The BioFIND study: Characteristics of a clinically typical Parkinson's disease biomarker cohort

A systematic review of salivary biomarkers in Parkinson's disease

The search for reliable and easily accessible biomarkers in Parkinson's disease is receiving a growing emphasis, to detect neurodegeneration from the prodromal phase and to enforce disease-modifying therapies. Despite the need for non-invasively accessible biomarkers, the majority of the studies have pointed to cerebrospinal fluid or peripheral biopsies biomarkers, which require invasive collection procedures. Saliva represents an easily accessible biofluid and an incredibly wide source of molecular biomarkers. In the present study, after presenting the morphological and biological bases for looking at saliva in the search of biomarkers for Parkinson's disease, we systematically reviewed the results achieved so far in the saliva of different cohorts of Parkinson's disease patients. A comprehensive literature search on PubMed and SCOPUS led to the discovery of 289 articles. After screening and exclusion, 34 relevant articles were derived for systematic review. Alpha-synuclein, the histopathological hallmark of Parkinson's disease, has been the most investigated Parkinson's disease biomarker in saliva, with oligomeric alpha-synuclein consistently found increased in Parkinson's disease patients in comparison to healthy controls, while conflicting results have been reported regarding the levels of total alpha-synuclein and phosphorylated alpha-synuclein, and few studies described an increased oligomeric alpha-synuclein/total alpha-synuclein ratio in Parkinson's disease. Beyond alpha-synuclein, other biomarkers targeting different molecular pathways have been explored in the saliva of Parkinson's disease patients: total tau, phosphorylated tau, amyloid-β1-42 (pathological protein aggregation biomarkers); DJ-1, heme-oxygenase-1, metabolites (altered energy homeostasis biomarkers); MAPLC-3beta (aberrant proteostasis biomarker); cortisol, tumor necrosis factor-alpha (inflammation biomarkers); DNA methylation, miRNA (DNA/RNA defects biomarkers); acetylcholinesterase activity (synaptic and neuronal network dysfunction biomarkers); Raman spectra, proteome, and caffeine. Despite a few studies investigating biomarkers targeting molecular pathways different from alpha-synuclein in Parkinson's disease, these results should be replicated and observed in studies on larger cohorts, considering the potential role of these biomarkers in determining the molecular variance among Parkinson's disease subtypes. Although the need for standardization in sample collection and processing, salivary-based biomarkers studies have reported encouraging results, calling for large-scale longitudinal studies and multicentric assessments, given the great molecular potentials and the non-invasive accessibility of saliva.

The combined effect of lifestyle factors and polygenic scores on age at onset in Parkinson's disease

The objective of this study was to investigate the association between a Parkinson's disease (PD)-specific polygenic score (PGS) and protective lifestyle factors on age at onset (AAO) in PD. We included data from 4367 patients with idiopathic PD, 159 patients with GBA1-PD, and 3090 healthy controls of European ancestry from AMP-PD, PPMI, and Fox Insight cohorts. The association between PGS and lifestyle factors on AAO was assessed with linear and Cox proportional hazards models. The PGS showed a negative association with AAO (β = - 1.07, p = 6 × 10-7) in patients with idiopathic PD. The use of one, two, or three of the protective lifestyle factors showed a reduction in the hazard ratio by 21% (p = 0.0001), 44% (p < 2 × 10-16), and 55% (p < 2 × 10-16), compared to no use. An additive effect of aspirin (β = 7.62, p = 9 × 10-7) and PGS (β = - 1.58, p = 0.0149) was found for AAO without an interaction (p = 0.9993) in the linear regressions, and similar effects were seen for tobacco. In contrast, no association between aspirin intake and AAO was found in GBA1-PD (p > 0.05). In our cohort, coffee, tobacco, aspirin, and PGS are independent predictors of PD AAO. Additionally, lifestyle factors seem to have a greater influence on AAO than common genetic risk variants with aspirin presenting the largest effect.

Transcriptional pathobiology and multi-omics predictors for Parkinson's disease

Early diagnosis and biomarker discovery to bolster the therapeutic pipeline for Parkinson's disease (PD) are urgently needed. In this study, we leverage the large-scale whole-blood total RNA-seq dataset from the Accelerating Medicine Partnership in Parkinson's Disease (AMP PD) program to identify PD-associated RNAs, including both known genes and novel circular RNAs (circRNA) and enhancer RNAs (eRNAs). There were 1,111 significant marker RNAs, including 491 genes, 599 eRNAs, and 21 circRNAs, that were first discovered in the PPMI cohort (FDR < 0.05) and confirmed in the PDBP/BioFIND cohorts (nominal p < 0.05). Functional enrichment analysis showed that the PD-associated genes are involved in neutrophil activation and degranulation, as well as the TNF-alpha signaling pathway. We further compare the PD-associated genes in blood with those in post-mortem brain dopamine neurons in our BRAINcode cohort. 44 genes show significant changes with the same direction in both PD brain neurons and PD blood, including neuroinflammation-associated genes IKBIP, CXCR2, and NFKBIB. Finally, we built a novel multi-omics machine learning model to predict PD diagnosis with high performance (AUC = 0.89), which was superior to previous studies and might aid the decision-making for PD diagnosis in clinical practice. In summary, this study delineates a wide spectrum of the known and novel RNAs linked to PD and are detectable in circulating blood cells in a harmonized, large-scale dataset. It provides a generally useful computational framework for further biomarker development and early disease prediction.

Learning Phenotypic Associations for Parkinson's Disease with Longitudinal Clinical Records

Parkinson's disease (PD) is associated with multiple clinical motor and non-motor manifestations. Understanding of PD etiologies has been informed by a growing number of genetic mutations and various fluid-based and brain imaging biomarkers. However, the mechanisms underlying its varied phenotypic features remain elusive. The present work introduces a data-driven approach for generating phenotypic association graphs for PD cohorts. Data collected by the Parkinson's Progression Markers Initiative (PPMI), the Parkinson's Disease Biomarkers Program (PDBP), and the Fox Investigation for New Discovery of Biomarkers (BioFIND) were analyzed by this approach to identify heterogeneous and longitudinal phenotypic associations that may provide insight into the pathology of this complex disease. Findings based on the phenotypic association graphs could improve understanding of longitudinal PD pathologies and how these relate to patient symptomology.

