Publications from Prior Recipients

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.

Changes in the blood-based RNA transcriptome have the potential to inform biomarkers of Parkinson's disease (PD) progression. Here we sequenced a discovery set of whole-blood RNA species in 4,871 longitudinally collected samples from 1,570 clinically phenotyped individuals from the Parkinson's Progression Marker Initiative (PPMI) cohort. Samples were sequenced to an average of 100 million read pairs to create a high-quality transcriptome. Participants with PD in the PPMI had significantly altered RNA expression (>2,000 differentially expressed genes), including an early and persistent increase in neutrophil gene expression, with a concomitant decrease in lymphocyte cell counts. This was validated in a cohort from the Parkinson's Disease Biomarkers Program (PDBP) consisting of 1,599 participants and by alterations in immune cell subtypes. This publicly available transcriptomic dataset, coupled with available detailed clinical data, provides new insights into PD biological processes impacting whole blood and new paths for developing diagnostic and prognostic PD biomarkers.

PRKN mutations are the most common recessive cause of Parkinson’s disease and are a promising target for gene and cell replacement therapies. Identification of biallelic PRKN patients at the population scale, however, remains a challenge, as roughly half are copy number variants and many single nucleotide polymorphisms are of unclear significance. Additionally, the true prevalence and disease risk associated with heterozygous PRKN mutations is unclear, as a comprehensive assessment of PRKN mutations has not been performed at a population scale. To address these challenges, we evaluated PRKN mutations in two cohorts with near complete genotyping of both single nucleotide polymorphisms and copy number variants: the NIH-PD + AMP-PD cohort, the largest Parkinson’s disease case-control cohort with whole genome sequencing data from 4094 participants, and the UK Biobank, the largest cohort study with whole exome sequencing and genotyping array data from 200 606 participants. Using the NIH-PD participants, who were genotyped using whole genome sequencing, genotyping array, and multi-plex ligation-dependent probe amplification, we validated genotyping array for the detection of copy number variants. Additionally, in the NIH-PD cohort, functional assays of patient fibroblasts resolved variants of unclear significance in biallelic carriers and suggested that cryptic loss of function variants in monoallelic carriers are not a substantial confounder for association studies. In the UK Biobank, we identified 2692 PRKN copy number variants from genotyping array data from nearly half a million participants (the largest collection to date). Deletions or duplications involving exon 2 accounted for roughly half of all copy number variants and the vast majority (88%) involved exons 2, 3, or 4. In the UK Biobank, we found a pathogenic PRKN mutation in 1.8% of participants and two mutations in ∼1/7800 participants. Those with one PRKN pathogenic variant were as likely as non-carriers to have Parkinson’s disease [odds ratio = 0.91 (0.58–1.38), P-value 0.76] or a parent with Parkinson’s disease [odds ratio = 1.12 (0.94–1.31), P-value = 0.19]. Similarly, those in the NIH-PD + AMP + PD cohort with one PRKN pathogenic variant were as likely as non-carriers to have Parkinson’s disease [odds ratio = 1.29 (0.74–2.38), P-value = 0.43]. Together our results demonstrate that heterozygous pathogenic PRKN mutations are common in the population but do not increase the risk of Parkinson’s disease.

Up to 80% of Parkinson's disease patients develop dementia, but time to dementia varies widely from motor symptom onset. Dementia with Lewy bodies presents with clinical features similar to Parkinson's disease dementia, but cognitive impairment precedes or coincides with motor onset. It remains controversial whether dementia with Lewy bodies and Parkinson's disease dementia are distinct conditions or represent part of a disease spectrum. The biological mechanisms underlying disease heterogeneity, in particular the development of dementia, remain poorly understood, but will likely be key to understanding disease pathways and ultimately therapy development. Previous genome-wide association studies in Parkinson's disease and dementia with Lewy bodies/Parkinson's disease dementia have identified risk loci differentiating patients from controls. We collated data for 7,804 patients of European ancestry from Tracking Parkinson's (PRoBaND), The Oxford Discovery Cohort, and AMP-PD. We conducted a discrete phenotype genome-wide association studies comparing Lewy body diseases with and without dementia to decode disease heterogeneity by investigating the genetic drivers of dementia in Lewy body diseases. We found that risk alleles rs429358 tagging APOEe4 and rs7668531 near the MMRN1 and SNCA-AS1 genes, increase the odds of developing dementia and that an intronic variant rs17442721 tagging LRRK2 G2019S, on chromosome 12 is protective against dementia. These results should be validated in autopsy confirmed cases in future studies.

