Reduced cerebrospinal fluid motion in patients with Parkinson's disease revealed by magnetic resonance imaging with low b-value diffusion weighted imaging.

BACKGROUND: Parkinson's disease is characterized by dopamine-responsive symptoms as well as aggregation of α-synuclein protofibrils. New diagnostic methods assess α-synuclein aggregation characteristics from cerebrospinal fluid (CSF) and recent pathophysiologic mechanisms suggest that CSF circulation disruptions may precipitate α-synuclein retention. Here, diffusion-weighted MRI with low-to-intermediate diffusion-weightings was applied to test the hypothesis that CSF motion is reduced in Parkinson's disease relative to healthy participants. METHODS: Multi-shell diffusion weighted MRI (spatial resolution = 1.8 × 1.8 × 4.0 mm) with low-to-intermediate diffusion weightings (b-values = 0, 50, 100, 200, 300, 700, and 1000 s/mm2) was applied over the approximate kinetic range of suprasellar cistern fluid motion at 3 Tesla in Parkinson's disease (n = 27; age = 66 ± 6.7 years) and non-Parkinson's control (n = 32; age = 68 ± 8.9 years) participants. Wilcoxon rank-sum tests were applied to test the primary hypothesis that the noise floor-corrected decay rate of CSF signal as a function of b-value, which reflects increasing fluid motion, is reduced within the suprasellar cistern of persons with versus without Parkinson's disease and inversely relates to choroid plexus activity assessed from perfusion-weighted MRI (significance-criteria: p < 0.05). RESULTS: Consistent with the primary hypothesis, CSF decay rates were higher in healthy (D = 0.00673 ± 0.00213 mm2/s) relative to Parkinson's disease (D = 0.00517 ± 0.00110 mm2/s) participants. This finding was preserved after controlling for age and sex and was observed in the posterior region of the suprasellar cistern (p < 0.001). An inverse correlation between choroid plexus perfusion and decay rate in the voxels within the suprasellar cistern (Spearman's-r=-0.312; p = 0.019) was observed. CONCLUSIONS: Multi-shell diffusion MRI was applied to identify reduced CSF motion at the level of the suprasellar cistern in adults with versus without Parkinson's disease; the strengths and limitations of this methodology are discussed in the context of the growing literature on CSF flow.

Neuropsychiatric symptoms profile and markers of Alzheimer disease-type pathology in patients with Lewy body dementias.

BACKGROUND: To determine whether Lewy body dementia (LBD) patients with likely copathology of Alzheimer's disease (AD) exhibit greater neuropsychiatric symptom (NPS) compared to those without likely AD-type copathology. METHODS: We enrolled 69 individuals diagnosed with Lewy body dementia (LBD), comprising both dementia with Lewy bodies (DLB) (n = 36) and Parkinson's disease dementia (PDD) (n = 33). These participants had accessible cerebrospinal fluid (CSF) markers related to Alzheimer's disease (AD) and cognitive data. We assessed CSF levels of ß-amyloid 42 (Aß42), phosphorylated tau (p-tau), and total tau (t-tau). Employing autopsy-validated CSF thresholds (t-tau/Aß42 ratio > 0.3, n = 69), we categorized individuals into LBD with AD pathology (LBD + AD, n = 31) and LBD without apparent AD co-pathology (LBD - AD, n = 38). Moreover, the Hamilton Depression Scale (HAMD24), Hamilton Anxiety Scale (HAMA14), and Neuropsychiatric Inventory Questionnaire (NPI-Q) was used to assess the NPS. Spearman correlations were utilized to explore links between NPS and CSF marker profiles. RESULTS: In terms of neuropsychiatric symptoms, LBD + AD patients demonstrated notably elevated levels of depressive symptoms (HAMD24) in comparison to LBD - AD patients (P < 0.001). However, based on PDD and DLB groups, no significant variations were noted in the neuropsychiatric symptoms(P＞0.05). Moreover, CSF-derived biomarkers of Aß42, and t-tau/Aß42 were also associated with HAMD24 total scores in the LBD + AD subsample (P < 0.05). CONCLUSION: There is an association between AD pathological markers and the NPS of LBD. The biologically based classification of LBD may be more advantageous in elucidating clinical heterogeneity than clinically defined syndromes.

Mood disturbances in newly diagnosed Parkinson's Disease patients reflect intrathecal inflammation.