Unveiling Parkinson's disease through biomarker research: current insights and future prospects

Parkinson's disease (PD) is a neurodegenerative condition marked by the gradual depletion of dopaminergic neurons in the substantia nigra. Despite substantial strides in comprehending potential causative mechanisms, the validation of biomarkers with unequivocal evidence for routine clinical application remains elusive. Consequently, the diagnosis heavily relies on patients' clinical assessments and medical backgrounds. The imperative need for diagnostic and prognostic biomarkers arises due to the prevailing limitations of treatments, which predominantly address symptoms without modifying the disease course. This comprehensive review aims to elucidate the existing landscape of diagnostic and prognostic biomarkers for PD, drawing insights from contemporary literature.

Elevated ApoE, ApoJ and lipoprotein-bound α-synuclein levels in cerebrospinal fluid from Parkinson's disease patients - Validation in the BioFIND cohort

BACKGROUND

The progressive accumulation, aggregation, and spread of α-synuclein (aSN) are common hallmarks of Parkinson's disease (PD) pathology. The genotype of apolipoprotein E (ApoE) influences PD progression. Recently we found that aSN co-localize with apolipoproteins on lipoprotein vesicles. We reported an increased level of ApoE, ApoJ and lipoprotein-bound aSN in CSF from early PD patients compared to matched controls. We also found reduced plasma ApoAI in PD patients.

OBJECTIVE

In this study, we used the same approach on the BioFIND cohort to validate our previous results and extended the studies to examine correlations with ApoE genotype, demographic variables, clinical symptoms and other biochemical findings reported in the BioFIND cohort.

METHODS

For the assessment, we used Western-Blot (WB) technique for apolipoproteins measurements in CSF and plasma from PD patients and healthy controls. Further, for measurement of aSN bound to lipoproteins, we combined immunodepletion with the enzyme-linked immunosorbent assay (ELISA).

RESULTS

Levels of ApoE, ApoJ and lipoprotein bound aSN were significantly increased in CSF from PD patients compared to controls. We also observed decreased levels of ApoAI and ApoJ in plasma from PD patients compared to controls.

CONCLUSIONS

Concluding, the present data validated our previous findings. Altered lipoproteins appear to be important in early PD pathology and may be involved in mechanisms underlying aSN cell-to-cell transfer in the nervous system and be developed in algorithms for early diagnosis of PD.

Large-scale proximity extension assay reveals CSF midkine and DOPA decarboxylase as supportive diagnostic biomarkers for Parkinson's disease

BACKGROUND

There is a need for biomarkers to support an accurate diagnosis of Parkinson's disease (PD). Cerebrospinal fluid (CSF) has been a successful biofluid for finding neurodegenerative biomarkers, and modern highly sensitive multiplexing methods offer the possibility to perform discovery studies. Using a large-scale multiplex proximity extension assay (PEA) approach, we aimed to discover novel diagnostic protein biomarkers allowing accurate discrimination of PD from both controls and atypical Parkinsonian disorders (APD).

METHODS

CSF from patients with PD, corticobasal syndrome (CBS), progressive supranuclear palsy (PSP), multiple system atrophy and controls, were analysed with Olink PEA panels. Three cohorts were used in this study, comprising 192, 88 and 36 cases, respectively. All samples were run on the Cardiovascular II, Oncology II and Metabolism PEA panels.

RESULTS

Our analysis revealed that 26 and 39 proteins were differentially expressed in the CSF of test and validation PD cohorts, respectively, compared to controls. Among them, 6 proteins were changed in both cohorts. Midkine (MK) was increased in PD with the strongest effect size and results were validated with ELISA. Another most increased protein in PD, DOPA decarboxylase (DDC), which catalyses the decarboxylation of DOPA (L-3,4-dihydroxyphenylalanine) to dopamine, was strongly correlated with dopaminergic treatment. Moreover, Kallikrein 10 was specifically changed in APD compared with both PD and controls, but unchanged between PD and controls. Wnt inhibitory factor 1 was consistently downregulated in CBS and PSP patients in two independent cohorts.

CONCLUSIONS

Using the large-scale PEA approach, we have identified potential novel PD diagnostic biomarkers, most notably MK and DDC, in the CSF of PD patients.

The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson's disease data

Open science and collaboration are necessary to facilitate the advancement of Parkinson's disease (PD) research. Hackathons are collaborative events that bring together people with different skill sets and backgrounds to generate resources and creative solutions to problems. These events can be used as training and networking opportunities, thus we coordinated a virtual 3-day hackathon event, during which 49 early-career scientists from 12 countries built tools and pipelines with a focus on PD. Resources were created with the goal of helping scientists accelerate their own research by having access to the necessary code and tools. Each team was allocated one of nine different projects, each with a different goal. These included developing post-genome-wide association studies (GWAS) analysis pipelines, downstream analysis of genetic variation pipelines, and various visualization tools. Hackathons are a valuable approach to inspire creative thinking, supplement training in data science, and foster collaborative scientific relationships, which are foundational practices for early-career researchers. The resources generated can be used to accelerate research on the genetics of PD.

Diagnostic metabolomic profiling of Parkinson's disease biospecimens

BACKGROUND

Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson's disease (PD) diagnosis.

OBJECTIVE

To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD.

METHODS

Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression.

RESULTS

Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative.

CONCLUSIONS

UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD.

Myeloperoxidase and Advanced Oxidation Protein Products in the Cerebrospinal Fluid in Women and Men with Parkinson's Disease

Background: Myeloperoxidase (MPO) and advanced oxidation protein products, or AOPP (a type of MPO-derived chlorinated adducts), have been implicated in Parkinson´s disease (PD). Human MPO also show sex-based differences in PD. The objective was to study the relationship of MPO and AOPP in the cerebrospinal fluid (CSF) with motor features of idiopathic PD in male and female patients. Methods: MPO concentration and activity and AOPP content were measured in the CSF and serum in 34 patients and 30 controls. CSF leukocytes and the integrity of the blood-brain barrier were evaluated. Correlations of MPO and AOPP with clinical variables were examined. Results: The blood-brain barrier was intact and CSF leukocyte count was normal in all patients. CSF MPO concentration and activity were similar in the cohort of patients and controls, but CSF MPO content was significantly higher in male patients than in PD women (p = 0.0084). CSF MPO concentration correlated with disease duration in male and female patients (p < 0.01). CSF MPO concentration was significantly higher in men with disease duration ≥12 years versus the remainder of the male subjects (p < 0.01). Changes in CSF MPO in women were not significant. Serum MPO concentration and activity were significantly higher in all PD patients relative to controls (p < 0.0001). CSF MPO was not correlated with serum MPO. Serum AOPP were detected in all patients, but CSF AOPP was undetectable in 53% of patients. AOPP were not quantifiable in controls. Conclusions: CSF MPO is not a good biomarker for PD because mean CSF MPO concentration and activity are not different between the cohort of patients and controls. CSF MPO concentration positively correlated with disease duration in men and women, but CSF MPO is significantly enhanced only in male patients with disease duration longer than 12 years. It can be hypothesized that the MPO-related immune response in early-stage PD might be weak in all patients, but then the MPO-related immune response is progressively enhanced in men, not women. Since the blood-brain barrier is intact, and CSF MPO is not correlated with serum MPO, CSF myeloperoxidase would reflect MPO content in brain cells, not blood-derived cells. Finally, serum AOPP was detected in all patients, but not controls, which is consistent with the occurrence of chlorinative stress in blood serum in PD. The study of CSF AOPP as biomarker could not be assessed because the ELISA assay was hampered by its detection limit in the CSF.

