Combined CSF α-SYN RT-QuIC, CSF NFL and midbrain-pons planimetry in degenerative parkinsonisms: From bedside to bench, and back again

Ethical Considerations for Identifying Individuals in the Prodromal/Early Phase of Parkinson's Disease: A Narrative Review

The ability to identify individuals in the prodromal phase of Parkinson's disease has improved in recent years, raising the question of whether and how those affected should be informed about the risk of future disease. Several studies investigated prognostic counselling for individuals with isolated REM sleep behavior disorder and have shown that most patients want to receive information about prognosis, but autonomy and individual preferences must be respected. However, there are still many unanswered questions about risk disclosure or early diagnosis of PD, including the impact on personal circumstances, cultural preferences and specific challenges associated with different profiles of prodromal symptoms, genetic testing or biomarker assessments. This narrative review aims to summarize the current literature on prognostic counselling and risk disclosure in PD, as well as highlight future perspectives that may emerge with the development of new biomarkers and their anticipated impact on the definition of PD.

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.

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study

INTRODUCTION

Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases.

METHODS

Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC.

RESULTS

PSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85-0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04.

CONCLUSION

Our findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.

Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor

Introduction

Distinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data.

Methods

We enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients.

Results

Both structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase.

Conclusion

Machine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

Detecting Misfolded α-Synuclein in Blood Years before the Diagnosis of Parkinson's Disease

BACKGROUND

Identifying individuals with Parkinson's disease (PD) already in the prodromal phase of the disease has become a priority objective for opening a window for early disease-modifying therapies.

OBJECTIVE

The aim was to evaluate a blood-based α-synuclein seed amplification assay (α-syn SAA) as a novel biomarker for diagnosing PD in the prodromal phase.

METHODS

In the TREND study (University of Tuebingen) biennial blood samples of n = 1201 individuals with/without increased risk for PD were taken prospectively over 4 to 10 years. We retrospectively analyzed blood samples of 12 participants later diagnosed with PD during the study to detect and amplify pathological α-syn conformers derived from neuronal extracellular vesicles using (1) immunoblot analyses with an antibody against these conformers and (2) an α-syn-SAA. Additionally, blood samples of n = 13 healthy individuals from the TREND cohort and n = 20 individuals with isolated rapid eye movement sleep behavior disorder (iRBD) from the University Hospital Cologne were analyzed.

RESULTS

All individuals with PD showed positive immunoblots and a positive α-syn SAA at the time of diagnosis. Moreover, all PD patients showed a positive α-syn SAA 1 to 10 years before clinical diagnosis. In the iRBD cohort, 30% showed a positive α-syn SAA. All healthy controls had a negative SAA.

CONCLUSIONS

We here demonstrate the possibility to detect and amplify pathological α-syn conformers in peripheral blood up to 10 years before the clinical diagnosis of PD in individuals with and without iRBD. The findings of this study indicate that this blood-based α-syn SAA assay has the potential to serve as a diagnostic biomarker for prodromal PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fluid and tissue biomarkers in Parkinson's disease: Immunodetection or seed amplification? Central or peripheral?

Over the last two decades there have been meaningful developments on biomarkers of neurodegenerative diseases, extensively (but not solely) focusing on their proteinopathic nature. Accordingly, in Alzheimer's disease determination of levels of total and phosphorylated tau (τ and p-τ, usually p-τ181) along with amyloid-beta1-42 (Aβ1-42) by immunodetection in cerebrospinal fluid (CSF) and currently even in peripheral blood, have been widely accepted and introduced to routine diagnosis. In the case of Parkinson's disease, α-synuclein as a potential biomarker (both for diagnosis and progression tracking) has proved more elusive under the immunodetection approach. In recent years, the emergence of the so-called seed amplification assays is proving to be a game-changer, with mounting evidence under different technical approaches and using a variety of biofluids or tissues, yielding promising diagnostic accuracies. Currently the least invasive but at once more reliable source of biosamples and techniques are being sought. Here we overview these advances.

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.