Background: GBA mutations are a common risk factor for Parkinson's disease (PD). A recent study has suggested that GBA haplotypes, identified by intronic variants, can affect age at diagnosis of PD.
Objectives: In this study, we assess this hypothesis using long reads across a large cohort and the publicly available Accelerating Medicines Partnership-Parkinson's Disease (AMP-PD) cohort.
Methods: We recruited a PD cohort through the Remote Assessment of Parkinsonism Supporting Ongoing Development of Interventions in Gaucher Disease study (RAPSODI) and sequenced GBA using Oxford Nanopore technology. Genetic and clinical data on the full AMP-PD cohort were obtained from the online portal of the consortium.
Results: A total of 1417 participants were analyzed. There was no significant difference in age at PD diagnosis between the two main haplotypes of the GBA gene.
Conclusions: GBA haplotypes do not affect age at diagnosis of PD in the two independent cohorts studied. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Parkinson’s disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson’s Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson’s Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66–0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Although many rare variants have been reportedly associated with Parkinson’s disease (PD), many have not been replicated or have failed to replicate. Here, we conduct a large-scale replication of rare PD variants. We assessed a total of 27,590 PD cases, 6701 PD proxies, and 3,106,080 controls from three data sets: 23andMe, Inc., UK Biobank, and AMP-PD. Based on well-known PD genes, 834 variants of interest were selected from the ClinVar annotated 23andMe dataset. We performed a meta-analysis using summary statistics of all three studies. The meta-analysis resulted in five significant variants after Bonferroni correction, including variants in GBA1 and LRRK2. Another eight variants are strong candidate variants for their association with PD. Here, we provide the largest rare variant meta-analysis to date, providing information on confirmed and newly identified variants for their association with PD using several large databases. Additionally we also show the complexities of studying rare variants in large-scale cohorts.

Parkinson’s disease has a large heritable component and genome-wide association studies have identified over 90 variants with disease-associated common variants, providing deeper insights into the disease biology. However, there have not been large-scale rare variant analyses for Parkinson’s disease. To address this gap, we investigated the rare genetic component of Parkinson’s disease at minor allele frequencies <1%, using whole genome and whole exome sequencing data from 7184 Parkinson’s disease cases, 6701 proxy cases and 51 650 healthy controls from the Accelerating Medicines Partnership Parkinson's disease (AMP-PD) initiative, the National Institutes of Health, the UK Biobank and Genentech. We performed burden tests meta-analyses on small indels and single nucleotide protein-altering variants, prioritized based on their predicted functional impact. Our work identified several genes reaching exome-wide significance. Two of these genes, GBA1 and LRRK2, have variants that have been previously implicated as risk factors for Parkinson’s disease, with some variants in LRRK2 resulting in monogenic forms of the disease. We identify potential novel risk associations for variants in B3GNT3, AUNIP, ADH5, TUBA1B, OR1G1, CAPN10 and TREML1 but were unable to replicate the observed associations across independent datasets. Of these, B3GNT3 and TREML1 could provide new evidence for the role of neuroinflammation in Parkinson’s disease. To date, this is the largest analysis of rare genetic variants in Parkinson’s disease.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

Background: A mitochondrial polygenic score (MGS) is composed of genes related to mitochondrial function and found to be associated with Parkinson's disease (PD) risk.
Objective: To investigate the impact of the MGS and lifestyle/environment on age at onset (AAO) in LRRK2 p.Gly2019Ser parkinsonism (LRRK2-PD) and idiopathic PD (iPD).
Methods: We included N = 486 patients with LRRK2-PD and N = 9259 with iPD from the Accelerating Medicines Partnership® Parkinson's Disease Knowledge Platform (AMP-PD), Fox Insight, and a Tunisian Arab-Berber founder population. Genotyping data were used to perform the MGS analysis. Additionally, lifestyle/environmental data were obtained from the PD Risk Factor Questionnaire (PD-RFQ). Linear regression models were used to assess the relationship between MGS, lifestyle/environment, and AAO.
Results: Our derived MGS was significantly higher in PD cases compared with controls (P = 1.1 × 10-8 ). We observed that higher MGS was significantly associated with earlier AAO in LRRK2-PD (P = 0.047, β = -1.40) and there was the same trend with a smaller effect size in iPD (P = 0.231, β = 0.22). There was a correlation between MGS and AAO in LRRK2-PD patients of European descent (P = 0.049, r = -0.12) that was visibly less pronounced in Tunisians (P = 0.449, r = -0.05). We found that the MGS interacted with caffeinated soda consumption (P = 0.003, β = -5.65) in LRRK2-PD and with tobacco use (P = 0.010, β = 1.32) in iPD. Thus, patients with a high MGS had an earlier AAO only if they consumed caffeinated soda or were non-smokers.
Conclusions: The MGS was more strongly associated with earlier AAO in LRRK2-PD compared with iPD. Caffeinated soda consumption or tobacco use interacted with MGS to predict AAO. Our study suggests gene-environment interactions as modifiers of AAO in LRRK2-PD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Keywords: gene-environment interaction; mitochondrial genes; polygenic score.

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

Background: Whole-genome sequencing data are available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and to achieve sufficient power for discoveries, harmonization of multiple cohorts is critical.
Objectives: The Accelerating Medicines Partnership Parkinson's Disease program has developed a research platform for Parkinson's disease (PD) that integrates the storage and analysis of whole-genome sequencing data, RNA expression data, and clinical data, harmonized across multiple cohort studies.
Methods: The version 1 release contains whole-genome sequencing data derived from 3941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these whole-genome sequencing data using the Accelerating Medicines Partnership Parkinson's Disease platform.
Results: The clinical diagnosis of participants in version 1 release includes 2005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit, and 705 participants of genetically enriched cohorts carrying PD risk-associated GBA variants or LRRK2 variants, of whom 304 were affected. We did not observe significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score was higher in PD both in nongenetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry.
Conclusions: We describe the genetic component of the Accelerating Medicines Partnership Parkinson's Disease platform, a solution to democratize data access and analysis for the PD research community. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article is a U.S. Government work and is in the public domain in the USA.

