Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study

Association analyses of apolipoprotein E genotypes and cognitive performance in patients with Parkinson's disease

BACKGROUND

Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD). The apolipoprotein E (APOE) ε4 genotype increases the risk of Alzheimer's disease (AD). However, the effect of APOEε4 on cognitive function of PD patients remains unclear. In this study, we aimed to understand whether and how carrying APOEε4 affects cognitive performance in patients with early-stage and advanced PD.

METHODS

A total of 119 Chinese early-stage PD patients were recruited. Movement Disorder Society Unified Parkinson's Disease Rating Scale, Hamilton anxiety scale, Hamilton depression scale, non-motor symptoms scale, Mini-mental State Examination, Montreal Cognitive Assessment, and Fazekas scale were evaluated. APOE genotypes were determined by polymerase chain reactions and direct sequencing. Demographic and clinical information of 521 early-stage and 262 advanced PD patients were obtained from Parkinson's Progression Marker Initiative (PPMI).

RESULTS

No significant difference in cognitive performance was found between ApoEε4 carriers and non-carriers in early-stage PD patients from our cohort and PPMI. The cerebrospinal fluid (CSF) Amyloid Beta 42 (Aβ42) level was significantly lower in ApoEε4 carrier than non-carriers in early-stage PD patients from PPMI. In advanced PD patients from PPMI, the BJLOT, HVLT retention and SDMT scores seem to be lower in ApoEε4 carriers without reach the statistical significance.

CONCLUSIONS

APOEε4 carriage does not affect the cognitive performance of early-stage PD patients. However, it may promote the decline of CSF Aβ42 level and the associated amyloidopathy, which is likely to further contribute to the cognitive dysfunction of PD patients in the advanced stage.

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.

Lysosomal and synaptic dysfunction markers in longitudinal cerebrospinal fluid of de novo Parkinson's disease

Lysosomal and synaptic dysfunctions are hallmarks in neurodegeneration and potentially relevant as biomarkers, but data on early Parkinson's disease (PD) is lacking. We performed targeted mass spectrometry with an established protein panel, assessing autophagy and synaptic function in cerebrospinal fluid (CSF) of drug-naïve de novo PD, and sex-/age-matched healthy controls (HC) cross-sectionally (88 PD, 46 HC) and longitudinally (104 PD, 58 HC) over 10 years. Multiple markers of autophagy, synaptic plasticity, and secretory pathways were reduced in PD. We added samples from prodromal subjects (9 cross-sectional, 12 longitudinal) with isolated REM sleep behavior disorder, revealing secretogranin-2 already decreased compared to controls. Machine learning identified neuronal pentraxin receptor and neurosecretory protein VGF as most relevant for discriminating between groups. CSF levels of LAMP2, neuronal pentraxins, and syntaxins in PD correlated with clinical progression, showing predictive potential for motor- and non-motor symptoms as a valid basis for future drug trials.

Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset

Introduction

Parkinson's disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5.

Method

We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets.

Results

Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75.

Conclusion

The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.

Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty

BACKGROUND

International clinical criteria are the reference for the diagnosis of degenerative parkinsonism in clinical research, but they may lack sensitivity and specificity in the early stages.

OBJECTIVES

To determine whether magnetic resonance imaging (MRI) analysis, through visual reading or machine-learning approaches, improves diagnostic accuracy compared with clinical diagnosis at an early stage in patients referred for suspected degenerative parkinsonism.

MATERIALS

Patients with initial diagnostic uncertainty between Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA), with brain MRI performed at the initial visit (V1) and available 2-year follow-up (V2), were included. We evaluated the accuracy of the diagnosis established based on: (1) the international clinical diagnostic criteria for PD, PSP, and MSA at V1 ("Clin1"); (2) MRI visual reading blinded to the clinical diagnosis ("MRI"); (3) both MRI visual reading and clinical criteria at V1 ("MRI and Clin1"), and (4) a machine-learning algorithm ("Algorithm"). The gold standard diagnosis was established by expert consensus after a 2-year follow-up.

RESULTS

We recruited 113 patients (53 with PD, 31 with PSP, and 29 with MSA). Considering the whole population, compared with clinical criteria at the initial visit ("Clin1": balanced accuracy, 66.2%), MRI visual reading showed a diagnostic gain of 14.3% ("MRI": 80.5%; P = 0.01), increasing to 19.2% when combined with the clinical diagnosis at the initial visit ("MRI and Clin1": 85.4%; P < 0.0001). The algorithm achieved a diagnostic gain of 9.9% ("Algorithm": 76.1%; P = 0.08).

CONCLUSION

Our study shows the use of MRI analysis, whether by visual reading or machine-learning methods, for early differentiation of parkinsonism. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Evaluating the Diagnostic Potential of Combined Salivary and Skin Biomarkers in Parkinson's Disease

Oligomeric alpha-synuclein (α-syn) in saliva and phosphorylated α-syn deposits in the skin have emerged as promising diagnostic biomarkers for Parkinson's disease (PD). This study aimed to assess and compare the diagnostic value of these biomarkers in discriminating between 38 PD patients and 24 healthy subjects (HSs) using easily accessible biological samples. Additionally, the study sought to determine the diagnostic potential of combining these biomarkers and to explore their correlations with clinical features. Salivary oligomeric α-syn levels were quantified using competitive ELISA, while skin biopsies were analyzed through immunofluorescence to detect phosphorylated α-syn at Ser129 (p-S129). Both biomarkers individually were accurate in discriminating PD patients from HSs, with a modest agreement between them. The combined positivity of salivary α-syn oligomers and skin p-S129 aggregates differentiated PD patients from HSs with an excellent discriminative ability with an AUC of 0.9095. The modest agreement observed between salivary and skin biomarkers individually suggests that they may reflect different aspects of PD pathology, thus providing complementary information when combined. This study's results highlight the potential of utilizing a multimodal biomarker approach to enhance diagnostic accuracy in PD.

The connection of motor improvement after deep brain stimulation in Parkinson's disease and microstructural integrity of the substantia nigra and subthalamic nucleus

BACKGROUND

Nigrostriatal microstructural integrity has been suggested as a biomarker for levodopa response in Parkinson's disease (PD), which is a strong predictor for motor response to deep brain stimulation (DBS) of the subthalamic nucleus (STN). This study aimed to explore the impact of microstructural integrity of the substantia nigra (SN), STN, and putamen on motor response to STN-DBS using diffusion microstructure imaging.