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

Background

Misfolded α-synuclein can be detected in blood samples of Parkinson's disease (PD) patients by a seed amplification assay (SAA), but the association with disease duration is not clear, yet.

Objective

In the present study we aimed to elucidate whether seeding activity of misfolded α-synuclein derived from neuronal exosomes in blood is associated with PD diagnosis and disease duration.

Methods

Cross-sectional samples of PD patients were analyzed and compared to samples of age- and gender-matched healthy controls using a blood-based SAA. Presence of α-synuclein seeding activity and differences in seeding parameters, including fluorescence response (in arbitrary units) at the end of the amplification assay (F60) were analyzed. Additionally, available PD samples collected longitudinally over 5-9 years were included.

Results

In the cross-sectional dataset, 79 of 80 PD patients (mean age 69 years, SD = 8; 56% male) and none of the healthy controls (n = 20, mean age 70 years, SD = 10; 55% male) showed seeding activity (sensitivity 98.8%). When comparing subgroups divided by disease duration, longer disease duration was associated with lower α-synuclein seeding activity (F60: p < 0.001). In the longitudinal analysis 10/11 patients showed a gradual decrease of α-synuclein seeding activity over time.

Conclusions

This study confirms the high sensitivity of the blood-based α-synuclein SAA applied here. The negative association of α-synuclein seeding activity in blood with disease duration makes this parameter potentially interesting as biomarker for future studies on the pathophysiology of disease progression in PD, and for biologically oriented trials in this field.

Comparison of biospecimens for α-synuclein seed amplification assays in Parkinson's disease: A systematic review and network meta-analysis

BACKGROUND AND PURPOSE

Alpha-synuclein seed amplification assays (α-syn SAAs) are promising diagnostic methods for Parkinson's disease (PD) and other synucleinopathies. However, there is limited consensus regarding the diagnostic and differential diagnostic performance of α-syn SAAs on biofluids and peripheral tissues.

METHODS

A comprehensive research was performed in PubMed, Web of Science, Embase and Cochrane Library. Meta-analysis was performed using a random-effects model. A network meta-analysis based on an ANOVA model was conducted to compare the relative accuracy of α-syn SAAs with different specimens.

RESULTS

The pooled sensitivity and specificity of α-syn SAAs in distinguishing PD from healthy controls or non-neurodegenerative neurological controls were 0.91 (95% confidence interval [CI] 0.89-0.92) and 0.95 (95% CI 0.94-0.96) for cerebrospinal fluid (CSF); 0.91 (95% CI 0.86-0.94) and 0.92 (95% CI 0.87-0.95) for skin; 0.80 (95% CI 0.66-0.89) and 0.87 (95% CI 0.69-0.96) for submandibular gland; 0.44 (95% CI 0.30-0.59) and 0.92 (95% CI 0.79-0.98) for gastrointestinal tract; 0.79 (95% CI 0.70-0.86) and 0.88 (95% CI 0.77-0.95) for saliva; and 0.51 (95% CI 0.39-0.62) and 0.91 (95% CI 0.84-0.96) for olfactory mucosa (OM). The pooled sensitivity and specificity were 0.91 (95% CI 0.89-0.93) and 0.50 (95% CI 0.44-0.55) for CSF, 0.92 (95% CI 0.83-0.97) and 0.22 (95% CI 0.06-0.48) for skin, and 0.55 (95% CI 0.42-0.68) and 0.50 (95% CI 0.35-0.65) for OM in distinguishing PD from multiple system atrophy. The pooled sensitivity and specificity were 0.92 (95% CI 0.89-0.94) and 0.84 (95% CI 0.73-0.91) for CSF, 0.92 (95% CI 0.83-0.97) and 0.88 (95% CI 0.64-0.99) for skin and 0.63 (95% CI 0.52-0.73) and 0.86 (95% CI 0.64-0.97) for OM in distinguishing PD from progressive supranuclear palsy. The pooled sensitivity and specificity were 0.94 (95% CI 0.90-0.97) and 0.95 (95% CI 0.77-1.00) for CSF and 0.94 (95% CI 0.84-0.99) and 0.86 (95% CI 0.42-1.00) for skin in distinguishing PD from corticobasal degeneration.