Objective
To test the relationship between clinically relevant types of GBA mutations (none, risk variants, mild mutations, severe mutations) and β-glucocerebrosidase activity in patients with Parkinson disease (PD) in cross-sectional and longitudinal case-control studies.
Methods
A total of 481 participants from the Harvard Biomarkers Study (HBS) and the NIH Parkinson's Disease Biomarkers Program (PDBP) were analyzed, including 47 patients with PD carrying GBA variants (GBA-PD), 247 without a GBA variant (idiopathic PD), and 187 healthy controls. Longitudinal analysis comprised 195 participants with 548 longitudinal measurements over a median follow-up period of 2.0 years (interquartile range, 1–2 years).
Results
β-Glucocerebrosidase activity was low in blood of patients with GBA-PD compared to healthy controls and patients with idiopathic PD, respectively, in HBS (p < 0.001) and PDBP (p < 0.05) in multivariate analyses adjusting for age, sex, blood storage time, and batch. Enzyme activity in patients with idiopathic PD was unchanged. Innovative enzymatic quantitative trait locus (xQTL) analysis revealed a negative linear association between residual β-glucocerebrosidase activity and mutation type with p < 0.0001. For each increment in the severity of mutation type, a reduction of mean β-glucocerebrosidase activity by 0.85 μmol/L/h (95% confidence interval, −1.17, −0.54) was predicted. In a first longitudinal analysis, increasing mutation severity types were prospectively associated with steeper declines in β-glucocerebrosidase activity during a median 2 years of follow-up (p = 0.02).
Conclusions
Residual activity of the β-glucocerebrosidase enzyme measured in blood inversely correlates with clinical severity types of GBA mutations in PD. β-Glucocerebrosidase activity is a quantitative endophenotype that can be monitored noninvasively and targeted therapeutically.

An altered immune response has been identified as a pathophysiological factor in Parkinson's disease (PD). We aimed to identify blood immunity-associated proteins that discriminate PD from controls and that are associated with long-term disease severity in PD patients. Immune response-derived proteins in blood plasma were measured using Proximity Extension Technology by OLINK in a cohort of PD patients (N = 66) and age-matched healthy controls (N = 52). In a selection of 30 PD patients, we evaluated changes in protein levels 7-10 years after the baseline and assessed correlations with motor and cognitive assessments. Data from the Parkinson's Disease Biomarkers Program (PDBP) cohort and the Parkinson's Progression Markers Initiative (PPMI) cohort were used for independent validation. PD patients showed an altered immune response compared to controls based on a panel of four proteins (IL-12B, OPG, CXCL11, and CSF-1). The expression levels of five inflammation-associated proteins (CCL23, CCL25, TNFRSF9, TGF-alpha, and VEGFA) increased over time in PD and were partially associated with more severe motor and cognitive symptoms at follow-up. Increased CCL23 levels were associated with cognitive decline and the APOE4 genotype. Our findings provide further evidence for an altered immune response in PD that is associated with disease severity in PD over a long period of time.

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.

PRKN mutations are the most common cause of young onset and autosomal recessive Parkinson's disease (PD). PRKN is located in FRA6E which is one of the common fragile sites in the human genome, making this region prone to structural variants. However, complex structural variants such as inversions of PRKN are seldom reported, suggesting that there are potentially unrevealed complex pathogenic PRKN structural variants.

Pathogenic missense variants in the leucine-rich repeat kinase 2 (LRRK2) gene have been identified through linkage analysis in familial Parkinson disease (PD). Subsequently, other missense variants with lower effect sizes on PD risk have emerged, as well as non-coding polymorphisms (e.g. rs76904798) enriched in PD cases in genome-wide association studies. Here we leverage recent whole-genome sequences from the Accelerating Medicines Partnership-Parkinson’s Disease (AMP-PD) and the Genome Aggregation (gnomAD) databases to characterize novel missense variants in LRRK2 and explore their relationships with known pathogenic and PD-linked missense variants. Using a computational prediction tool that successfully classifies known pathogenic LRRK2 missense variants, we describe an online web-based resource that catalogs characteristics of over 1200 LRRK2 missense variants of unknown significance. Novel high-pathogenicity scoring variants, some identified exclusively in PD cases, tightly cluster within the ROC-COR-Kinase domains. Structure–function predictions support that some of these variants exert gain-of-function effects with respect to LRRK2 kinase activity. In AMP-PD participants, all p.R1441G carriers (N = 89) are also carriers of the more common PD-linked variant p.M1646T. In addition, nearly all carriers of the PD-linked p.N2081D missense variant are also carriers of the LRRK2 PD-risk variant rs76904798. These results provide a compendium of LRRK2 missense variants and how they associate with one another. While the pathogenic p.G2019S variant is by far the most frequent high-pathogenicity scoring variant, our results suggest that ultra-rare missense variants may have an important cumulative impact in increasing the number of individuals with LRRK2-linked PD

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.

An altered immune response has been identified as a pathophysiological factor in Parkinson's disease (PD). We aimed to identify blood immunity-associated proteins that discriminate PD from controls and that are associated with long-term disease severity in PD patients. Immune response-derived proteins in blood plasma were measured using Proximity Extension Technology by OLINK in a cohort of PD patients (N = 66) and age-matched healthy controls (N = 52). In a selection of 30 PD patients, we evaluated changes in protein levels 7-10 years after the baseline and assessed correlations with motor and cognitive assessments. Data from the Parkinson's Disease Biomarkers Program (PDBP) cohort and the Parkinson's Progression Markers Initiative (PPMI) cohort were used for independent validation. PD patients showed an altered immune response compared to controls based on a panel of four proteins (IL-12B, OPG, CXCL11, and CSF-1). The expression levels of five inflammation-associated proteins (CCL23, CCL25, TNFRSF9, TGF-alpha, and VEGFA) increased over time in PD and were partially associated with more severe motor and cognitive symptoms at follow-up. Increased CCL23 levels were associated with cognitive decline and the APOE4 genotype. Our findings provide further evidence for an altered immune response in PD that is associated with disease severity in PD over a long period of time.