METHODS

Data was collected from 23 PD patients (mean age 63 ± 7, 6 females) who underwent STN-DBS, had preoperative 3 T diffusion magnetic resonance imaging including multishell diffusion-weighted MRI with b-values of 1000 and 2000 s/mm2 and records of motor improvement available.

RESULTS

The association between a poorer DBS-response and increased free interstitial fluid showed notable effect sizes (rho > |0.4|) in SN and STN, but not in putamen. However, this did not reach significance after Bonferroni correction and controlling for sex and age.

CONCLUSION

Microstructural integrity of SN and STN are potential biomarkers for the prediction of therapy efficacy following STN-DBS, but further studies are required to confirm these associations.

Conjugal Synucleinopathies: A Clinicopathologic Study

BACKGROUND

While preclinical studies have shown that alpha-synuclein can spread through cell-to-cell transmission whether it can be transmitted between humans is unknown.

OBJECTIVES

The aim was to assess the presence of a synucleinopathy in autopsied conjugal couples.

METHODS

Neuropathological findings in conjugal couples were categorized as Parkinson's disease (PD), dementia with Lewy bodies (DLB), Alzheimer's disease with Lewy bodies (ADLB), incidental Lewy body disease (ILBD), or no Lewy bodies.

RESULTS

Ninety conjugal couples were included; the mean age of death was 88.3 years; 32 couples had no Lewy bodies; 42 couples had 1 spouse with a synucleinopathy: 10 PD, 3 DLB, 13 ADLB, and 16 ILBD; 16 couples had both spouses with a synucleinopathy: in 4 couples both spouses had PD, 1 couple had PD and DLB, 4 couples had PD and ADLB, 2 couples had PD and ILBD, 1 couple had DLB and ADLB, in 3 couples both had ADLB, and 1 couple had ADLB and ILBD. No couples had both spouses with ILBD.

CONCLUSIONS

This large series of 90 autopsied conjugal couples found 16 conjugal couples with synucleinopathies, suggesting transmission of synucleinopathy between spouses is unlikely. © 2024 International Parkinson and Movement Disorder Society.

Symmetry of synuclein density in autopsied Parkinson's disease submandibular glands

BACKGROUND

Peripheral tissue biopsy in Parkinson's disease (PD) may be valuable for clinical care, biomarker validation, and as research enrollment criteria.

OBJECTIVE

Determine whether submandibular gland pathologic alpha-synuclein (aSyn) density is symmetrical and whether previous needle biopsy caused tissue damage.

METHODS

Thirty autopsy-confirmed PD cases having fixed submandibular gland tissue from one side and frozen submandibular gland tissue from the contralateral side were studied. Tissue was stained for phosphorylated aSyn and density (0-4 semiquantitative scale) was determined. Three previously biopsied cases were also assessed for tissue damage at subsequent autopsy.

RESULTS

Mean (SD) age was 80.9 (5.5) years and disease duration 12.5 (9.3). Submandibular gland aSyn staining had a mean score of 2.13 for both the initially fixed and the initially frozen submandibular glands. The correlation between aSyn density of the two sides was r = 0.63. Correlation of aSyn density, in the originally fixed submandibular gland, with disease duration was good (r = 0.49, p =.006). No permanent tissue damage was found in the three previously biopsied cases.

CONCLUSIONS

This study found good correlation between aSyn density in both submandibular glands of patients with PD and found no evidence of significant tissue damage in previously biopsied subjects.

The association between plasma GPNMB and Parkinson's disease and multiple system atrophy

AIMS

Parkinson's disease (PD), as the second most common neurodegenerative disorder, often presents diagnostic challenges in differentiation from other forms of Parkinsonism. Recent studies have reported an association between plasma glycoprotein nonmetastatic melanoma protein B (pGPNMB) and PD.

METHODS

A retrospective study was conducted, comprising 401 PD patients, 111 multiple system atrophy (MSA) patients, 13 progressive supranuclear palsy (PSP) patients and 461 healthy controls from the Chinese Han population, with an assessment of pGPNMB levels.

RESULTS

The study revealed that pGPNMB concentrations were significantly lower in PD and MSA patients compared to controls (area under the receiver operating characteristics curve (AUC) 0.62 and 0.74, respectively, P < 0.0001 for both), but no difference was found in PSP patients compared to controls (P > 0.05). Interestingly, the level of pGPNMB was significantly higher in PD patients than in MSA patients (AUC = 0.63, P < 0.0001). Furthermore, the study explored the association between pGPNMB levels and disease severity in PD and MSA patients, revealing a positive correlation in PD patients but not in MSA patients with both disease severity and cognitive impairment.

CONCLUSION

This study successfully replicated prior findings, demonstrating an association between pGPNMB levels and disease severity, and also identified a correlation with cognitive impairment in PD patients of the Chinese Han population. Additionally, this study is the first to identify a significant difference in pGPNMB levels between MSA, PD, and normal controls. The data provide new evidence supporting the potential role of pGPNMB in the diagnosis and differential diagnosis of Parkinsonism.

Pathological α-synuclein detected by real-time quaking-induced conversion in synucleinopathies

synucleinopathies are diseases characterized by the aggregation of α-synuclein (α-syn), which forms fibrils through misfolding and accumulates in a prion-like manner. To detect the presence of these α-syn aggregates in clinical samples, seed amplification assays (SAAs) have been developed. These SAAs are capable of amplifying the α-syn seeds, allowing for their detection. αSyn-SAAs have been reported under the names 'protein misfolding cyclic amplification' (αSyn-PMCA) and 'real-time quaking-induced conversion'α-Syn-RT-QuIC. The α-Syn RT-QuIC, in particular, has been adapted to amplify and detect α-syn aggregates in various biospecimens, including cerebrospinal fluid (CSF), skin, nasal brushing, serum and saliva. The α-syn RT-QuIC assay has demonstrated good sensitivity and specificity in detecting pathological α-syn, particularly in Parkinson's disease (PD) and dementia with Lewy bodies (DLB) cases, with an accuracy rate of up to 80 %. Additionally, differential diagnosis between DLB and PD, as well as PD and multiple system atrophy (MSA), can be achieved by utilizing certain kinetic thioflavin T (ThT) parameters and other parameters. Moreover, the positive detection of α-syn in the prodromal stage of synucleinopathies provides an opportunity for early intervention and management. In summary, the development of the α-syn RT-QuIC assay has greatly contributed to the field of synucleinopathies. Therefore, we review the development of α-syn RT-QuIC assay and describe in detail the recent advancements of α-syn RT-QuIC assay for detecting pathological α-syn in synucleinopathies.

Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level

Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [18F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.

Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics

OBJECTIVES

To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics.

METHODS

One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models.

RESULTS

The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model.

CONCLUSIONS

Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics.

CLINICAL RELEVANCE STATEMENT

This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance.

KEY POINTS

•Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.

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.

Perspectives of People At-Risk on Parkinson's Prevention Research

 The movement toward prevention trials in people at-risk for Parkinson's disease (PD) is rapidly becoming a reality. The authors of this article include a genetically at-risk advocate with the LRRK2 G2019 S variant and two patients with rapid eye movement sleep behavior disorder (RBD), one of whom has now been diagnosed with PD. These authors participated as speakers, panelists, and moderators in the "Planning for Prevention of Parkinson's: A Trial Design Forum" hosted by Massachusetts General Hospital in 2021 and 2022. Other authors include a young onset person with Parkinson's (PwP) and retired family physician, an expert in patient engagement in Parkinson's, and early career and veteran movement disorders clinician researchers. Several themes emerged from the at-risk participant voice concerning the importance of early intervention, the legitimacy of their input in decision-making, and the desire for transparent communication and feedback throughout the entire research study process. Challenges and opportunities in the current environment include lack of awareness among primary care physicians and general neurologists about PD risk, legal and psychological implications of risk disclosure, limited return of individual research study results, and undefined engagement and integration of individuals at-risk into the broader Parkinson's community. Incorporating the perspectives of individuals at-risk as well as those living with PD at this early stage of prevention trial development is crucial to success.

Altered Outer Retinal Structure, Electrophysiology and Visual Perception in Parkinson's Disease

BACKGROUND

Visual biomarkers of Parkinson's disease (PD) are attractive as the retina is an outpouching of the brain. Although inner retinal neurodegeneration in PD is well-established this has overlap with other neurodegenerative diseases and thus outer retinal (photoreceptor) measures warrant further investigation.

OBJECTIVE

To examine in a cross-sectional study whether clinically implementable measures targeting outer retinal function and structure can differentiate PD from healthy ageing and whether these are sensitive to intraday levodopa (L-DOPA) dosing.

METHODS

Centre-surround perceptual contrast suppression, macular visual field sensitivity, colour discrimination, light-adapted electroretinography and optical coherence tomography (OCT) were tested in PD participants (n = 16) and controls (n = 21). Electroretinography and OCT were conducted before and after midday L-DOPA in PD participants, or repeated after ∼2 hours in controls.

RESULTS

PD participants had decreased center-surround contrast suppression (p < 0.01), reduced macular visual field sensitivity (p < 0.05), color vision impairment (p < 0.01) photoreceptor dysfunction (a-wave, p < 0.01) and photoreceptor neurodegeneration (outer nuclear layer thinning, p < 0.05), relative to controls. Effect size comparison between inner and outer retinal parameters showed that photoreceptor metrics were similarly robust in differentiating the PD group from age-matched controls as inner retinal changes. Electroretinography and OCT were unaffected by L-DOPA treatment or time.

CONCLUSIONS

We show that outer retinal outcomes of photoreceptoral dysfunction (decreased cone function and impaired color vision) and degeneration (i.e., outer nuclear layer thinning) were equivalent to inner retinal metrics at differentiating PD from healthy age-matched adults. These findings suggest outer retinal metrics may serve as useful biomarkers for PD.

α-Synuclein seed amplification assay as a diagnostic tool for parkinsonian disorders

INTRODUCTION

Synucleinopathies such as Parkinson's disease (PD) and multiple system atrophy (MSA) can be challenging to diagnose due to the symptom overlap with, for example, atypical parkinsonisms like progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). Seed amplification assays (SAA), developed for the detection of α-synuclein (αSyn) aggregates in CSF, have been successful when used as a biomarker evaluation for synucleinopathies. In this study, we investigated the potential of this assay to not only detect αSyn seeds in CSF, but also discriminate between movement disorders.

METHODS

The αSyn-SAA was tested in a Scandinavian cohort composed of 129 CSF samples from patients with PD (n = 55), MSA (n = 27), CBD (n = 7), and PSP (n = 16), as well as healthy controls (HC, n = 24).

RESULTS

The αSyn seed amplification assay (αSyn-SAA) was able to correctly identify all PD samples as positive (sensitivity of 100%) while also discriminating the PD group from HC (70.8% specificity, p < 0.0001) and tauopathies [CBD (71% specificity) and PSP (75% specificity), p < 0.0001)]. The αSyn-SAA was also able to identify almost all MSA samples as positive for αSyn aggregation (sensitivity of 92.6%). In general, this assay is able to discriminate between the synucleinopathies and tauopathies analyzed herein (p < 0.0001) despite the overlapping symptoms in these diseases.

CONCLUSION

These findings suggest the αSyn-SAA is a useful diagnostic tool for differentiating between different parkinsonian disorders, although further optimization may be needed.

A multimodal screening system for elderly neurological diseases based on deep learning

In this paper, we propose a deep-learning-based algorithm for screening neurological diseases. We proposed various examination protocols for screening neurological diseases and collected data by video-recording persons performing these protocols. We converted video data into human landmarks that capture action information with a much smaller data dimension. We also used voice data which are also effective indicators of neurological disorders. We designed a subnetwork for each protocol to extract features from landmarks or voice and a feature aggregator that combines all the information extracted from the protocols to make a final decision. Multitask learning was applied to screen two neurological diseases. To capture meaningful information about these human landmarks and voices, we applied various pre-trained models to extract preliminary features. The spatiotemporal characteristics of landmarks are extracted using a pre-trained graph neural network, and voice features are extracted using a pre-trained time-delay neural network. These extracted high-level features are then passed onto the subnetworks and an additional feature aggregator that are simultaneously trained. We also used various data augmentation techniques to overcome the shortage of data. Using a frame-length staticizer that considers the characteristics of the data, we can capture momentary tremors without wasting information. Finally, we examine the effectiveness of different protocols and different modalities (different body parts and voice) through extensive experiments. The proposed method achieves AUC scores of 0.802 for stroke and 0.780 for Parkinson's disease, which is effective for a screening system.