CONCLUSIONS

α-Synuclein SAAs of CSF, skin, saliva, submandibular gland, gastrointestinal tract and OM are promising diagnostic assays for PD, with CSF and skin α-syn SAAs demonstrating higher diagnostic performance.

How should we be using biomarkers in trials of disease modification in Parkinson's disease?

The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.

DOPA decarboxylase is an emerging biomarker for Parkinsonian disorders including preclinical Lewy body disease

The diagnosis of Parkinsonian disorders is currently based on clinical criteria, which have limited sensitivity until most dopaminergic neurons are lost. Here we show that cerebrospinal fluid levels of DOPA decarboxylase (DDC) (also known as aromatic L-amino acid decarboxylase) can accurately identify patients with Lewy body disease (LBD) (area under the curve (AUC) = 0.89; PFDR = 2.6 × 10-13) and are associated with worse cognitive performance (P < 0.05). We also found that DDC can detect preclinical LBD stages in clinically unimpaired individuals with a positive seed amplification α-synuclein assay (AUC = 0.81, P = 1.0 × 10-5) and that this biomarker could predict progression to clinical LBD over a 3-year period in preclinical cases (hazard ratio = 3.7 per s.d. change, confidence interval = 1.1-12.7). Moreover, DDC levels were also increased in atypical Parkinsonian disorders but not in non-Parkinsonian neurodegenerative disorders. These cerebrospinal fluid results were replicated in an independent cohort, where we also found that DDC levels in plasma could identify both LBD and atypical Parkinsonian disorders (AUC = 0.92, P = 1.3 × 10-14). Our results show that DDC might have a future role in clinical practice as a biomarker of dopaminergic dysfunction to detect Parkinsonian disorders even during the preclinical disease stages and predict their progression to clinical LBD.

Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design

Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.

Altered sleep and neurovascular dysfunction in alpha-synucleinopathies: the perfect storm for glymphatic failure

Clinical and cognitive progression in alpha-synucleinopathies is highly heterogeneous. While some patients remain stable over long periods of time, other suffer early dementia or fast motor deterioration. Sleep disturbances and nocturnal blood pressure abnormalities have been identified as independent risk factors for clinical progression but a mechanistic explanation linking both aspects is lacking. We hypothesize that impaired glymphatic system might play a key role on clinical progression. Glymphatic system clears brain waste during specific sleep stages, being blood pressure the motive force that propels the interstitial fluid through brain tissue to remove protein waste. Thus, the combination of severe sleep alterations, such as REM sleep behavioral disorder, and lack of the physiological nocturnal decrease of blood pressure due to severe dysautonomia may constitute the perfect storm for glymphatic failure, causing increased abnormal protein aggregation and spreading. In Lewy body disorders (Parkinson's disease and dementia with Lewy bodies) the increment of intraneuronal alpha-synuclein and extracellular amyloid-β would lead to cognitive deterioration, while in multisystemic atrophy, increased pathology in oligodendroglia would relate to the faster and malignant motor progression. We present a research model that may help in developing studies aiming to elucidate the role of glymphatic function and associated factors mainly in alpha-synucleinopathies, but that could be relevant also for other protein accumulation-related neurodegenerative diseases. If the model is proven to be useful could open new lines for treatments targeting glymphatic function (for example through control of nocturnal blood pressure) with the objective to ameliorate cognitive and motor progression in alpha-synucleinopathies.

Misfolded α-Synuclein Assessment in the Skin and CSF by RT-QuIC in Isolated REM Sleep Behavior Disorder

BACKGROUND AND OBJECTIVES

Real-time quaking-induced conversion (RT-QuIC) assay detects misfolded α-synuclein (AS) in the skin and CSF of patients with the synucleinopathies Parkinson disease and dementia with Lewy bodies. Isolated REM sleep behavior disorder (IRBD) constitutes the prodromal stage of these synucleinopathies. We aimed to compare the ability of RT-QuIC to identify AS in the skin and CSF of patients with IRBD.

