Neurofilament Light in the Differentiation of Parkinsonism

OBJECTIVE

Accurate diagnosis is particularly challenging in Parkinson's disease (PD), multiple system atrophy (MSAp), and progressive supranuclear palsy (PSP). We compare the utility of 3 promising biomarkers to differentiate disease state and explain disease severity in parkinsonism: the Automated Imaging Differentiation in Parkinsonism (AID-P), the Magnetic Resonance Parkinsonism Index (MRPI), and plasma-based neurofilament light chain protein (NfL).

METHODS

For each biomarker, the area under the curve (AUC) of receiver operating characteristic curves were quantified for PD versus MSAp/PSP and MSAp versus PSP and statistically compared. Unique combinations of variables were also assessed. Furthermore, each measures association with disease severity was determined using stepwise multiple regression.

RESULTS

For PD versus MSAp/PSP, AID-P (AUC, 0.900) measures had higher AUC compared with NfL (AUC, 0.747) and MRPI (AUC, 0.669), P < 0.05. For MSAp versus PSP, AID-P (AUC, 0.889), and MRPI (AUC, 0.824) measures were greater than NfL (AUC, 0.537), P < 0.05. We then combined measures to determine if any unique combination provided enhanced accuracy and found that no combination performed better than the AID-P alone in differentiating parkinsonisms. Furthermore, we found that the AID-P demonstrated the highest association with the MDS-UPDRS (Radj2 -AID-P, 26.58%; NfL,15.12%; MRPI, 12.90%).

CONCLUSIONS

Compared with MRPI and NfL, AID-P provides the best overall differentiation of PD versus MSAp/PSP. Both AID-P and MRPI are effective in differentiating MSAp versus PSP. Furthermore, combining biomarkers did not improve classification of disease state compared with using AID-P alone. The findings demonstrate in the current sample that the AID-P and MRPI are robust biomarkers for PD, MSAp, and PSP. © 2020 International Parkinson and Movement Disorder Society.