In Parkinson's disease (PD), neuroinflammation may be involved in the pathogenesis of mood disorders, contributing to the clinical heterogeneity of the disease. The cerebrospinal fluid (CSF) levels of interleukin (IL)-1ß, IL-2, IL-6, IL-7, IL-8, IL-9, IL-12, IL-17, interferon (IFN)γ, macrophage inflammatory protein 1-alpha (MIP-1a), MIP-1b, granulocyte colony stimulating factor (GCSF), eotaxin, tumor necrosis factor (TNF), and monocyte chemoattractant protein 1 (MCP-1), were assessed in 45 newly diagnosed and untreated PD patients and in 44 control patients. Spearman's correlations were used to explore possible associations between CSF cytokines and clinical variables including mood. Benjamini-Hochberg (B-H) correction for multiple comparisons was applied. Linear regression was used to test significant associations correcting for other clinical variables. In PD patients, higher CSF concentrations of the inflammatory molecules IL-6, IL-9, IFNγ, and GCSF were found (all B-H corrected p < 0.02). Significant associations were found between BDI-II and the levels of IL-6 (Beta = 0.438; 95%CI 1.313-5.889; p = 0.003) and IL-8 (Beta = 0.471; 95%CI 0.185-0.743; p = 0.002). Positive associations were also observed between STAI-Y state and both IL-6 (Beta = 0.452; 95%CI 1.649-7.366; p = 0.003), and IL-12 (Beta = 0.417; 95%CI 2.238-13.379; p = 0.007), and between STAI-Y trait and IL-2 (Beta = 0.354; 95%CI 1.923-14.796; p = 0.012), IL-6 (Beta = 0.362; 95%CI 0.990-6.734; p = 0.01), IL-8 (Beta = 0.341; 95%CI 0.076-0.796; p = 0.019), IL-12 (Beta = 0.328; 95%CI 0.975-12.135; p = 0.023), and IL-17 (Beta = 0.334; 95CI 0.315-4.455; p = 0.025). An inflammatory CSF milieu may be associated with depression and anxiety in the early phases of PD, supporting a role of neuroinflammation in the pathogenesis of mood disturbances.

Investigating alpha-synuclein co-pathology in Alzheimer's disease by means of cerebrospinal fluid alpha-synuclein seed amplification assay.

INTRODUCTION: Lewy body disease, a frequently observed co-pathology in Alzheimer's disease (AD), can be identified antemortem in cerebrospinal fluid (CSF) by α-synuclein seed amplification assay (αS-SAA). The prevalence and clinical impact of CSF αS-SAA positivity in AD are still unknown. METHODS: αS-SAA was performed on CSF samples from 240 AD patients (preclinical, prodromal, and dementia stages), 85 controls, 84 patients with Parkinson's disease (PD), and 21 patients with PD with dementia or dementia with Lewy bodies. In AD patients, associations between αS-SAA positivity and cognitive changes were also evaluated. RESULTS: In agreement with available neuropathological studies, αS-SAA positivity was observed in 30% of AD patients (vs 9% in controls), and was associated with cognitive decline, visuospatial impairment, and behavioral disturbances. DISCUSSION: αS-SAA positivity in AD patients reflects the prevalence observed in neuropathological series and is associated with a worse clinical outcome. These data confirm the validity of CSF αS-SAA positivity as biomarker of synucleinopathy.

Glypican-4 serum levels are associated with cognitive dysfunction and vascular risk factors in Parkinson's disease.

Glypicans are biomarkers for various pathologies, including cardiovascular disease, cancer and diabetes. Increasing evidence suggests that glypicans also play a role in the context of neurodegenerative disorders. Initially described as supporting functionality of synapses via glutamate receptors during CNS development, Glypican 4 (GPC-4) also plays a role in the context of dementia via tau hyperphosphorylation in Alzheimer's disease, which is also a co-pathology in Parkinson's disease dementia. However, clinical evidence of circulating GPC-4 in Parkinson's disease (PD) is missing so far. We therefore investigated GPC-4 in biofluids of PD patients. We analyzed GPC-4 levels in cerebrospinal fluid (CSF, n = 140), serum (n = 80), and tear fluid samples (n = 70) of PD patients and control subjects in a similar age range by ELISA (serum, CSF) and western blot (tear fluid). Expression of circulating GPC-4 was confirmed in all three biofluids, with highest levels in serum. Interestingly, GPC-4 levels were age-dependent, and multiple regression analysis revealed a significant association between GPC-4 serum levels and MoCA score, suggesting an involvement of GPC-4 in PD-associated cognitive decline. Furthermore, stratification of PD patients for vascular risk factors revealed a significant increase of GPC-4 serum levels in PD patients with vascular risk factors. Our results suggest GPC-4 as a clinical biomarker for vascular risk stratification in order to identify PD patients with increased risk of developing dementia.

Cerebrospinal fluid protein biomarkers in Parkinson's disease.