METHODS

This was a cross-sectional study where consecutive patients with polysomnographic-confirmed IRBD and age-matched controls without RBD underwent skin biopsy and lumbar puncture the same day. Three-millimeter skin punch biopsies were obtained bilaterally in the cervical region from dorsal C7 and C8 dermatomes and in distal legs. RT-QuIC assessed AS in these 6 skin sites and the CSF.

RESULTS

We recruited 91 patients with IRBD and 41 controls. In the skin, sensitivity to detect AS was 76.9% (95% CI 66.9-85.1), specificity 97.6% (95% CI 87.1-99.9) positive predictive value 98.6% (95% CI 91.0-99.8), negative predictive value 65.6% (95% CI 56.6-73.6), and accuracy 83.3% (95% CI 75.9-89.3). In the CSF, the sensitivity was 75.0% (95% CI 64.6-83.6), the specificity was 97.5% (95% CI 86.8-99.9), the positive predictive value was 98.5% (95% CI 90.5-99.8), the negative predictive value was 63.9% (95% CI 55.2-71.9), and the accuracy was 82.0% (95% CI 74.3-88.3). Results in the skin and CSF samples showed 99.2% agreement. Compared with negative patients, RT-QuIC AS-positive patients had a higher likelihood ratio of prodromal Parkinson disease (p < 0.001) and showed more frequently hyposmia (p < 0.001), dopamine transporter imaging single-photon emission CT deficit (p = 0.002), and orthostatic hypotension (p = 0.014). No severe or moderate adverse effects were reported. There was no difference between the percentage of participants reporting mild adverse events secondary to skin biopsy or lumbar puncture (9.1% vs 17.2%; p = 0.053). One hundred and ten (83%) and 104 (80%) participants, respectively, stated they would accept to undergo skin biopsy and lumbar puncture again for research purposes.

DISCUSSION

Our study in IRBD shows that (1) RT-QuIC detects AS in the skin and CSF with similar high sensitivity, specificity, and agreement, (2) AS RT-QuIC positivity is associated with supportive features and biomarkers of synucleinopathy, and (3) skin punch biopsy and lumbar puncture have comparable mild adverse effects, tolerance, and acceptance. RT-QuIC in the skin or CSF might represent a patient selection strategy for future neuroprotective trials targeting AS in IRBD.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that RT-QuIC-detected AS in the skin and CSF distinguishes patients with IRBD from controls.

Head-to-head comparison of 6 plasma biomarkers in early multiple system atrophy

There is a dire need for reliable biomarkers to solidify an early and accurate diagnosis of multiple system atrophy (MSA). We sought to compare the ability of emerging plasma markers in distinguishing MSA from its mimics and healthy controls in early disease stages, and to evaluate their performance in detecting disease severity and brain atrophy. Plasma neurofilament light (NfL), glial fibrillary acidic protein (GFAP), phosphorylated tau181, amyloid-β (Aβ)42, and Aβ40 were measured using ultrasensitive Simoa in early-stage patients with MSA (n = 73), spinocerebellar ataxia (SCA, n = 29), Parkinson's disease (PD, n = 28), and healthy controls (n = 100). We observed that elevated NfL outperformed other biomarkers in distinguishing MSA and its subtypes (AUC = 0.9) versus controls. Intriguingly, when separating MSA from its mimics, increased GFAP (AUC = 0.717) in MSA-C and decreased Aβ40 (AUC = 0.807) in MSA-P best discriminated from SCA and PD respectively. Plasma levels were comparable between MSA-C and MSA-P and the differentiation by plasma index alone was poor. Combining plasma markers noticeably improved the discriminatory efficacy. Of note, among MSA patients, higher GFAP and NfL were correlated with the atrophy of brain regions vulnerable to MSA (e.g., cerebellum, pons, or putamen). They could also aggravate the severity of MSA, and this association was partially mediated by cerebral volumes. In contrast, no obvious associations of phosphorylated tau and Aβ with disease severity were observed. Collectively, plasma biomarkers, especially in combination, are useful to facilitate the discriminatory work-up of MSA at early stages. Moreover, NfL and GFAP may be promising biomarkers to monitor the disease severity of MSA.