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. DOI: https://doi.org/10.1016/j.parkreldis.2023.105765.

In spinocerebellar ataxia, ataxia onset can be preceded by mild clinical manifestation, cerebellar and/or brainstem alterations, or biomarker modifications. READISCA is a prospective, longitudinal observational study of patients with spinocerebellar ataxia type 1 (SCA1) and 3 (SCA3) to provide essential markers for therapeutic interventions. We looked for clinical, imaging, or biological markers that are present at an early stage of the disease. We enrolled carriers of a pathologic ATXN1 or ATXN3 expansion and controls from 18 US and 2 European ataxia referral centers. Clinical, cognitive, quantitative motor, neuropsychological measures and plasma neurofilament light chain (NfL) measurements were compared between expansion carriers with and without ataxia and controls. We enrolled 200 participants: 45 carriers of a pathologic ATXN1 expansion (31 patients with ataxia [median Scale for the Assessment and Rating of Ataxia: 9; 7-10] and 14 expansion carriers without ataxia [1; 0-2]) and 116 carriers of a pathologic ATXN3 expansion (80 patients with ataxia [7; 6-9] and 36 expansion carriers without ataxia [1; 0-2]). In addition, we enrolled 39 controls who did not carry a pathologic expansion in ATXN1 or ATXN3. Plasma NfL levels were significantly higher in expansion carriers without ataxia than controls, despite similar mean age (controls: 5.7 pg/mL, SCA1: 18.0 pg/mL [p < 0.0001], SCA3: 19.8 pg/mL [p < 0.0001]). Expansion carriers without ataxia differed from controls by significantly more upper motor signs (SCA1 p = 0.0003, SCA3 p = 0.003) and by the presence of sensor impairment and diplopia in SCA3 (p = 0.0448 and 0.0445, respectively). Functional scales, fatigue and depression scores, swallowing difficulties, and cognitive impairment were worse in expansion carriers with ataxia than those without ataxia. Ataxic SCA3 participants showed extrapyramidal signs, urinary dysfunction, and lower motor neuron signs significantly more often than expansion carriers without ataxia. READISCA showed the feasibility of harmonized data acquisition in a multinational network. NfL alterations, early sensory ataxia, and corticospinal signs were quantifiable between preataxic participants and controls. Patients with ataxia differed in many parameters from controls and expansion carriers without ataxia, with a graded increase of abnormal measures from control to preataxic to ataxic cohorts. DOI: https://doi.org/10.1212/WNL.0000000000207088

β-Amyloid (Aβ) plaques can co-occur with Lewy-related pathology in patients with dementia with Lewy bodies (DLB), but Aβ load at prodromal stages of DLB still needs to be elucidated. We investigated Aβ load on PET throughout the DLB continuum, from an early prodromal stage of isolated REM sleep behavior disorder (iRBD) to a stage of mild cognitive impairment with Lewy bodies (MCI-LB), and finally DLB. We performed a cross-sectional study in patients with a diagnosis of iRBD, MCI-LB, or DLB from the Mayo Clinic Alzheimer Disease Research Center. Aβ levels were measured by Pittsburgh compound B (PiB) PET, and global cortical standardized uptake value ratio (SUVR) was calculated. Global cortical PiB SUVR values from each clinical group were compared with each other and with those of cognitively unimpaired (CU) individuals (n = 100) balanced on age and sex using analysis of covariance. We used multiple linear regression testing for interaction to study the influences of sex and APOE ε4 status on PiB SUVR along the DLB continuum. Of the 162 patients, 16 had iRBD, 64 had MCI-LB, and 82 had DLB. Compared with CU individuals, global cortical PiB SUVR was higher in those with DLB (p < 0.001) and MCI-LB (p = 0.012). The DLB group included the highest proportion of Aβ-positive patients (60%), followed by MCI-LB (41%), iRBD (25%), and finally CU (19%). Global cortical PiB SUVR was higher in APOE ε4 carriers compared with that in APOE ε4 noncarriers in MCI-LB (p < 0.001) and DLB groups (p = 0.049). Women had higher PiB SUVR with older age compared with men across the DLB continuum (β estimate = 0.014, p = 0.02). In this cross-sectional study, levels of Aβ load was higher further along the DLB continuum. Whereas Aβ levels were comparable with those in CU individuals in iRBD, a significant elevation in Aβ levels was observed in the predementia stage of MCI-LB and in DLB. Specifically, APOE ε4 carriers had higher Aβ levels than APOE ε4 noncarriers, and women tended to have higher Aβ levels than men as they got older. These findings have important implications in targeting patients within the DLB continuum for clinical trials of disease-modifying therapies.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disabling disorder characterized by disrupted daily patterns of activity, sleep, and physiology. Past studies in ME/CFS patients have examined circadian rhythms, suggested that desynchronization between central and peripheral rhythms may be an important pathological feature, and identified associated changes in post-inflammatory cytokines such as transforming growth factor beta (TGFB). However, no previous studies have examined circadian rhythms in ME/CFS using cellular models or studied the role of cytokines on circadian rhythms. In this study, we used serum samples previously collected from ME/CFS patients (n = 20) selected for the presence of insomnia symptoms and matched controls (n = 20) to determine the effects of serum factors and TGFB on circadian rhythms in NIH3T3 mouse immortalized fibroblasts stably transfected with the Per2-luc bioluminescent circadian reporter. Compared to control serum, ME/CFS serum caused a significant loss of rhythm robustness (decreased goodness of fit) and nominally increased the rate of damping of cellular rhythms. Damping rate was associated with insomnia severity in ME/CFS patients using the Pittsburgh Sleep Quality Index (PSQI). Recombinant TGFB1 peptide applied to cells reduced rhythm amplitude, caused phase delay and decreased robustness of rhythms. However, there was no difference in TGFB1 levels between ME/CFS and control serum indicating the effects of serum on cellular rhythms cannot be explained by levels of this cytokine. Future studies will be required to identify additional serum factors in ME/CFS patients that alter circadian rhythms in cells. DOI: https://doi.org/10.1016/j.jneuroim.2023.578142