Unraveling the transcriptomic signatures of Parkinson's disease and major depression using single-cell and bulk data

Background

Motor symptoms are well-characterized in Parkinson's disease (PD). However, non-motor symptoms, such as depression, are commonly observed and can appear up to 10 years before motor features, resulting in one-third of individuals being misdiagnosed with a neuropsychiatric disorder. Thus, identifying diagnostic biomarkers is crucial for accurate PD diagnosis during its prodromal or early stages.

Methods

We employed an integrative approach, combining single nucleus RNA and bulk mRNA transcriptomics to perform comparative molecular signatures analysis between PD and major depressive disorder (MDD). We examined 39,834 nuclei from PD (GSE202210) and 32,707 nuclei from MDD (GSE144136) in the dorsolateral prefrontal cortex (dlPFC) of Brodmann area 9. Additionally, we analyzed bulk mRNA peripheral blood samples from PD compared to controls (GSE49126, GSE72267), as well as MDD compared to controls (GSE39653).

Results

Our findings show a higher proportion of astrocytes, and oligodendrocyte cells in the dlPFC of individuals with PD vs. MDD. The excitatory to inhibitory neurons (E/I) ratio analysis indicates that MDD has a ratio close to normal 80/20, while PD has a ratio of 62/38, indicating increased inhibition in the dlPFC. Microglia displayed the most pronounced differences in gene expression profiles between the two conditions. In PD, microglia display a pro-inflammatory phenotype, while in MDD, they regulate synaptic transmission through oligodendrocyte-microglia crosstalk. Analysis of bulk mRNA blood samples revealed that the COL5A, MID1, ZNF148, and CD22 genes were highly expressed in PD, whereas the DENR and RNU1G2 genes were highly expressed in MDD. CD22 is involved in B-cell activation and the negative regulation of B-cell receptor signaling. Additionally, CD86, which provides co-stimulatory signals for T-cell activation and survival, was found to be a commonly differentially expressed gene in both conditions. Pathway analysis revealed several immune-related pathways common in both conditions, including the complement and coagulation cascade, and B-cell receptor signaling.

Discussion

This study demonstrates that bulk peripheral immune cells play a role in both conditions, but neuroinflammation in the dlPFC specifically manifests in PD as evidenced by the analysis of single nucleus dlPFC datasets. Integrating these two omics levels offers a better understanding of the shared and distinct molecular pathophysiology of PD and MDD in both the periphery and the brain. These findings could lead to potential diagnostic biomarkers, improving accuracy and guiding pharmacological treatments.

Automatic Segmentation and Quantification of Nigrosome-1 Neuromelanin and Iron in MRI: A Candidate Biomarker for Parkinson's Disease

BACKGROUND

There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome-1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the "swallow-tail" in iron-sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation.

PURPOSE

Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male).

FIELD STRENGTH/SEQUENCE

3 T Anatomical T1-weighted MPRAGE, NM-MRI T1-weighted gradient with magnetization transfer, susceptibility-weighted imaging (SWI).

ASSESSMENT

N1 was automatically segmented in SWI images using a multi-image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied.

STATISTICAL TESTS

Nonparametric tests were used (Mann-Whitney's U, chi-square, and Friedman tests) at P = 0.05.

RESULTS

N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM-CRHC  = 22.55 ± 1.49; NM-CRPD  = 19.79 ± 1.92; NM-nVolHC  = 2.69 × 10-5  ± 1.02 × 10-5 ; NM-nVolPD  = 1.18 × 10-5  ± 0.96 × 10-5 ; Iron-CRHC  = 10.51 ± 2.64; Iron-CRPD  = 19.35 ± 7.88; Iron-nVolHC  = 0.72 × 10-5  ± 0.81 × 10-5 ; Iron-nVolPD  = 2.82 × 10-5  ± 2.04 × 10-5 ). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM-CR  = -0.31; ρiron-CR  = 0.43; ρiron-nVol  = 0.46) and the motor status (ρNM-nVol  = -0.27; ρiron-CR  = 0.33; ρiron-nVol  = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses.

DATA CONCLUSION

This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

Cardiac 123I-Metaiodobenzylguanidine (MIBG) Scintigraphy in Parkinson's Disease: A Comprehensive Review

Cardiac sympathetic denervation, as documented on 123I-metaiodobenzylguanidine (MIBG) myocardial scintigraphy, is relatively sensitive and specific for distinguishing Parkinson's disease (PD) from other neurodegenerative causes of parkinsonism. The present study aims to comprehensively review the literature regarding the use of cardiac MIBG in PD. MIBG is an analog to norepinephrine. They share the same uptake, storage, and release mechanisms. An abnormal result in the cardiac MIBG uptake in individuals with parkinsonism can be an additional criterion for diagnosing PD. However, a normal result of cardiac MIBG in individuals with suspicious parkinsonian syndrome does not exclude the diagnosis of PD. The findings of cardiac MIBG studies contributed to elucidating the pathophysiology of PD. We investigated the sensitivity and specificity of cardiac MIBG scintigraphy in PD. A total of 54 studies with 3114 individuals diagnosed with PD were included. The data were described as means with a Hoehn and Yahr stage of 2.5 and early and delayed registration H/M ratios of 1.70 and 1.51, respectively. The mean cutoff for the early and delayed phases were 1.89 and 1.86. The sensitivity for the early and delayed phases was 0.81 and 0.83, respectively. The specificity for the early and delayed phases were 0.86 and 0.80, respectively.

An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson's Disease

Walking detection in the daily life of patients with Parkinson's disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.

Longitudinal motor decline in dementia with Lewy bodies, Parkinson disease dementia, and Alzheimer's dementia in a community autopsy cohort

INTRODUCTION

We examined the progression of extrapyramidal symptoms and signs in autopsy-confirmed dementia with Lewy bodies (DLB), Parkinson's disease dementia (PDD), and Alzheimer's disease dementia (AD).