Salivary ATP13A2 is a potential marker of therapy-induced motor complications and is expressed by inclusions in submandibulary glands in Parkinson ́s disease

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Patients with motor complications, not the remainder of the patients, show quantifiable levels of salivary ATP13A2.

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Patients with motor complications present high salivary ATP13A2 concentration relative to controls.

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Salivary ATP13A2 content in patients with motor complications positively correlates with levodopa equivalent daily dose and MDS-UPDRS.

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The submandibulary gland in PD patients contains ATP13A2-expressing rounded inclusions of 10–20 µm in diameter.

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This is the first description of ATP13A2-expressing inclusions outside the nervous system in PD patients.

Background

ATP13A2 holds promise as biomarker for Parkinsońs disease (PD). No study has examined how salivary ATP13A2 is related to motor features in idiopathic PD.

Methods

Salivary ATP13A2 concentration was evaluated with ELISA, and statistical correlations of ATP13A2 level with PD parameters were examined. The dose intensity of the dopaminergic medication regimen was expressed as levodopa equivalent daily dose (LEDD). ATP13A2 expression on histological sections of submandibular glands was evaluated using immunohistochemistry.

Results

Salivary ATP13A2 was undetectable in many subjects (28 % of patients, 43.7 % of controls). However, all the patients with motor complications (n = 28) showed quantifiable levels of ATP13A2, that positively correlated with MDS-UPDRS (total, parts III and IV), and LEDD (p < 0.05). Dyskinetic patients showed the highest LEDD values (p < 0.05). The histological study revealed: a) ATP13A2 staining was very intense in duct cells and vascular endothelium, and b) two patterns of ATP13A2-positive deposits are observed: rounded inclusions of 10–20 µm in diameter located in the interlobular tissue of the patients, and amorphous aggregates inside duct lumen in controls and patients.

Conclusions

The sensitivity of the ELISA assay was a major limitation for quantifying ATP13A2. However, salivary ATP13A2 was detected in all patients with motor complications, where a direct relationship among ATP13A2 concentration, levodopa equivalent daily dose, and MDS-UPDRS was found. Therefore, salivary ATP13A2 might be a reliable index of therapy-induced motor complications. ATP13A2 was expressed by rounded inclusions in the submandibulary gland of patients. This is the first description of ATP13A2-positive inclusions outside the nervous system.

Metabolic Profile in Plasma AND CSF of LEVODOPA-induced Dyskinesia in Parkinson's Disease: Focus on Neuroinflammation

The existence of few biomarkers and the lack of a better understanding of the pathophysiology of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD) require new approaches, as the metabolomic analysis, for discoveries. We aimed to identify a metabolic profile associated with LID in patients with PD in an original cohort and to confirm the results in an external cohort (BioFIND). In the original cohort, plasma and CSF were collected from 20 healthy controls, 23 patients with PD without LID, and 24 patients with PD with LID. LC-MS/MS and metabolomics data analysis were used to perform untargeted metabolomics. Untargeted metabolomics data from the BioFIND cohort were analyzed. We identified a metabolic profile associated with LID in PD, composed of multiple metabolic pathways. In particular, the dysregulation of the glycosphingolipid metabolic pathway was more related to LID and was strongly associated with the severity of dyskinetic movements. Furthermore, bile acid biosynthesis metabolites simultaneously found in plasma and CSF have distinguished patients with LID from other participants. Data from the BioFIND cohort confirmed dysregulation in plasma metabolites from the bile acid biosynthesis pathway. There is a distinct metabolic profile associated with LID in PD, both in plasma and CSF, which may be associated with the dysregulation of lipid metabolism and neuroinflammation.

Plasma acyl-carnitines, bilirubin, tyramine and tetrahydro-21-deoxycortisol in Parkinson's disease and essential tremor. A case control biomarker study

BACKGROUND AND PURPOSE

Given the overlapping clinical manifestations and pathology, the differentiation between essential tremor (ET) and Parkinson's disease (PD) is difficult. Our aims were to examine the plasma metabolomics profiling and their association with motor and non-motor symptoms (NMS) in patients with PD, and to determine differences between de novo PD compared to moderate-advanced PD vs. controls and patients with ET.

METHODS

Plasma samples were collected from 137 subjects including 35 age matched controls, 29 NOVO-PD, 35 PD and 38 ET patients. PD severity, motor and NMS including cognitive function were assessed using the UPDRS, NMS and PD cognitive rating scales, respectively. Metabolomics analysis was performed by UPLC-ESI-QToF-MS followed by unsupervised multivariate statistics. The area under the curve of the biomarkers according to distribution of their concentrations and the diagnosis of PD (NOVO-PD, advanced PD) vs ET and healthy controls was used as a measurement of diagnostic ability.

RESULTS

Several acyl-carnitines, bilirubin, tyramine and tetrahydro-21-deoxycortisol (THS) presented good predictive accuracy (AUC higher than 0.8) for differentiating de novo PD and advanced PD from controls and ET, suggesting an alteration in the lipid oxidation pathway. In multivariate regression analysis, metabolite levels were not significantly associated with motor and NMS severity in PD.

CONCLUSIONS

Diverse acyl-carnitines, bilirubin, tyramine and some adrenal gland derived metabolites are suggested as potential biomarkers able to distinguish between PD from controls and ET.