Proteomic profiling is an effective way to identify biomarkers for Parkinson's disease (PD). Cerebrospinal fluid (CSF) has direct connectivity with the brain and could be a source of finding biomarkers and their clinical implications. Comparative proteomic profiling has shown that a group of differentially displayed proteins exist. The studies performed using conventional and classical tools also supported the occurrence of these proteins. Many studies have highlighted the potential of CSF proteomic profiling for biomarker identification and their clinical applications. Some of these proteins are useful for disease diagnosis and prediction. Proteomic profiling of CSF also has immense potential to distinguish PD from similar neurodegenerative disorders. A few protein biomarkers help in fundamental knowledge generation and clinical interpretation. However, the specific biomarker of PD is not yet known. The use of proteomic approaches in clinical settings is also rare. A large-scale, multi-centric, multi-population and multi-continental study using multiple proteomic tools is warranted. Such a study can provide valuable, comprehensive and reliable information for a better understanding of PD and the development of specific biomarkers. The current article sheds light on the role of CSF proteomic profiling in identifying biomarkers of PD and their clinical implications. The article also explains the achievements, obstacles and hopes for future directions of this approach.

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 (Aß1-42) 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.

An interactive web application to identify early Parkinsonian non-tremor-dominant subtypes.

BACKGROUND: Parkinson's disease (PD) patients with tremor-dominant (TD) and non-tremor-dominant (NTD) subtypes exhibit heterogeneity. Rapid identification of different motor subtypes may help to develop personalized treatment plans. METHODS: The data were acquired from the Parkinson's Disease Progression Marker Initiative (PPMI). Following the identification of predictors utilizing recursive feature elimination (RFE), seven classical machine learning (ML) models, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting, etc., were trained to predict patients' motor subtypes, evaluating the performance of models through the area under the receiver operating characteristic curve (AUC) and validating by the follow-up data. RESULTS: The feature subset engendered by RFE encompassed 20 features, comprising some clinical assessments and cerebrospinal fluid α-synuclein (CSF α-syn). ML models fitted in the RFE subset performed better in the test and validation sets. The best performing model was support vector machines with the polynomial kernel (P-SVM), achieving an AUC of 0.898. Five-fold repeated cross-validation showed the P-SVM model with CSF α-syn performed better than the model without CSF α-syn (P = 0.034). The Shapley additive explanation plot (SHAP) illustrated that how the levels of each feature affect the predicted probability as NTD subtypes. CONCLUSION: An interactive web application was developed based on the P-SVM model constructed from feature subset by RFE. It can identify the current motor subtypes of PD patients, making it easier to understand the status of patients and develop personalized treatment plans.

[Tandem mass tag-based quantitative proteomics analysis of plasma and plasma exosomes in Parkinson's disease].

The cardinal clinical features of Parkinson's disease (PD), a common neurodegenerative disease, include the irreversible impairment of movement coordination, such as tremors, gait rigidity, bradykinesia, and hypokinesia. Although various factors are associated with the pathological changes in PD, such as oxidative stress, mitochondrial dysfunction, and neuroinflammation, the availability of treatments to retard PD progression is limited. Therefore, novel biomarkers for PD diagnosis and therapeutic targets are urgently needed. The diagnosis of PD mainly depends on its clinical manifestations and has an error rate of approximately 20%. Studies have shown that α-synuclein (α-syn) levels are significantly increased in the cerebrospinal fluid of patients with PD; however, the invasive nature of lumbar puncture restricts further studies on its clinical applications. Hence, the development of novel peripheral blood markers would be helpful for the early diagnosis of PD. Exosomes are extracellular vesicles (EVs) released by various cell types under physiological and pathophysiological conditions. Because exosomes carry a variety of bioactive molecules, they play a key role in biological processes such as intercellular communication and the immune response. Central nervous system (CNS)-derived exosomes can be detected in the cerebrospinal and peripheral body fluids of patients with PD, and their contents are altered during the disease process, rendering them an attractive biomarker resource. Therefore, a comprehensive and high-throughput investigation of the plasma and its exosomes may enhance our understanding of PD. In this study, we isolated exosomes from plasma using standard differential centrifugation and performed tandem mass tag (TMT)-labeled quantitative proteomic analysis of plasma and plasma exosome samples from healthy individuals and patients with PD using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 724 proteins were quantified in the plasma samples, and 611 proteins were screened from the exosome samples. Among these 611 proteins, 413 were found in the Exosomal Protein Database (Exocarta). Using |log2FC|>0.26 and P-value (P)<0.05 as the cutoff, five upregulated and six downregulated proteins were identified in the plasma samples of the PD group compared with the healthy group. In the plasma exosome samples, compared with the healthy group, the PD group showed six upregulated and seven downregulated proteins. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted based on gene set enrichment analysis (GSEA). GO-cellular component (CC) analysis revealed that plasma-enriched proteins were mainly located in the nucleus whereas plasma exosome-enriched proteins were mainly located in the cytoplasm. According to the GO-molecular function (MF) analysis, the MFs of differentially expressed proteins in the plasma were mainly enriched in RNA, DNA binding, and complement binding. By contrast, the molecular functions of differentially expressed proteins derived from plasma exosomes were enriched in antioxidant activity, oxidoreductase activity, and peroxide acceptor activity. We then analyzed the enriched KEGG pathways of differentially expressed proteins derived from the plasma and plasma exosome samples. The enrichment pathways of differentially expressed proteins in the plasma samples included the lysosome pathway, cellular senescence, and protein processing in the endoplasmic reticulum. By contrast, the enrichment pathways of differentially expressed proteins in the plasma exosome samples included chemokine signaling and cytokine receptor interactions. Finally, we assessed the functions of some exosomal proteins in PD to elucidate their potential for PD diagnosis and treatment. Significant differences were observed between the plasma and plasma exosome protein profiles, and the functions of differentially expressed proteins in plasma exosomes were strongly related to the pathology of PD. Our study provides a reference for identifying the potential biomarkers and therapeutic targets of PD.