METHODS

Longitudinal data were obtained from Arizona Study of Aging and Neurodegenerative Disease, with PDD (n = 98), AD (n = 47) and DLB (n = 48) further sub-grouped as with or without parkinsonism (DLB+ and DLB-). Within-group Unified Parkinson's Disease Rating Scale (UPDRS) -II and UPDRS-III trajectories were analyzed using non-linear mixed effects models.

RESULTS

In DLB, 65.6% had parkinsonism. Baseline UPDRS-II and III scores (off-stage) were highest (P < 0.001) for PDD (mean ± SD 14.3 ± 7.8 and 27.4 ± 16.3), followed by DLB+ (6.0 ± 8.8 and 17.2 ± 17.1), DLB- (1.1 ± 1.3 and 3.3 ± 5.5) and AD (3.2 ± 6.1 and 8.2 ± 13.6). Compared to PDD, the DLB+ group had faster UPDRS-III progression over 8-years (Cohen's-d range 0.98 to 2.79, P < 0.001), driven by gait (P < 0.001) and limb bradykinesia (P = 0.02) subscales.

DISCUSSION

Motor deficits progress faster in DLB+ than PDD, providing insights about expected changes in motor function.

HIGHLIGHTS

Dementia with Lewy bodies has faster motor progression than Parkinson's disease dementia Linear and non-linear mixed modeling analysis of longitudinal data was utilized Findings have implications for clinical prognostication and trial design.

Parkinson's disease updates: Addressing the pathophysiology, risk factors, genetics, diagnosis, along with the medical and surgical treatment

After only Alzheimer's disease (AD), Parkinson's disease (PD) is the second most prevalent neurodegenerative disease. The incidence of this disease increases with age, especially for those above 70 years old. There are many risk factors that are well-established in the contribution to the development of PD, such as age, gender, ethnicity, rapid eye movement sleep disorder, high consumption of dairy products, traumatic brain injury, genetics, and pesticides/herbicides. Interestingly, smoking, consumption of caffeine, and physical activities are the protective factors of PD. A deficiency of dopamine in the substantia nigra of the brainstem is the main pathology. This, subsequently, alters the neurotransmitter, causing an imbalance between excitatory and inhibitory signals. In addition, genetics is also involved in the pathogenesis of the disease. As a result, patients exhibit characteristic motor symptoms such as tremors, stiffness, bradykinesia, and postural instability, along with non-motor symptoms, including dementia, urinary incontinence, sleeping disturbances, and orthostatic hypotension. PD may resemble other diseases; therefore, it is important to pay attention to the diagnosis criteria. Parkinson's disease dementia can share common features with AD; this can include behavioral as well as psychiatric symptoms, in addition to the pathology being protein aggregate accumulation in the brain. For PD management, the administration of pharmacological treatment depends on the motor symptoms experienced by the patients. Non-pharmacological treatment plays a role as adjuvant therapy, while surgical management is indicated in chronic cases. This paper aims to review the etiology, risk factors, protective factors, pathophysiology, signs and symptoms, associated conditions, and management of PD.

Identification of diagnostic and prognostic biomarkers of PD using a multiplex proteomics approach

Given the complexity of Parkinson's disease (PD), achieving acceptable diagnostic and prognostic accuracy will require the support of a panel of diverse biomarkers. We used Proximity extension assays to measure a panel of 92 proteins in CSF of 120 newly diagnosed PD patients and 45 control subjects without neurological disease. From 75 proteins detectable in the CSF of >90% of the subjects, regularized regression analysis identified four proteins (β-NGF, CD38, tau and NCAN) as downregulated in newly diagnosed PD patients (age at diagnosis 67.2 ± 9.4 years) compared to controls (age 65.4 ± 10.9 years). Higher tau (β -0.82 transformed MMSE points/year, 95% CI -1.37 to -0.27, P = 0.005) was also linked to faster cognitive decline over the first ten years after PD diagnosis. These findings provide insights into multiple aspects of PD pathophysiology and may serve as the foundation for identifying new biomarkers and therapeutic targets.

Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson's Disease

(1) Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. Early diagnosis and reliable clinical assessments are essential for appropriate therapy and improving patients' quality of life. Keystroke biometrics, which capture unique typing behavior, have shown potential for early PD diagnosis. This study aimed to evaluate keystroke biometric parameters from two datasets to identify indicators that can effectively distinguish de novo PD patients from healthy controls. (2) Methods: Data from natural typing tasks in Physionet were analyzed to estimate keystroke biometric parameters. The parameters investigated included alternating-finger tapping (afTap) and standard deviations of interkey latencies (ILSD) and release latencies (RLSD). Sensitivity rates were calculated to assess the discriminatory ability of these parameters. (3) Results: Significant differences were observed in three parameters, namely afTap, ILSD, and RLSD, between de novo PD patients and healthy controls. The sensitivity rates were high, with values of 83%, 88%, and 96% for afTap, ILSD, and RLSD, respectively. Correlation analysis revealed a significantly negative correlation between typing speed and number of words typed with the standard motor assessment for PD, UPDRS-III, in patients with early PD. (4) Conclusions: Simple algorithms utilizing keystroke biometric parameters can serve as effective screening tests in distinguishing de novo PD patients from healthy controls. Moreover, typing speed and number of words typed were identified as reliable tools for assessing clinical statuses in PD patients. These findings underscore the potential of keystroke biometrics for early PD diagnosis and clinical severity assessment.

Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease

Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.