Comprehensive subtyping of Parkinson's disease patients with similarity fusion: a case study with BioFIND data

Parkinson’s disease (PD) is a complex neurodegenerative disorder with diverse clinical manifestations. To better understand this disease, research has been done to categorize, or subtype, patients, using an array of criteria derived from clinical assessments and biospecimen analyses. In this study, using data from the BioFIND cohort, we aimed at identifying subtypes of moderate-to-advanced PD via comprehensively considering motor and non-motor manifestations. A total of 103 patients were included for analysis. Through the use of a patient-wise similarity matrix fusion technique and hierarchical agglomerative clustering analysis, three unique subtypes emerged from the clustering results. Subtype I, comprised of 60 patients (~58.3%), was characterized by mild symptoms, both motor and non-motor. Subtype II, comprised of 20 (~19.4%) patients, was characterized by an intermediate severity, with a high tremor score and mild non-motor symptoms. Subtype III, comprised of 23 (~22.3%) patients, was characterized by more severe motor and non-motor symptoms. These subtypes show statistically significant differences when looking at motor (on and off medication) clinical features and non-motor clinical features, while there was no clear difference in demographics, biomarker levels, and genetic risk scores.

Native α-Synuclein, 3-Nitrotyrosine Proteins, and Patterns of Nitro-α-Synuclein-Immunoreactive Inclusions in Saliva and Submandibulary Gland in Parkinson's Disease

Background. Salivary α-synuclein (aSyn) and its nitrated form, or 3-nitrotyrosine-α-synuclein (3-NT-αSyn), hold promise as biomarkers for idiopathic Parkinson's disease (IPD). Nitrative stress that is characterized by an excess of 3-nitrotyrosine proteins (3-NT-proteins) has been proposed as a pathogenic mechanism in IPD. The objective is to study the pathological role of native αSyn, 3-NT-αSyn, and 3-NT-proteins in the saliva and submandibulary glands of patients with IPD. Methods. The salivary and serum αSyn and 3-NT-proteins concentration is evaluated with ELISA in patients and controls. Correlations of αSyn and 3-NT-proteins content with clinical features of the disease are examined. Immunohistochemical 3-NT-αSyn expression in submandibulary gland sections is analyzed. Results. (a) Salivary concentration and saliva/serum ratios of native αSyn and 3-NT-proteins are similar in patients and controls; (b) salivary αSyn and 3-NT-proteins do not correlate with any clinical feature; and (c) three patterns of 3-NT-αSyn-positive inclusions are observed on histological sections: rounded "Lewy-type" aggregates of 10-25 µm in diameter, coarse deposits with varied morphology, and spheroid inclusions or bodies of 3-5 µm in diameter. "Lewy-type" and coarse inclusions are observed in the interlobular connective tissue of the gland, and small-sized bodies are located within the cytoplasm of duct cells. "Lewy-type" inclusions are only observed in patients, and the remaining patterns of inclusions are observed in both the patients and controls. Conclusions. The patients' saliva presents a similar concentration of native αSyn and 3-nitrotyrosine-proteins than that of the controls, and no correlations with clinical features are found. These findings preclude the utility of native αSyn in the saliva as a biomarker, and they indicate the absence of nitrative stress in the saliva and serum of patients. As regards nitrated αSyn, "Lewy-type" inclusions expressing 3-NT-αSyn are observed in the patients, not the controls-a novel finding that suggests that a biopsy of the submandibulary gland, if proven safe, could be a useful technique for diagnosing IPD. Finally, to our knowledge, this is also the first description of 3-NT-αSyn-immunoreactive intracytoplasmic bodies in cells that are located outside the nervous system. These intracytoplasmic bodies are present in duct cells of submandibulary gland sections from all subjects regardless of their pathology, and they can represent an aging or involutional change. Further immunostaining studies with different antibodies and larger samples are needed to validate the data.

Cerebrospinal fluid lactoperoxidase level is enhanced in idiopathic Parkinson's disease, and correlates with levodopa equivalent daily dose

Lactoperoxidase (LPO) is proposed to play a role in the pathogenesis of Parkinson's disease (PD). This enzyme has been reported to be enhanced in the cerebrospinal fluid (CSF) in parkinsonian patients. The objective was to look at the relationship of LPO in the CSF and serum with clinical features of idiopathic PD. LPO concentration was analyzed through ELISA techniques. Correlation of CSF or serum LPO and MDS-UPDRS, dopaminergic medication, and other clinical parameters was examined. The findings revealed that LPO concentration in the CSF, not serum, was found to be elevated in patients with PD relative to controls (p < 0.001). CSF LPO concentration negatively correlated with MDS-UPDRS part-IV score (p < .0001), a rating scale that allows evaluating motor complications. CSF LPO level inversely correlated with the dose intensity of the dopaminergic medication regimen, as evaluated with levodopa equivalent dose or LED (mg/day; p < .0001). LED value positively correlated with MDS-UPDRS part-IV score (p < .0001). To sum up, the findings indicate that CSF LPO is found to be elevated in the CSF of PD patients, and this enzyme holds promise as potential biomarker for diagnosis of PD. Increasing the dose intensity of the dopaminergic medication regimen attenuates the elevation in LPO levels in the CSF, and it facilitates the development of motor complications in patients. The pathophysiological mechanisms that seem to be responsible for LPO increase would include dopamine deficiency, oxidative stress, and less likely, microbial infection.

A rapid α-synuclein seed assay of Parkinson's disease CSF panel shows high diagnostic accuracy

BACKGROUND

Assays that specifically measure α-synuclein seeding activity in biological fluids could revolutionize the diagnosis of Parkinson's disease. Recent improvements in α-synuclein real-time quaking-induced conversion assays of cerebrospinal fluid have dramatically reduced reaction times from 5-13 days down to 1-2 days.

OBJECTIVE

To test our improved assay against a panel of cerebrospinal fluid specimens from patients with Parkinson's disease and healthy controls from the MJ Fox Foundation/NINDS BioFIND collection.

METHODS

Specimens collected from healthy controls and patients with clinically typical moderate-to-advanced Parkinson's disease were tested without prior knowledge of disease status. Correlative analyses between assay parameters and clinical measures were performed by an independent investigator.

RESULTS

BioFIND samples gave positive signals in 105/108 (97%) Parkinson's disease cases versus 11/85 (13%) healthy controls. Receiver operating characteristic analyses of diagnosis of cases versus healthy controls gave areas under the curve of 95%. Beyond binary positive/negative determinations, only weak correlations were observed between various assay response parameters and Parkinson's disease clinical measures or other cerebrospinal fluid analytes. Of note, REM sleep behavioral disorder questionnaire scores correlated with the reaction times needed to reach 50% maximum fluorescence. Maximum fluorescence was inversely correlated with Unified Parkinson's Disease Rating Scale motor scores, which was driven by the patients without REM sleep behavioral disorder.