To date, no medication has slowed the progression of Parkinson’s disease (PD). Preclinical, epidemiological, and experimental data on humans all support many benefits of endurance exercise among persons with PD. The key question is whether there is a definitive additional benefit of exercising at high intensity, in terms of slowing disease progression, beyond the well-documented benefit of endurance training on a treadmill for fitness, gait, and functional mobility. This study will determine the efficacy of high-intensity endurance exercise as first-line therapy for persons diagnosed with PD within 3 years, and untreated with symptomatic therapy at baseline.

Given the clear role of inflammation in the pathogenesis of Parkinson’s disease (PD) and its impact on incidence and phenotypical characteristics, this review provides an overview with focus on inflammatory biofluid markers in blood and cerebrospinal fluid (CSF) in PD patient cohorts. In preparation for clinical trials targeting the immune system, we specifically address the following questions: 1) What evidence do we have for pro-inflammatory profiles in blood and in CSF of sporadic and genetic PD patients? 2) Is there a role of anti-inflammatory mediators in blood/CSF? 3) Do inflammatory profiles in blood reflect those in CSF indicative of a cross-talk between periphery and brain? 4) Do blood/CSF inflammatory profiles change over the disease course as assessed in repeatedly taken biosamples? 5) Are blood/CSF inflammatory profiles associated with phenotypical trajectories in PD? 6) Are blood/CSF inflammatory profiles associated with CSF levels of neurodegenerative/PD-specific biomarkers? Knowledge on these questions will inform future strategies for patient stratification and cohort enrichment as well as suitable outcome measures for clinical trials.

Parkinson’s disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson’s Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson’s Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66–0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Background. Care management of Parkinson’s disease (PD) patients currently remains symptomatic, mainly because diagnosis relying on the expression of the cardinal motor symptoms is made too late. Earlier detection of PD therefore represents a key step for developing therapies able to delay or slow down its progression. Methods. We investigated metabolic markers in 3 different animal models of PD, mimicking different phases of the disease assessed by behavioral and histological evaluation, and in 3 cohorts of de novo PD patients and matched controls (n = 129). Serum and brain tissue samples were analyzed by nuclear magnetic resonance spectroscopy and data submitted to advanced multivariate statistics. Results. Our translational strategy reveals common metabolic dysregulations in serum of the different animal models and PD patients. Some of them were mirrored in the tissue samples, possibly reflecting pathophysiological mechanisms associated with PD development. Interestingly, some metabolic dysregulations appeared before motor symptom emergence and could represent early biomarkers of PD. Finally, we built a composite biomarker with a combination of 6 metabolites. This biomarker discriminated animals mimicking PD from controls, even from the first, nonmotor signs and, very interestingly, also discriminated PD patients from healthy subjects. Conclusion. From our translational study, which included 3 animal models and 3 de novo PD patient cohorts, we propose a promising biomarker exhibiting a high accuracy for de novo PD diagnosis that may possibly predict early PD development, before motor symptoms appear. Funding. French National Research Agency (ANR), DOPALCOMP, Institut National de la Santé et de la Recherche Médicale, Université Grenoble Alpes, Association France Parkinson.

Introduction: Dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) are characterized by cognitive alterations, visual hallucinations, and motor impairment. Diagnosis is based on type and timing of clinical manifestations; however, determination of clinical subtypes is challenging. The utility of blood DNA methylation as a biomarker for Lewy body disorders (LBD) is mostly unexplored. Methods: We performed a cross-sectional analysis of blood methylation in 42 DLB and 50 PDD cases applying linear models to compare groups and logistic least absolute shrinkage and selection operator regression to explore the discriminant power of methylation signals. Results: DLB blood shows differential methylation compared to PDD. Some methylation changes associate with core features of LBD. Sets of probes show high predictive value to discriminate between variants. Discussion: Our study is the first to explore LBD blood methylation. Despite overlapping clinical presentation, we detected differential epigenetic signatures that, if confirmed in independent cohorts, could be developed into useful biomarkers.