Proteomic comparison between non-purified cerebrospinal fluid and cerebrospinal fluid-derived extracellular vesicles from patients with Alzheimer's, Parkinson's and Lewy body dementia.

Dementia is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases. The most common neurodegenerative disorders are Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). With this study, we took an in-depth look at the proteome of the (non-purified) cerebrospinal fluid (CSF) and the CSF-derived extracellular vesicles (EVs) of AD, PD, PD-MCI (Parkinson's disease with mild cognitive impairment), PDD and DLB patients analysed by label-free mass spectrometry. This has led to the discovery of differentially expressed proteins that may be helpful for differential diagnosis. We observed a greater number of differentially expressed proteins in CSF-derived EV samples (N = 276) compared to non-purified CSF (N = 169), with minimal overlap between both datasets. This finding suggests that CSF-derived EV samples may be more suitable for the discovery phase of a biomarker study, due to the removal of more abundant proteins, resulting in a narrower dynamic range. As disease-specific markers, we selected a total of 39 biomarker candidates identified in non-purified CSF, and 37 biomarker candidates across the different diseases under investigation in the CSF-derived EV data. After further exploration and validation of these proteins, they can be used to further differentiate between the included dementias and may offer new avenues for research into more disease-specific pharmacological therapeutics.

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

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

Misfolded α-Synuclein in Cerebrospinal Fluid of Contact Sport Athletes.

BACKGROUND: Misfolded α-synuclein in Parkinson's disease (PD) and dementia with Lewy bodies (DLB) can be detected using the real-time quaking-induced conversion (RT-QuIC) technique in cerebrospinal fluid (CSF). OBJECTIVES: The objectives are (1) to examine misfolded CSF α-synuclein incidence, and (2) to compare clinical presentation, sports history, brain volumes, and RT-QuIC α-synuclein positivity in former athletes. METHODS: Thirty former athletes with magnetic resonance imaging, neuropsychological testing, and CSF analyzed for phosphorylated tau 181 (p-tau), total tau (t-tau), amyloid-ß 42 (Aß42), and neurofilament light chain (NfL). CSF α-synuclein was detected using RT-QuIC. RESULTS: Six (20%) former athletes were α-synuclein positive. α-Synuclein positive athletes were similar to α-synuclein negative athletes on demographics, sports history, clinical features, CSF p-tau, t-tau, Aß42, and NfL; however, had lower grey matter volumes in the right inferior orbitofrontal, right anterior insula and right olfactory cortices. CONCLUSIONS: α-Synuclein RT-QuIC analysis of CSF may be useful as a prodromal biofluid marker of PD and DLB. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Neurofilament light chain in spinal fluid and plasma in multiple system atrophy: a prospective, longitudinal biomarker study.