CONCLUSIONS

Our improved α-synuclein seed amplification assay dramatically reduces the time needed to diagnose Parkinson's disease while maintaining the high-performance standards associated with previous α-synuclein seed assays, supporting the clinical utility of this assay for Parkinson's disease diagnosis.

Whole Clinic Research Enrollment in Parkinson's Disease: The Molecular Integration in Neurological Diagnosis (MIND) Study

BACKGROUND

Observational studies in Parkinson's disease (PD) have focused on relatively small numbers of research participants who are studied extensively. The Molecular Integration in Neurological Diagnosis Initiative at the University of Pennsylvania aims to characterize molecular and clinical features of PD in every patient in a large academic center.

OBJECTIVE

To determine the feasibility and interest in a global-capture biomarker research protocol. Additionally, to describe the clinical characteristics and GBA and LRRK2 variant carrier status among participants.

METHODS

All patients at UPenn with a clinical diagnosis of PD were eligible. Informed consent included options for access to the medical record, future recontact, and use of biosamples for additional studies. A blood sample and a completed questionnaire were obtained from participants. Targeted genotyping for four GBA and eight LRRK2 variants was performed, with plasma and DNA banked for future research.

RESULTS

Between September 2018 and December 2019, 704 PD patients were approached for enrollment; 652 (92.6%) enrolled, 28 (3.97%) declined, and 24 (3.41%) did not meet eligibility criteria. Median age was 69 (IQR 63_75) years, disease duration was 5.41 (IQR 2.49_9.95) years, and 11.10%of the cohort was non-white. Disease risk-associated variants in GBA were identified in 39 participants (5.98%) and in LRRK2 in 16 participants (2.45%).

CONCLUSIONS

We report the clinical and genetic characteristics of PD patients in an all-comers, global capture protocol from an academic center. Patient interest in participation and yield for identification of GBA and LRRK2 mutation carriers is high, demonstrating feasibility of PD clinic-wide molecular characterization.

Prevalence and Relationship of Rest Tremor and Action Tremor in Parkinson's Disease

Background:

Despite the significance of tremor in Parkinson’s disease (PD) diagnosis, classification, and patient’s quality of life, there is a relative lack of data on prevalence and relationship of different tremor types in PD.

Methods:

The presence of rest tremor (RT) and action tremor (AT; defined as combination of both postural and kinetic tremor) was determined and RT severity was defined using the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) at baseline in the Progression Marker Initiative (PPMI, n = 423), the Fox Investigation for New Discovery of Biomarkers (BioFIND, n = 118) and the Parkinson’s Disease Biomarkers Program (PDBP, n = 873) cohorts.

Results:

Across baseline data of all three cohorts, RT prevalence (58.2%) was higher than AT prevalence (39.0%). Patients with RT had significantly higher (Chi-square test, p < 0.05) prevalence of AT compared to patients without RT in the PPMI (40.0% versus 30.1%), BioFIND (48.0% versus 40.0%) and PDBP (49.9% versus 21.0%) cohorts. Furthermore, patients with AT had significantly (Student t-test, p < 0.05) higher RT severity that those without AT in PPMI (5.7 ± 5.4 versus 3.9 ± 3.3), BioFIND, 6.4 ± 6.3 versus 3.8 ± 4.4) and PDBP (6.4 ± 6.6 versus 3.7 ± 4.4) cohorts. In the BioFIND cohort, the prevalence of all types of tremor and their combinations significantly decreased from the off-state to on-state.

Discussion:

The RT is the most frequent tremor type and present in more than half of the PD patients. However, AT is also present in nearly one-third of the PD patients. Our results also indicate that RT and AT may have cross-interactions in PD, and that dopaminergic treatment influences both RT and AT.

Mining genetic and transcriptomic data using machine learning approaches in Parkinson's disease

High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson’s disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.

Towards an improved early diagnosis of neurodegenerative diseases: the emerging role of in vitro conversion assays for protein amyloids

Tissue accumulation of abnormal aggregates of amyloidogenic proteins such as prion protein, α-synuclein, and tau represents the hallmark of most common neurodegenerative disorders and precedes the onset of symptoms by years. As a consequence, the sensitive and specific detection of abnormal forms of these proteins in patients' accessible tissues or fluids as biomarkers may have a significant impact on the clinical diagnosis of these disorders. By exploiting seeded polymerization propagation mechanisms to obtain cell-free reactions that allow highly amplified detection of these amyloid proteins, novel emerging in vitro techniques, such as the real-time quaking-induced conversion assay (RT-QuIC) have paved the way towards this important goal. Given its high accuracy in identifying misfolded forms of prion protein from Creutzfeldt-Jakob disease (CJD) CSF, RT-QuIC has already been included in the diagnostic criteria for the clinical diagnosis of sporadic CJD, the most common human prion disease. By showing that this assay may also accurately discriminate between Lewy body disorders and other forms of parkinsonisms or dementias, more recent studies strongly suggested that CSF RT-QuIC can also be successfully applied to synucleinopathies. Finally, preliminary encouraging data also suggested that CSF RT-QuIC might also work for tau protein, and accurately distinguish between 3R- and 4R tauopathies, including Pick's disease, progressive supranuclear palsy, and corticobasal degeneration. Here we will review the state of the art of cell-free aggregation assays, their current diagnostic value and putative limitations, and the future perspectives for their expanded use in clinical practice.

Identification of novel cerebrospinal fluid biomarker candidates for dementia with Lewy bodies: a proteomic approach

BACKGROUND

Diagnosis of dementia with Lewy bodies (DLB) is challenging, largely due to a lack of diagnostic tools. Cerebrospinal fluid (CSF) biomarkers have been proven useful in Alzheimer's disease (AD) diagnosis. Here, we aimed to identify novel CSF biomarkers for DLB using a high-throughput proteomic approach.

METHODS

We applied liquid chromatography/tandem mass spectrometry with label-free quantification to identify biomarker candidates to individual CSF samples from a well-characterized cohort comprising patients with DLB (n = 20) and controls (n = 20). Validation was performed using (1) the identical proteomic workflow in an independent cohort (n = 30), (2) proteomic data from patients with related neurodegenerative diseases (n = 149) and (3) orthogonal techniques in an extended cohort consisting of DLB patients and controls (n = 76). Additionally, we utilized random forest analysis to identify the subset of candidate markers that best distinguished DLB from all other groups.