A growing body of evidence suggests nigral iron accumulation plays an important role in the pathophysiology of Parkinson's disease (PD), contributing to dopaminergic neuron loss in the substantia nigra pars compacta (SNc). Converging evidence suggests this accumulation might be related to, or increased by, serotonergic dysfunction, a common, often early feature of the disease. We investigated whether lower plasma serotonin in PD is associated with higher nigral iron. We obtained plasma samples from 97 PD patients and 89 controls and MRI scans from a sub-cohort (62 PD, 70 controls). We measured serotonin concentrations using ultra-high performance liquid chromatography and regional iron content using MRI-based quantitative susceptibility mapping. PD patients had lower plasma serotonin (p< 0.0001) and higher nigral iron content (SNc: p < 0.001) overall. Exclusively in PD, lower plasma serotonin was correlated with higher nigral iron (SNc: r(58)=  − 0.501, p < 0.001). This correlation was signifcant even in patients newly diagnosed (<1 year) and stronger in the SNc than any other region examined. This study reveals an early, linear association between low serotonin and higher nigral iron in PD patients, which is absent in controls. This is consistent with a serotonin-iron relationship in the disease process, warranting further studies to determine its cause and directionality.

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition.

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.

A key driver of patients' well-being and clinical trials for Parkinson's disease (PD) is the course that the disease takes over time (progression and prognosis). To assess how genetic variation influences the progression of PD over time to dementia, a major determinant for quality of life, we performed a longitudinal genome-wide survival study of 11.2 million variants in 3,821 patients with PD over 31,053 visits. We discover RIMS2 as a progression locus and confirm this in a replicate population (hazard ratio (HR) = 4.77, P = 2.78 × 10-11), identify suggestive evidence for TMEM108 (HR = 2.86, P = 2.09 × 10-8) and WWOX (HR = 2.12, P = 2.37 × 10-8) as progression loci, and confirm associations for GBA (HR = 1.93, P = 0.0002) and APOE (HR = 1.48, P = 0.001). Polygenic progression scores exhibit a substantial aggregate association with dementia risk, while polygenic susceptibility scores are not predictive. This study identifies a novel synaptic locus and polygenic score for cognitive disease progression in PD and proposes diverging genetic architectures of progression and susceptibility.

In Huntington’s disease (HD), the mutant Huntingtin (mHTT) is postulated to mediate template‑based aggregation that can propagate across cells. It has been difficult to quantitatively detect such pathological seeding activities in patient biosamples, e.g. cerebrospinal fluids (CSF), and study their correlation with the disease manifestation. Here we developed a cell line expressing a domain‑engineered mHTT‑exon 1 reporter, which showed remarkably high sensitivity and specificity in detecting mHTT seeding species in HD patient biosamples. We showed that the seeding‑competent mHTT species in HD CSF are significantly elevated upon disease onset and with the progression of neuropathological grades. Mechanistically, we showed that mHTT seeding activities in patient CSF could be ameliorated by the overexpression of chaperone DNAJB6 and by antibodies against the polyproline domain of mHTT. Together, our study developed a selective and scalable cell‑based tool to investigate mHTT seeding activities in HD CSF, and demonstrated that the CSF mHTT seeding species are significantly associated with certain disease states. This seeding activity can be ameliorated by targeting specific domain or proteostatic pathway of mHTT, providing novel insights into such pathological activities.

Spinocerebellar ataxias are progressive neurodegenerative disorders primarily affecting the cerebellum. Although the first disease-causing gene was identified nearly 30 years ago, there is no known cure to date, and only a few options exist for symptomatic treatment, with modest effects. The recently developed tools in molecular biology, such as CRISPR/Cas9 and antisense oligonucleotides, can directly act on the disease mechanisms at the genomic or RNA level in disease models. In a nutshell, we are finally just one step away from clinical trials with therapies targeting the underlying genetic cause. However, we still face the challenges for rare neurodegenerative diseases: difficulty in obtaining a large cohort size for sufficient statistical power and the need for biomarkers and clinical outcome assessments (COA) with adequate sensitivity to reflect progression or treatment responses. To overcome these obstacles, ataxia experts form research networks for clinical trial readiness. In this review, we retrace our steps of the collaborative efforts among ataxia researchers in the United States over the years to study and treat these relentless disorders and the future directions of such research networks.

OBJECTIVE: Accurate diagnosis is particularly challenging in Parkinson's disease (PD), multiple system atrophy (MSAp), and progressive supranuclear palsy (PSP). We compare the utility of 3 promising biomarkers to differentiate disease state and explain disease severity in parkinsonism: the Automated Imaging Differentiation in Parkinsonism (AID-P), the Magnetic Resonance Parkinsonism Index (MRPI), and plasma-based neurofilament light chain protein (NfL). METHODS: For each biomarker, the area under the curve (AUC) of receiver operating characteristic curves were quantified for PD versus MSAp/PSP and MSAp versus PSP and statistically compared. Unique combinations of variables were also assessed. Furthermore, each measures association with disease severity was determined using stepwise multiple regression. RESULTS: For PD versus MSAp/PSP, AID-P (AUC, 0.900) measures had higher AUC compared with NfL (AUC, 0.747) and MRPI (AUC, 0.669), P < 0.05. For MSAp versus PSP, AID-P (AUC, 0.889), and MRPI (AUC, 0.824) measures were greater than NfL (AUC, 0.537), P < 0.05. We then combined measures to determine if any unique combination provided enhanced accuracy and found that no combination performed better than the AID-P alone in differentiating parkinsonisms. Furthermore, we found that the AID-P demonstrated the highest association with the MDS-UPDRS (R(adj) (2) -AID-P, 26.58%; NfL,15.12%; MRPI, 12.90%). CONCLUSIONS: Compared with MRPI and NfL, AID-P provides the best overall differentiation of PD versus MSAp/PSP. Both AID-P and MRPI are effective in differentiating MSAp versus PSP. Furthermore, combining biomarkers did not improve classification of disease state compared with using AID-P alone. The findings demonstrate in the current sample that the AID-P and MRPI are robust biomarkers for PD, MSAp, and PSP. © 2020 International Parkinson and Movement Disorder Society.