Extracellular Vesicles for the Diagnosis of Parkinson's Disease: Systematic Review and Meta-Analysis

Parkinson's disease (PD) biomarkers are needed by both clinicians and researchers (for diagnosis, identifying study populations, and monitoring therapeutic response). Imaging, genetic, and biochemical biomarkers have been widely studied. In recent years, extracellular vesicles (EVs) have become a promising material for biomarker development. Proteins and molecular material from any organ, including the central nervous system, can be packed into EVs and transported to the periphery into easily obtainable biological specimens like blood, urine, and saliva. We performed a systematic review and meta-analysis of articles (published before November 15, 2022) reporting biomarker assessment in EVs in PD patients and healthy controls (HCs). Biomarkers were analyzed using random effects meta-analysis and the calculated standardized mean difference (Std.MD). Several proteins and ribonucleic acids have been identified in EVs in PD patients, but only α-synuclein (aSyn) and leucine-rich repeat kinase 2 (LRRK2) were reported in sufficient studies (n = 24 and 6, respectively) to perform a meta-analysis. EV aSyn was significantly increased in neuronal L1 cell adhesion molecule (L1CAM)-positive blood EVs in PD patients compared to HCs (Std.MD = 1.84, 95% confidence interval = 0.76-2.93, P = 0.0009). Further analysis of the biological sample and EV isolation method indicated that L1CAM-IP (immunoprecipitation) directly from plasma was the best isolation method for assessing aSyn in PD patients. Upcoming neuroprotective clinical trials immediately need peripheral biomarkers for identifying individuals at risk of developing PD. Overall, the improved sensitivity of assays means they can identify biomarkers in blood that reflect changes in the brain. CNS-derived EVs in blood will likely play a major role in biomarker development in the coming years. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Neurofilament light chain and cardiac MIBG uptake as predictors for phenoconversion in isolated REM sleep behavior disorder

BACKGROUND

Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is considered as a prodromal stage of either multiple system atrophy (MSA) or Lewy body disease (LBD; Parkinson's disease and dementia with Lewy bodies). However, current knowledge is limited in predicting and differentiating the type of future phenoconversion in iRBD patients. We investigated the role of plasma neurofilament light chain (NfL) and cardiac metaiodobenzylguanidine (MIBG) uptake as predictors for phenoconversion.

METHODS

Forty patients with iRBD were enrolled between April 2018 and October 2019 and prospectively followed every 3 months to determine phenoconversion to either MSA or LBD. Plasma NfL levels were measured at enrollment. Cardiac MIBG uptake and striatal dopamine transporter uptake were assessed at baseline.

RESULTS

Patients were followed for a median of 2.92 years. Four patients converted to MSA and 7 to LBD. Plasma NfL level at baseline was significantly higher in future MSA-converters (median 23.2 pg/mL) when compared with the rest of the samples (median 14.1 pg/mL, p = 0.003). NfL level above 21.3 pg/mL predicted phenoconversion to MSA with the sensitivity of 100% and specificity of 94.3%. Baseline MIBG heart-to-mediastinum ratio of LBD-converters (median 1.10) was significantly lower when compared with the rest (median 2.00, p < 0.001). Heart-to-mediastinum ratio below 1.545 predicted phenoconversion to LBD with the sensitivity of 100% and specificity of 92.9%.

CONCLUSIONS

Plasma NfL and cardiac MIBG uptake may be useful biomarkers in predicting phenoconversion of iRBD. Elevated plasma NfL levels may suggest imminent phenoconversion to MSA, whereas low cardiac MIBG uptake suggests phenoconversion to LBD.

A blood-based marker of mitochondrial DNA damage in Parkinson's disease

Parkinson's disease (PD) is the most common neurodegenerative movement disorder, and neuroprotective or disease-modifying interventions remain elusive. High-throughput markers aimed at stratifying patients on the basis of shared etiology are required to ensure the success of disease-modifying therapies in clinical trials. Mitochondrial dysfunction plays a prominent role in the pathogenesis of PD. Previously, we found brain region-specific accumulation of mitochondrial DNA (mtDNA) damage in PD neuronal culture and animal models, as well as in human PD postmortem brain tissue. To investigate mtDNA damage as a potential blood-based marker for PD, we describe herein a PCR-based assay (Mito DNADX) that allows for the accurate real-time quantification of mtDNA damage in a scalable platform. We found that mtDNA damage was increased in peripheral blood mononuclear cells derived from patients with idiopathic PD and those harboring the PD-associated leucine-rich repeat kinase 2 (LRRK2) G2019S mutation in comparison with age-matched controls. In addition, mtDNA damage was elevated in non-disease-manifesting LRRK2 mutation carriers, demonstrating that mtDNA damage can occur irrespective of a PD diagnosis. We further established that Lrrk2 G2019S knock-in mice displayed increased mtDNA damage, whereas Lrrk2 knockout mice showed fewer mtDNA lesions in the ventral midbrain, compared with wild-type control mice. Furthermore, a small-molecule kinase inhibitor of LRRK2 mitigated mtDNA damage in a rotenone PD rat midbrain neuron model and in idiopathic PD patient-derived lymphoblastoid cell lines. Quantifying mtDNA damage using the Mito DNADX assay may have utility as a candidate marker of PD and for measuring the pharmacodynamic response to LRRK2 kinase inhibitors.

Validation of Administrative Health Data Algorithms for Identifying Persons with Parkinson's Disease and the 10-Year Prevalence Trend in Bologna, Italy

INTRODUCTION

Health administrative databases are widely used for the estimation of the prevalence of Parkinson's disease (PD). Few in general, and none used in Italy, have been validated by testing their diagnostic accuracy. The primary objective was to validate two algorithms for the identification of persons with PD using clinical diagnosis as the reference standard on an Italian sample of people with PD. The second objective was to estimate 10-year trends in PD prevalence in the Bologna Local Health Trust from 2010 to 2019.

METHODS

Two algorithms (index tests) applied to health administrative databases (hospital discharge, drug prescriptions, exemptions for medical costs) were validated against clinical diagnosis of PD by an expert neurologist (reference standard) in a cohort of consecutive outpatients. Sensitivity and specificity with relative 95% confidence intervals (CIs) were calculated. The prevalence of PD in a specific year was estimated as the ratio between the number of subjects fulfilling any criteria of the algorithm with better diagnostic accuracy and the total population in the same year (×1,000), stratified by age, sex, and district of residence.

RESULTS

The two algorithms showed high accuracy for identifying patients with PD: one with greater sensitivity of 94.2% (CI: 88.4-97.6) and the other with greater specificity of 98.1% (CI: 97.7-98.5). For the estimation of prevalence, we chose the most specific algorithm with the fewest total number of misclassified cases. We identified 3,798 people with PD as of December 31, 2019, corresponding to a prevalence of 4.3 per 1,000 inhabitants (CI: 4.2-4.4). Prevalence was higher in males (4.7, CI: 4.5-5.0) than females (3.8, CI: 3.7-4.0) and increased with age. The crude prevalence over time was slightly elevated as it followed a progressive aging of the population. When stratifying the prevalence for age groups, we did not observe a trend except in the 45-64 year category where we observed an increasing trend over time.