PURPOSE: There is a critical need for reliable diagnostic biomarkers as well as surrogate markers of disease progression in multiple system atrophy (MSA). Neurofilament light chain (NfL) has been reported to potentially meet those needs. We therefore sought to explore the value of NfL in plasma (NfL-p) in contrast to cerebrospinal fluid (NfL-c) as a diagnostic marker of MSA, and to assess NfL-p and NfL-c as markers of clinical disease progression. METHODS: Well-characterized patients with early MSA (n = 32), Parkinson's disease (PD; n = 21), and matched controls (CON; n = 15) were enrolled in a prospective, longitudinal study of synucleinopathies with serial annual evaluations. NfL was measured using a high-sensitivity immunoassay, and findings were assessed by disease category and relationship with clinical measures of disease progression. RESULTS: Measurements of NfL-c were highly reproducible across immunoassay platforms (Pearson, r = 0.99), while correlation between NfL-c and -p was only moderate (r = 0.66). NfL was significantly higher in MSA compared with CON and PD; the separation was essentially perfect for NfL-c, but there was overlap, particularly with PD, for NfL-p. While clinical measures of disease severity progressively increased over time, NfL-c and -p remained at stable elevated levels within subjects across serial measurements. Neither change in NfL nor baseline NfL were significantly associated with changes in clinical markers of disease severity. CONCLUSIONS: These findings confirm NfL-c as a faithful diagnostic marker of MSA, while NfL-p showed less robust diagnostic value. The significant NfL elevation in MSA was found to be remarkably stable over time and was not predictive of clinical disease progression.

Genome-Wide Meta-Analysis of Cerebrospinal Fluid Biomarkers in Alzheimer's Disease and Parkinson's Disease Cohorts.

BACKGROUND: Amyloid-ß, phosphorylated tau (p-tau), and total tau (t-tau) in cerebrospinal fluid are established biomarkers for Alzheimer's disease (AD). In other neurodegenerative diseases, such as Parkinson's disease (PD), these biomarkers have also been found to be altered, and the molecular mechanisms responsible for these alterations are still under investigation. Moreover, the interplay between these mechanisms and the diverse underlying disease states remains to be elucidated. OBJECTIVE: To investigate genetic contributions to the AD biomarkers and assess the commonality and heterogeneity of the associations per underlying disease status. METHODS: We conducted genome-wide association studies (GWASs) for the AD biomarkers on subjects from the Parkinson's Progression Markers Initiative, the Fox Investigation for New Discovery of Biomarkers, and the Alzheimer's Disease Neuroimaging Initiative, and meta-analyzed with the largest AD GWAS. We tested heterogeneity of associations of interest between different disease statuses (AD, PD, and control). RESULTS: We observed three GWAS signals: the APOE locus for amyloid-ß, the 3q28 locus between GEMC1 and OSTN for p-tau and t-tau, and the 7p22 locus (top hit: rs60871478, an intronic variant for DNAAF5, also known as HEATR2) for p-tau. The 7p22 locus is novel and colocalized with the brain DNAAF5 expression. Although no heterogeneity from underlying disease status was observed for the earlier GWAS signals, some disease risk loci suggested disease-specific associations with these biomarkers. CONCLUSIONS: Our study identified a novel association at the intronic region of DNAAF5 associated with increased levels of p-tau across all diseases. We also observed some disease-specific genetic associations with these biomarkers. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Seed amplification assay results illustrate discrepancy in Parkinson's disease clinical diagnostic accuracy and error rates.

Parkinson's disease (PD) may be misdiagnosed due to the clinical overlap between PD and atypical parkinsonism. The utility of α-Synuclein (αSyn) Seed Amplification Assay (SAA) as a diagnostic indicator for PD has been reported in numerous studies, but never when administered as a validated clinical laboratory test. This study compares results from αSyn-SAA validation testing performed using well-characterized cohorts from two biorepositories to better understand the accuracy of PD clinical diagnosis. Blinded cerebrospinal fluid (CSF) specimens from a repository that included cohorts of subjects clinically diagnosed as PD or healthy controls, both with confirmatory dopamine transporter single-photon emission computed tomography (DAT SPECT) imaging, and blinded CSF specimens from a repository that included cohorts of subjects clinically diagnosed as PD or healthy controls based on clinical diagnosis alone, were tested as part of the validation studies for the diagnostic αSyn-SAA test (SYNTap® Biomarker Test). Measured αSyn-SAA test accuracy was 83.9% using clinical diagnosis as comparator, and 93.6% using clinical diagnosis with confirmatory DAT- SPECT imaging as comparator. The statistically significant discordance between accuracy determinations using specimens classified using different diagnostic inclusion criteria indicates that there is some symbiosis between dopamine-weighted imaging and αSyn-SAA results, both of which are associated with higher accuracy compared with the clinical diagnosis alone.

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.

Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.

Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta1-42, geriatric depression scale (GDS) scores, and anxiety scores. Interestingly, many of the predictive features in our model have previously been associated with Alzheimer's disease, showing the importance of assessing Alzheimer's disease pathology in patients with Parkinson's disease.