OBJECTIVE: To test the relationship between clinically relevant types of GBA mutations (none, risk variants, mild mutations, severe mutations) and β-glucocerebrosidase activity in patients with Parkinson disease (PD) in cross-sectional and longitudinal case-control studies. METHODS: A total of 481 participants from the Harvard Biomarkers Study (HBS) and the NIH Parkinson's Disease Biomarkers Program (PDBP) were analyzed, including 47 patients with PD carrying GBA variants (GBA-PD), 247 without a GBA variant (idiopathic PD), and 187 healthy controls. Longitudinal analysis comprised 195 participants with 548 longitudinal measurements over a median follow-up period of 2.0 years (interquartile range, 1-2 years). RESULTS: β-Glucocerebrosidase activity was low in blood of patients with GBA-PD compared to healthy controls and patients with idiopathic PD, respectively, in HBS (p < 0.001) and PDBP (p < 0.05) in multivariate analyses adjusting for age, sex, blood storage time, and batch. Enzyme activity in patients with idiopathic PD was unchanged. Innovative enzymatic quantitative trait locus (xQTL) analysis revealed a negative linear association between residual β-glucocerebrosidase activity and mutation type with p < 0.0001. For each increment in the severity of mutation type, a reduction of mean β-glucocerebrosidase activity by 0.85 μmol/L/h (95% confidence interval, -1.17, -0.54) was predicted. In a first longitudinal analysis, increasing mutation severity types were prospectively associated with steeper declines in β-glucocerebrosidase activity during a median 2 years of follow-up (p = 0.02). CONCLUSIONS: Residual activity of the β-glucocerebrosidase enzyme measured in blood inversely correlates with clinical severity types of GBA mutations in PD. β-Glucocerebrosidase activity is a quantitative endophenotype that can be monitored noninvasively and targeted therapeutically.

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 (PK(res) ) 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.

OBJECTIVES: The cognitive indicators of preclinical behavioral variant Frontotemporal Dementia (bvFTD) have not been identified. To investigate these indicators, we compared cross-sectional performance on a range of cognitive measures in 12 carriers of pathogenic MAPT mutations not meeting diagnostic criteria for bvFTD (i.e., preclinical) versus 32 demographically-matched familial non-carriers (n = 44). Studying preclinical carriers offers a rare glimpse into emergent disease, environmentally and genetically contextualized through comparison to familial controls. METHODS: Evaluating personnel blinded to carrier status administered a standardized neuropsychological battery assessing attention, speed, executive function, language, memory, spatial ability, and social cognition. Results from mixed effect modeling were corrected for multiplicity of comparison by the false discovery rate method, and results were considered significant at p < .05. To control for potential interfamilial variation arising from enrollment of six families, family was treated as a random effect, while carrier status, age, gender, and education were treated as fixed effects. RESULTS: Group differences were detected in 17 of 31 cognitive scores and spanned all domains except spatial ability. As hypothesized, carriers performed worse on specific measures of executive function, and social cognition, but also on measures of attention, speed, semantic processing, and memory storage and retrieval. CONCLUSIONS: Most notably, group differences arose on measures of memory storage, challenging long-standing ideas about the absence of amnestic features on neuropsychological testing in early bvFTD. Current findings provide important and clinically relevant information about specific measures that may be sensitive to early bvFTD, and advance understanding of neurocognitive changes that occur early in the disease. (JINS, 2019, 25, 184-194).

OBJECTIVE: To determine if any association between previously identified alleles that confer risk for Parkinson disease and variables measuring disease progression. METHODS: We evaluated the association between 31 risk variants and variables measuring disease progression. A total of 23,423 visits by 4,307 patients of European ancestry from 13 longitudinal cohorts in Europe, North America, and Australia were analyzed. RESULTS: We confirmed the importance of GBA on phenotypes. GBA variants were associated with the development of daytime sleepiness (p.N370S: hazard ratio [HR] 3.28 [1.69-6.34]) and possible REM sleep behavior (p.T408M: odds ratio 6.48 [2.04-20.60]). We also replicated previously reported associations of GBA variants with motor/cognitive declines. The other genotype-phenotype associations include an intergenic variant near LRRK2 and the faster development of motor symptom (Hoehn and Yahr scale 3.0 HR 1.33 [1.16-1.52] for the C allele of rs76904798) and an intronic variant in PMVK and the development of wearing-off effects (HR 1.66 [1.19-2.31] for the C allele of rs114138760). Age at onset was associated with TMEM175 variant p.M393T (-0.72 [-1.21 to -0.23] in years), the C allele of rs199347 (intronic region of GPNMB, 0.70 [0.27-1.14]), and G allele of rs1106180 (intronic region of CCDC62, 0.62 [0.21-1.03]). CONCLUSIONS: This study provides evidence that alleles associated with Parkinson disease risk, in particular GBA variants, also contribute to the heterogeneity of multiple motor and nonmotor aspects. Accounting for genetic variability will be a useful factor in understanding disease course and in minimizing heterogeneity in clinical trials.