RESULTS

In total, we identified 1995 proteins. In the discovery cohort, 69 proteins were differentially expressed in DLB compared to controls (p < 0.05). Independent cohort replication confirmed VGF, SCG2, NPTX2, NPTXR, PDYN and PCSK1N as candidate biomarkers for DLB. The downregulation of the candidate biomarkers was somewhat more pronounced in DLB in comparison with related neurodegenerative diseases. Using random forest analysis, we identified a panel of VGF, SCG2 and PDYN to best differentiate between DLB and other clinical groups (accuracy: 0.82 (95%CI: 0.75-0.89)). Moreover, we confirmed the decrease of VGF and NPTX2 in DLB by ELISA and SRM methods. Low CSF levels of all biomarker candidates, except PCSK1N, were associated with more pronounced cognitive decline (0.37 < r < 0.56, all p < 0.01).

CONCLUSION

We identified and validated six novel CSF biomarkers for DLB. These biomarkers, particularly when used as a panel, show promise to improve diagnostic accuracy and strengthen the importance of synaptic dysfunction in the pathophysiology of DLB.

Cerebrospinal fluid proteomics implicates the granin family in Parkinson's disease

Parkinson's disease, the most common age-related movement disorder, is a progressive neurodegenerative disease with unclear etiology. Better understanding of the underlying disease mechanism(s) is an urgent need for the development of disease-modifying therapeutics. Limited studies have been performed in large patient cohorts to identify protein alterations in cerebrospinal fluid (CSF), a proximal site to pathology. We set out to identify disease-relevant protein changes in CSF to gain insights into the etiology of Parkinson's disease and potentially assist in disease biomarker identification. In this study, we used liquid chromatography-tandem mass spectrometry in data-independent acquisition (DIA) mode to identify Parkinson's-relevant biomarkers in cerebrospinal fluid. We quantified 341 protein groups in two independent cohorts (n = 196) and a longitudinal cohort (n = 105 samples, representing 40 patients) consisting of Parkinson's disease and healthy control samples from three different sources. A first cohort of 53 Parkinson's disease and 72 control samples was analyzed, identifying 53 proteins with significant changes (p < 0.05) in Parkinson's disease relative to healthy control. We established a biomarker signature and multiple protein ratios that differentiate Parkinson's disease from healthy controls and validated these results in an independent cohort. The second cohort included 28 Parkinson's disease and 43 control samples. Independent analysis of these samples identified 41 proteins with significant changes. Evaluation of the overlapping changes between the two cohorts identified 13 proteins with consistent and significant changes (p < 0.05). Importantly, we found the extended granin family proteins as reduced in disease, suggesting a potential common mechanism for the biological reduction in monoamine neurotransmission in Parkinson's patients. Our study identifies several novel protein changes in Parkinson's disease cerebrospinal fluid that may be exploited for understanding etiology of disease and for biomarker development.

α-Synuclein in blood cells differentiates Parkinson's disease from healthy controls

OBJECTIVE

To determine whether blood cells expressed α-Syn can differentiate Parkinson's disease (PD) from healthy controls (HC).

METHODS

The concentrations of α-Syn were determined in samples of blood cell pellets using a quantitative Lipid-ELISA assay. In addition, the levels of total protein, hemoglobin, iron and H-ferritin were determined. The study includes samples from the Biofind cohort (n = 46 PD and 45 HC) and results were validated with an additional cohort (n = 35 PD and 28 HC).

RESULTS

A composite biomarker consisting of the concentrations of total α-Syn, proteinase-K resistant (PKres ) α-Syn and phospho-Serine 129 α-Syn (PSer 129), is designed based on the analysis of the discovery BioFIND cohort. This composite biomarker differentiates a PD subgroup, presenting motor symptoms without dementia from a HC group, with a convincing accuracy, represented by an AUC = 0.81 (95% CI, 0.71 to 0.92). Closely similar results were obtained for the validation cohort, that is, AUC = 0.81, (95% CI, 0.70 to 0.94).

INTERPRETATION

Our results demonstrate the potential usefulness of blood cells expressed α-Syn as a biomarker for PD.

Biomarkers for Parkinson's Disease: How Good Are They?

Parkinson's disease (PD) is a complex neurodegenerative disorder with no cure in sight. Clinical challenges of the disease include the inability to make a definitive diagnosis at the early stages and difficulties in predicting the disease progression. The unmet demand to identify reliable biomarkers for early diagnosis and management of the disease course of PD has attracted a lot of attention. However, only a few reported candidate biomarkers have been tried in clinical practice at the present time. Studies on PD biomarkers have often overemphasized the discovery of novel identity, whereas efforts to further evaluate such candidates are rare. Therefore, we update the new development of biomarker discovery in PD and discuss the standard process in the evaluation and assessment of the diagnostic or prognostic value of the identified potential PD biomarkers in this review article. Recent developments in combined biomarkers and the current status of clinical trials of biomarkers as outcome measures are also discussed. We believe that the combination of different biomarkers might enhance the specificity and sensitivity over a single measure that might not be sufficient for such a multiplex disease.

Bringing radiomics into a multi-omics framework for a comprehensive genotype-phenotype characterization of oncological diseases

Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.

Characterization of Parkinson's disease using blood-based biomarkers: A multicohort proteomic analysis

BACKGROUND

Parkinson's disease (PD) is a progressive neurodegenerative disease affecting about 5 million people worldwide with no disease-modifying therapies. We sought blood-based biomarkers in order to provide molecular characterization of individuals with PD for diagnostic confirmation and prediction of progression.