CONCLUSION

Algorithms based on administrative data are accurate when detecting people with PD in the Italian public health system. In a large northern Italian population, increased prevalence of about 10% was observed in the decade 2010-2019 and is explained by increased life expectancy. These data may be useful in planning the allocation of health care resources for people with PD.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification

¹⁸F-Fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on ¹⁸F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of ¹⁸F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

Whole-Brain Magnetic Resonance Spectroscopy Reveals Distinct Alterations in Neurometabolic Profile in Progressive Supranuclear Palsy

BACKGROUND

Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized by supranuclear gaze palsy, early postural instability, and a frontal dysexecutive syndrome. Contrary to normal brain magnetic resonance imaging in Parkinson's disease (PD), PSP shows specific cerebral atrophy patterns and alterations, but these findings are not present in every patient, and it is still unclear if these signs are also detectable in early disease stages.

OBJECTIVE

The aim of the present study was to analyze the metabolic profile of patients with clinically diagnosed PSP in comparison with matched healthy volunteers and PD patients using whole-brain magnetic resonance spectroscopic imaging (wbMRSI).

METHODS

Thirty-nine healthy controls (HCs), 29 PD, and 22 PSP patients underwent wbMRSI. PSP and PD patients were matched for age and handedness with HCs. Clinical characterization was performed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale, PSP rating scale, and DemTect (test for cognitive assessment).

RESULTS

In PSP patients a significant reduction in N-acetyl-aspartate (NAA) was detected in all brain lobes. Fractional volume of the cerebrospinal fluid significantly increased in PSP patients compared to PD and healthy volunteers.

CONCLUSIONS

In PSP much more neuronal degeneration and cerebral atrophy have been detected compared with PD. The most relevant alteration is the decrease in NAA in all lobes of the brain, which also showed a partial correlation with clinical symptoms. However, more studies are needed to confirm the additional value of wbMRSI in clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fluid and Biopsy Based Biomarkers in Parkinson's Disease

Several advances in fluid and tissue-based biomarkers for use in Parkinson's disease (PD) and other synucleinopathies have been made in the last several years. While work continues on species of alpha-synuclein (aSyn) and other proteins which can be measured from spinal fluid and plasma samples, immunohistochemistry and immunofluorescence from peripheral tissue biopsies and alpha-synuclein seeding amplification assays (aSyn-SAA: including real-time quaking induced conversion (RT-QuIC) and protein misfolding cyclic amplification (PMCA)) now offer a crucial advancement in their ability to identify aSyn species in PD patients in a categorical fashion (i.e., of aSyn + vs aSyn -); to augment clinical diagnosis however, aSyn-specific assays that have quantitative relevance to pathological burden remain an unmet need. Alzheimer's disease (AD) co-pathology is commonly found postmortem in PD, especially in those who develop dementia, and dementia with Lewy bodies (DLB). Biofluid biomarkers for tau and amyloid beta species can detect AD co-pathology in PD and DLB, which does have relevance for prognosis, but further work is needed to understand the interplay of aSyn tau, amyloid beta, and other pathological changes to generate comprehensive biomarker profiles for patients in a manner translatable to clinical trial design and individualized therapies.

Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and β) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.

Serum metabolomics analysis revealed metabolic disorders in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is by now the second of the most prevalent neurodegenerative diseases in the world, and its incidence is increasing rapidly as the global population ages, with 14.2 million PD patients expected worldwide by 2040.

METHODS

We gathered a completion of 45 serum samples, including 15 of healthy controls and 30 from the PD group. We used non-targeted metabolomics analysis based on liquid chromatography-mass spectrometry to identify the molecular changes in PD patients, and conducted bioinformatics analysis on this basis to explore the possible pathogenesis of PD.

RESULTS

We found significant metabolomics changes in the levels of 30 metabolites in PD patients compared with healthy controls.

CONCLUSION

Lipids and lipid-like molecules accounted for the majority of the 30 differentially expressed metabolites. Also, pathway enrichment analysis showed significant enrichment in sphingolipid metabolic pathway. These assessments can improve our perception on the underlying mechanism of PD as well as facilitate a better targeting on therapeutic interventions.

Retinal alpha-synuclein accumulation correlates with retinal dysfunction and structural thinning in the A53T mouse model of Parkinson's disease

Abnormal alpha-synuclein (α-SYN) protein deposition has long been recognized as one of the pathological hallmarks of Parkinson's disease's (PD). This study considers the potential utility of PD retinal biomarkers by investigating retinal changes in a well characterized PD model of α-SYN overexpression and how these correspond to the presence of retinal α-SYN. Transgenic A53T homozygous (HOM) mice overexpressing human α-SYN and wildtype (WT) control littermates were assessed at 4, 6, and 14  months of age (male and female, n = 15-29 per group). In vivo retinal function (electroretinography, ERG) and structure (optical coherence tomography, OCT) were recorded, and retinal immunohistochemistry and western blot assays were performed to examine retinal α-SYN and tyrosine hydroxylase. Compared to WT controls, A53T mice exhibited reduced light-adapted (cone photoreceptor and bipolar cell amplitude, p < 0.0001) ERG responses and outer retinal thinning (outer plexiform layer, outer nuclear layer, p < 0.0001) which correlated with elevated levels of α-SYN. These retinal signatures provide a high throughput means to study α-SYN induced neurodegeneration and may be useful in vivo endpoints for PD drug discovery.

Central and peripheral α-synuclein in Parkinson disease detected by seed amplification assay

OBJECTIVES

Detection of α-synuclein aggregates by seed amplification is a promising Parkinson disease biomarker assay. Understanding intraindividual relationships of α-synuclein measures could inform optimal biomarker development. The objectives were to test accuracy of α-synuclein seed amplification assay in central (cerebrospinal fluid) and peripheral (submandibular gland) sources, compare to total α-synuclein measures, and investigate within-subject relationships.

METHODS

The Systemic Synuclein Sampling Study aimed to characterize α-synuclein in multiple tissues and biofluids within Parkinson disease subjects (n = 59) and compared to healthy controls (n = 21). Motor and non-motor measures and dopamine transporter scans were obtained. Four measures of α-synuclein were compared: seed amplification assay in cerebrospinal fluid and formalin-fixed paraffin-embedded submandibular gland, total α-synuclein quantified in biofluids using enzyme-linked immunoassay, and aggregated α-synuclein in submandibular gland detected by immunohistochemistry. Accuracy of seed amplification assay for Parkinson disease diagnosis was examined and within-subject α-synuclein measures were compared.