BACKGROUND: Several reports have identified different patterns of Parkinson's disease progression in individuals carrying missense variants in GBA or LRRK2 genes. The overall contribution of genetic factors to the severity and progression of Parkinson's disease, however, has not been well studied. OBJECTIVES: To test the association between genetic variants and the clinical features of Parkinson's disease on a genomewide scale. METHODS: We accumulated individual data from 12 longitudinal cohorts in a total of 4093 patients with 22,307 observations for a median of 3.81 years. Genomewide associations were evaluated for 25 cross-sectional and longitudinal phenotypes. Specific variants of interest, including 90 recently identified disease-risk variants, were also investigated post hoc for candidate associations with these phenotypes. RESULTS: Two variants were genomewide significant. Rs382940(T>A), within the intron of SLC44A1, was associated with reaching Hoehn and Yahr stage 3 or higher faster (hazard ratio 2.04 [1.58-2.62]; P value = 3.46E-8). Rs61863020(G>A), an intergenic variant and expression quantitative trait loci for α-2A adrenergic receptor, was associated with a lower prevalence of insomnia at baseline (odds ratio 0.63 [0.52-0.75]; P value = 4.74E-8). In the targeted analysis, we found 9 associations between known Parkinson's risk variants and more severe motor/cognitive symptoms. Also, we replicated previous reports of GBA coding variants (rs2230288: p.E365K; rs75548401: p.T408M) being associated with greater motor and cognitive decline over time, and an APOE E4 tagging variant (rs429358) being associated with greater cognitive deficits in patients. CONCLUSIONS: We identified novel genetic factors associated with heterogeneity of Parkinson's disease. The results can be used for validation or hypothesis tests regarding Parkinson's disease. © 2019 International Parkinson and Movement Disorder Society.

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.

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.

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e - 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e - 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e - 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis.

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.

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.

BACKGROUND: Loss-of-function mutations in GRN cause frontotemporal lobar degeneration (FTLD). Patients with GRN mutations present with a uniform subtype of TAR DNA-binding protein 43 (TDP-43) pathology at autopsy (FTLD-TDP type A); however, age at onset and clinical presentation are variable, even within families. We aimed to identify potential genetic modifiers of disease onset and disease risk in GRN mutation carriers. METHODS: The study was done in three stages: a discovery stage, a replication stage, and a meta-analysis of the discovery and replication data. In the discovery stage, genome-wide logistic and linear regression analyses were done to test the association of genetic variants with disease risk (case or control status) and age at onset in patients with a GRN mutation and controls free of neurodegenerative disorders. Suggestive loci (p<1 × 10(-5)) were genotyped in a replication cohort of patients and controls, followed by a meta-analysis. The effect of genome-wide significant variants at the GFRA2 locus on expression of GFRA2 was assessed using mRNA expression studies in cerebellar tissue samples from the Mayo Clinic brain bank. The effect of the GFRA2 locus on progranulin concentrations was studied using previously generated ELISA-based expression data. Co-immunoprecipitation experiments in HEK293T cells were done to test for a direct interaction between GFRA2 and progranulin. FINDINGS: Individuals were enrolled in the current study between Sept 16, 2014, and Oct 5, 2017. After quality control measures, statistical analyses in the discovery stage included 382 unrelated symptomatic GRN mutation carriers and 1146 controls free of neurodegenerative disorders collected from 34 research centres located in the USA, Canada, Australia, and Europe. In the replication stage, 210 patients (67 symptomatic GRN mutation carriers and 143 patients with FTLD without GRN mutations pathologically confirmed as FTLD-TDP type A) and 1798 controls free of neurodegenerative diseases were recruited from 26 sites, 20 of which overlapped with the discovery stage. No genome-wide significant association with age at onset was identified in the discovery or replication stages, or in the meta-analysis. However, in the case-control analysis, we replicated the previously reported TMEM106B association (rs1990622 meta-analysis odds ratio [OR] 0·54, 95% CI 0·46-0·63; p=3·54 × 10(-16)), and identified a novel genome-wide significant locus at GFRA2 on chromosome 8p21.3 associated with disease risk (rs36196656 meta-analysis OR 1·49, 95% CI 1·30-1·71; p=1·58 × 10(-8)). Expression analyses showed that the risk-associated allele at rs36196656 decreased GFRA2 mRNA concentrations in cerebellar tissue (p=0·04). No effect of rs36196656 on plasma and CSF progranulin concentrations was detected by ELISA; however, co-immunoprecipitation experiments in HEK293T cells did suggest a direct binding of progranulin and GFRA2. INTERPRETATION: TMEM106B-related and GFRA2-related pathways might be future targets for treatments for FTLD, but the biological interaction between progranulin and these potential disease modifiers requires further study. TMEM106B and GFRA2 might also provide opportunities to select and stratify patients for future clinical trials and, when more is known about their potential effects, to inform genetic counselling, especially for asymptomatic individuals. FUNDING: National Institute on Aging, National Institute of Neurological Disorders and Stroke, Canadian Institutes of Health Research, Italian Ministry of Health, UK National Institute for Health Research, National Health and Medical Research Council of Australia, and the French National Research Agency.

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.