METHODS AND FINDINGS

In 141 plasma samples (96 PD, 45 neurologically normal control [NC] individuals; 45.4% female, mean age 70.0 years) from a longitudinally followed Discovery Cohort based at the University of Pennsylvania (UPenn), we measured levels of 1,129 proteins using an aptamer-based platform. We modeled protein plasma concentration (log10 of relative fluorescence units [RFUs]) as the effect of treatment group (PD versus NC), age at plasma collection, sex, and the levodopa equivalent daily dose (LEDD), deriving first-pass candidate protein biomarkers based on p-value for PD versus NC. These candidate proteins were then ranked by Stability Selection. We confirmed findings from our Discovery Cohort in a Replication Cohort of 317 individuals (215 PD, 102 NC; 47.9% female, mean age 66.7 years) from the multisite, longitudinally followed National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) Cohort. Analytical approach in the Replication Cohort mirrored the approach in the Discovery Cohort: each protein plasma concentration (log10 of RFU) was modeled as the effect of group (PD versus NC), age at plasma collection, sex, clinical site, and batch. Of the top 10 proteins from the Discovery Cohort ranked by Stability Selection, four associations were replicated in the Replication Cohort. These blood-based biomarkers were bone sialoprotein (BSP, Discovery false discovery rate [FDR]-corrected p = 2.82 × 10-2, Replication FDR-corrected p = 1.03 × 10-4), osteomodulin (OMD, Discovery FDR-corrected p = 2.14 × 10-2, Replication FDR-corrected p = 9.14 × 10-5), aminoacylase-1 (ACY1, Discovery FDR-corrected p = 1.86 × 10-3, Replication FDR-corrected p = 2.18 × 10-2), and growth hormone receptor (GHR, Discovery FDR-corrected p = 3.49 × 10-4, Replication FDR-corrected p = 2.97 × 10-3). Measures of these proteins were not significantly affected by differences in sample handling, and they did not change comparing plasma samples from 10 PD participants sampled both on versus off dopaminergic medication. Plasma measures of OMD, ACY1, and GHR differed in PD versus NC but did not differ between individuals with amyotrophic lateral sclerosis (ALS, n = 59) versus NC. In the Discovery Cohort, individuals with baseline levels of GHR and ACY1 in the lowest tertile were more likely to progress to mild cognitive impairment (MCI) or dementia in Cox proportional hazards analyses adjusting for age, sex, and disease duration (hazard ratio [HR] 2.27 [95% CI 1.04-5.0, p = 0.04] for GHR, and HR 3.0 [95% CI 1.24-7.0, p = 0.014] for ACY1). GHR's association with cognitive decline was confirmed in the Replication Cohort (HR 3.6 [95% CI 1.20-11.1, p = 0.02]). The main limitations of this study were its reliance on the aptamer-based platform for protein measurement and limited follow-up time available for some cohorts.

CONCLUSIONS

In this study, we found that the blood-based biomarkers BSP, OMD, ACY1, and GHR robustly associated with PD across multiple clinical sites. Our findings suggest that biomarkers based on a peripheral blood sample may be developed for both disease characterization and prediction of future disease progression in PD.

Motor phenotype classification in moderate to advanced PD in BioFIND study

BACKGROUND

Three motor phenotypes have been described in PD: postural instability and gait difficulty (PIGD) dominant, tremor-dominant (TD), and indeterminate (IND) subtype. These phenotypes have been associated with different cognitive trajectories, motor outcomes, and biomarkers profiles. However, whether motor subtype classifications change with treatment and disease progression is not well established.

METHODS

To evaluate motor subtype ratio changes, we used the chi-square test for the off and on state motor subtypes for 115 PD participants in the BioFIND study and used repeated-measures analyses to evaluate longitudinal changes in 162 PD participants with five-year follow-up in the PPMI study.

RESULTS

PIGD and TD subtypes in moderate to advanced PD participants change with dopaminergic agents. For those who shifted subtypes, improvement in tremor accounted for the transition of 15 (25.4%) TD participants, while the lack of tremor improvement along with minimal changes in PIGD score resulted in changes for eight (19.0%) PIGD individuals. Analyses of PPMI data revealed that all three subgroups had a significant decrease in subtype ratio with disease progression and a significant decline in subtype ratio occurred only in the TD subgroup with dopaminergic agents. The impact of dopaminergic medication effect on subtype shift for each visit was also more notable with disease advancement.

CONCLUSIONS

Motor subtypes are not fixed but change with progression of the disease and with treatment. Improvement in tremor was the main contributor to motor phenotype transitions in the BioFIND cohort. A more stable classification system for subtypes based on underlying biological differences is desirable.

CSF and blood biomarkers for Parkinson's disease

In the management of Parkinson's disease, reliable diagnostic and prognostic biomarkers are urgently needed. The diagnosis of Parkinson's disease mostly relies on clinical symptoms, which hampers the detection of the earliest phases of the disease-the time at which treatment with forthcoming disease-modifying drugs could have the greatest therapeutic effect. Reliable prognostic markers could help in predicting the response to treatments. Evidence suggests potential diagnostic and prognostic value of CSF and blood biomarkers closely reflecting the pathophysiology of Parkinson's disease, such as α-synuclein species, lysosomal enzymes, markers of amyloid and tau pathology, and neurofilament light chain. A combination of multiple CSF biomarkers has emerged as an accurate diagnostic and prognostic model. With respect to early diagnosis, the measurement of CSF α-synuclein aggregates is providing encouraging preliminary results. Blood α-synuclein species and neurofilament light chain are also under investigation because they would provide a non-invasive tool, both for early and differential diagnosis of Parkinson's disease versus atypical parkinsonian disorders, and for disease monitoring. In view of adopting CSF and blood biomarkers for improving Parkinson's disease diagnostic and prognostic accuracy, further validation in large independent cohorts is needed.

Comparative study of cerebrospinal fluid α-synuclein seeding aggregation assays for diagnosis of Parkinson's disease

BACKGROUND

PD diagnosis is based primarily on clinical criteria and can be inaccurate. Biological markers, such as α-synuclein aggregation, that reflect ongoing pathogenic processes may increase diagnosis accuracy and allow disease progression monitoring. Though α-synuclein aggregation assays have been published, reproducibility, standardization, and validation are key challenges for their development as clinical biomarkers.

OBJECTIVE

To cross-validate two α-synuclein seeding aggregation assays developed to detect pathogenic oligomeric α-synuclein species in CSF using samples from the same PD patients and healthy controls from the BioFIND cohort.

METHODS

CSF samples were tested by two independent laboratories in a blinded fashion. BioFIND features standardized biospecimen collection of clinically typical moderate PD patients and nondisease controls. α-synuclein aggregation was measured by protein misfolding cyclic amplification (Soto lab) and real-time quaking-induced conversion (Green lab). Results were analyzed by an independent statistician.

RESULTS

Measuring 105 PD and 79 healthy control CSF samples, these assays showed 92% concordance. The areas under the curve from receiver operating characteristic curve analysis for the diagnosis of PD versus healthy controls were 0.93 for protein misfolding cyclic amplification, 0.89 for real-time quaking-induced conversion, and 0.95 when considering only concordant assay results. Clinical characteristics of false-positive and -negative subjects were not different from true-negative and -positive subjects, respectively.

CONCLUSIONS

These α-synuclein seeding aggregation assays are reliable and reproducible for PD diagnosis. Assay parameters did not correlate with clinical parameters, including disease severity or duration. This assay is highly accurate for PD diagnosis and may impact clinical practice and clinical trials. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Diagnostic biomarkers for Parkinson's disease at a glance: where are we?