RESULTS

Sensitivity and specificity of α-synuclein seed amplification assay for Parkinson disease diagnosis was 92.6% and 90.5% in cerebrospinal fluid, and 73.2% and 78.6% in submandibular gland, respectively. 25/38 (65.8%) Parkinson disease participants were positive for both cerebrospinal fluid and submandibular gland seed amplification assay. Comparing accuracy for Parkinson disease diagnosis of different α-synuclein measures, cerebrospinal fluid seed amplification assay was the highest (Youden Index = 83.1%). 98.3% of all Parkinson disease cases had ≥1 measure of α-synuclein positive.

INTERPRETATION

α-synuclein seed amplification assay (cerebrospinal fluid>submandibular gland) had higher sensitivity and specificity compared to total α-synuclein measures, and within-subject relationships of central and peripheral α-synuclein measures emerged.

Reliability and validity of visual analysis of [ 18 F]FE-PE2I PET/CT in early Parkinsonian disease

OBJECTIVE

[ 18 F]FE-PE2I (FE-PE2I) is a new radiotracer for dopamine transporter (DAT) imaging with PET. The aim of this study was to evaluate the visual interpretation of FE-PE2I images for the diagnosis of idiopathic Parkinsonian syndrome (IPS). The inter-rater variability, sensitivity, specificity, and diagnostic accuracy for visual interpretation of striatal FE-PE2I compared to [ 123 I]FP-CIT (FP-CIT) single-photon emission computed tomography (SPECT) was evaluated.

METHODS

Thirty patients with newly onset parkinsonism and 32 healthy controls with both an FE-PE2I and FP-CIT were included in the study. Four patients had normal DAT imaging, of which three did not fulfil the IPS criteria at the clinical reassessment after 2 years. Six raters evaluated the DAT images blinded to the clinical diagnosis, interpreting the image as being 'normal' or 'pathological', and assessed the degree of DAT-reduction in the caudate and putamen. The inter-rater agreement was assessed with intra-class correlation and Cronbach's α . For calculation of sensitivity and specificity, DAT images were defined as correctly classified if categorized as normal or pathological by ≥4/6 raters.

RESULTS

The overall agreement in visual evaluation of the FE-PE2I- and FP-CIT images was high for the IPS patients ( α  = 0.960 and 0.898, respectively), but lower in healthy controls (FE-PE2I: α  = 0.693, FP-CIT: α  = 0.657). Visual interpretation gave high sensitivity (both 0.96) but lower specificity (FE-PE2I: 0.86, FP-CIT: 0.63) with an accuracy of 90% for FE-PE2I and 77% for FP-CIT.

CONCLUSION

Visual evaluation of FE-PE2I PET imaging demonstrates high reliability and diagnostic accuracy for IPS.

Low clinical sensitivity and unexpectedly high incidence for neuropathologically diagnosed progressive supranuclear palsy

The objective of this study was to determine the prevalence, incidence, and clinical diagnostic accuracy for neuropathologically diagnosed progressive supranuclear palsy (PSP) with data from a longitudinal clinicopathological study using Rainwater criteria to define neuropathological PSP. Of 954 autopsy cases, 101 met Rainwater criteria for the neuropathologic diagnosis of PSP. Of these, 87 were termed clinicopathological PSP as they also had either dementia or parkinsonism or both. The prevalence of clinicopathologically defined PSP subjects in the entire autopsy dataset was 9.1%, while the incidence rate was estimated at 780 per 100 000 persons per year, roughly 50-fold greater than most previous clinically determined PSP incidence estimates. A clinical diagnosis of PSP was 99.6% specific but only 9.2% sensitive based on first examination, and 99.3% specific and 20.7% sensitive based on the final clinical exam. Of the clinicopathologically defined PSP cases, 35/87 (∼40%) had no form of parkinsonism at first assessment, while this decreased to 18/83 (21.7%) at final assessment. Our study confirms a high specificity but low sensitivity for the clinical diagnosis of PSP. The low clinical sensitivity for PSP is likely primarily responsible for previous underestimates of the PSP population incidence rate.

Cutaneous α-Synuclein Signatures in Patients With Multiple System Atrophy and Parkinson Disease

BACKGROUND AND OBJECTIVES

Multiple system atrophy (MSA) is a progressive neurodegenerative disorder caused by the abnormal accumulation of α-synuclein in the nervous system. Clinical features include autonomic and motor dysfunction, which overlap with those of Parkinson disease (PD), particularly at early disease stages. There is an unmet need for accurate diagnostic and prognostic biomarkers for MSA and, specifically, a critical need to distinguish MSA from other synucleinopathies, particularly PD. The purpose of the study was to develop a unique cutaneous pathologic signature of phosphorylated α-synuclein that could distinguish patients with MSA from patients with PD and healthy controls.

METHODS

We studied 31 patients with MSA and 54 patients with PD diagnosed according to current clinical consensus criteria. We also included 24 matched controls. All participants underwent neurologic examinations, autonomic testing, and skin biopsies at 3 locations. The density of intraepidermal, sudomotor, and pilomotor nerve fibers was measured. The deposition of phosphorylated α-synuclein was quantified. Results were compared with clinical rating assessments and autonomic function test results.

RESULTS

Patients with PD had reduced nerve fiber densities compared with patients with MSA (p < 0.05, all fiber types). All patients with MSA and 51/54 with PD had evidence of phosphorylated α-synuclein in at least one skin biopsy. No phosphorylated α-synuclein was detected in controls. Patients with MSA had greater phosphorylated α-synuclein deposition (p < 0.0001) and more widespread peripheral distribution (p < 0.0001) than patients with PD. These results provided >90% sensitivity and specificity in distinguishing between the 2 disorders.

DISCUSSION

α-synuclein is present in the peripheral autonomic nerves of patients with MSA and when combined with synuclein distribution accurately distinguishes MSA from PD.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that measurement of phosphorylated α-synuclein in skin biopsies can differentiate patients with MSA from those with PD.

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.