Parkinson's disease (PD) is a neurodegenerative disorder whose aetiology remains unclear: degeneration involves several neurotransmission systems, resulting in a heterogeneous disease characterized by motor and non-motor symptoms. PD causes progressive disability that responds only to symptomatic therapies. Future advances include neuroprotective strategies for use in at-risk populations before the clinical onset of disease, hence the continuing need to identify reliable biomarkers that can facilitate the clinical diagnosis of PD. In this evaluative review, we summarize information on potential diagnostic biomarkers for use in the clinical and preclinical stages of PD.

Finding useful biomarkers for Parkinson's disease

The recent advent of an "ecosystem" of shared biofluid sample biorepositories and data sets will focus biomarker efforts in Parkinson's disease, boosting the therapeutic development pipeline and enabling translation with real-world impact.

Cerebrospinal fluid, plasma, and saliva in the BioFIND study: Relationships among biomarkers and Parkinson's disease Features

OBJECTIVE

Examine relationships among neurodegenerative biomarkers and PD motor and nonmotor symptoms.

BACKGROUND

CSF alpha-synuclein is decreased in PD versus healthy controls, but whether plasma and saliva alpha-synuclein differentiate these groups is controversial. Correlations of alpha-synuclein among biofluids (CSF, plasma, saliva) or biomarkers (eg, beta-amyloid, tau [total, phosphorylated]) are not fully understood. The relationships of these biomarkers with PD clinical features remain unclear.

METHODS

BioFIND, a cross-sectional, observational study, examines clinical and biomarker characteristics in moderate-advanced PD and matched healthy controls. We compared alpha-synuclein concentrations across diagnosis, biofluids, and CSF biomarkers. Correlations of CSF biomarkers and MDS-UPDRS, motor phenotype, MoCA, and rapid eye movement sleep behavior disorder questionnaire scores in PD were examined.

RESULTS

CSF alpha-synuclein was lower in PD versus controls (P = .01), controlling for age, gender, and education. Plasma and saliva alpha-synuclein did not differ between PD and controls, and alpha-synuclein did not significantly correlate among biofluids. CSF beta-amyloid_1-42 was lower in PD versus controls (P < .01), and correlated weakly with MoCA recall scores (r = 0.23, P = .02). CSF alpha-synuclein was lower in the postural instability/gait difficulty phenotype than other motor phenotypes (P < .01). No CSF biomarkers predicted or correlated with total motor or rapid eye movement sleep behavior disorder scores. CSF alpha-synuclein correlated with beta-amyloid_1-42 , total-tau, and phosphorylated-tau (r = 0.41, 0.81, 0.43, respectively; Ps < .001).

CONCLUSION

Lower CSF alpha-synuclein is associated with diagnosis and motor phenotype in moderate-advanced PD. Plasma and saliva alpha-synuclein neither correlate with CSF alpha-synuclein, nor distinguish PD from controls. CSF beta-amyloid_1-42 remains a potential biomarker for cognitive impairment in PD. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Parkinson's Disease Biomarkers: Where Are We and Where Do We Go Next?

Background

Objective measures of Parkinson's disease (PD) are needed for purposes of diagnosis and prognostication, as well as identification of those at risk of PD. In this qualitative review, we provide an overview of the current state of the field of PD biomarker development, delineate challenges, and discuss how the field is evolving.

Methods

A search of PubMed was conducted for articles pertaining to objective biomarkers for PD. Articles were selected based on relevance and methodology; where available, meta-analyses, systematic reviews, and comprehensive qualitative review articles were preferentially referenced.

Results

There are several potential sources of objective PD biomarkers including biofluids, peripheral tissue, imaging, genetics, and technology based objective motor testing. Approaches to biomarker identification include the candidate biomarker approach and unbiased discovery methods, each of which has advantages and disadvantages. Several emerging techniques hold promise in each of these areas. Advances in technology and bioinformatics, and the increasing availability of biobanks, are expected to facilitate future PD biomarker development.

Conclusions

The field of objective biomarkers for PD has made great progress but much remains to be done in translating putative biomarkers into tools useful in the clinic and for research. Multimodal biomarker platforms have the potential to capitalize on the utility and strengths of individual biomarkers. Rigorous methodology and standards for replication of findings will be key to meaningful progress in the field.

Cerebrospinal fluid β-glucocerebrosidase activity is reduced in parkinson's disease patients

BACKGROUND

Reduced β-glucocerebrosidase activity was observed in postmortem brains of both GBA1 mutation carrier and noncarrier Parkinson's disease patients, suggesting that lower β-glucocerebrosidase activity is a key feature in the pathogenesis of PD. The objectives of this study were to confirm whether there is reduced β-glucocerebrosidase activity in the CSF of GBA1 mutation carrier and noncarrier PD patients and verify if other lysosomal enzymes show altered activity in the CSF.

METHODS

CSF β-glucocerebrosidase, cathepsin D, and β-hexosaminidase activities were measured in 79 PD and 61 healthy controls from the BioFIND cohort. The whole GBA1 gene was sequenced.

RESULTS

Enzyme activities were normalized according to CSF protein content (specific activity). β-glucocerebrosidase specific activity was significantly decreased in PD versus controls (-28%, P < 0.001). GBA1 mutations were found in 10 of 79 PD patients (12.7%) and 3 of 61 controls (4.9%). GBA1 mutation carrier PD patients showed significantly lower β-glucocerebrosidase specific activity versus noncarriers. β-glucocerebrosidase specific activity was also decreased in noncarrier PD patients versus controls (-25%, P < 0.001). Cathepsin D specific activity was lower in PD versus controls (-21%, P < 0.001). β-Hexosaminidase showed a similar trend. β-Glucocerebrosidase specific activity fairly discriminated PD from controls (area under the curve, 0.72; sensitivity, 0.67; specificity, 0.77). A combination of β-glucocerebrosidase, cathepsin D, and β-hexosaminidase improved diagnostic accuracy (area under the curve, 0.77; sensitivity, 0.71; specificity, 0.85). Lower β-glucocerebrosidase and β-hexosaminidase specific activities were associated with worse cognitive performance.

CONCLUSIONS

CSF β-glucocerebrosidase activity is reduced in PD patients independent of their GBA1 mutation carrier status. Cathepsin D and β-hexosaminidase were also decreased. The possible link between altered CSF lysosomal enzyme activities and cognitive decline deserves further investigation. © 2017 International Parkinson and Movement Disorder Society.

Parkinson's disease biomarkers: perspective from the NINDS Parkinson's Disease Biomarkers Program

Biomarkers for Parkinson's disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset.