Disease Biomarker Program Research Articles

CISL-PD: A deep learning framework of clinical intervention strategies for Parkinson’s disease based on directional counterfactual Dual GANs

Parkinson’s disease (PD) is a prevalent chronic neurodegenerative disorder characterized by both motor and non-motor symptoms. The significant heterogeneity among PD patients poses a major challenge for treatment interventions. Current clinical interventions for PD primarily target motor symptoms, often neglecting non-motor symptoms, which can lead to unnecessary complications in non-motor symptoms while treating motor symptoms. Therefore, it is crucial to provide comprehensive and precise intervention strategies that encompass both symptom types. To address this issue, we develop a deep learning framework of clinical intervention strategies for PD (CISL-PD) based on counterfactual thinking. This framework introduces Directional Counterfactual Dual Generative Adversarial Networks (DCD-GANs), which apply various counterfactual constraints to longitudinal data to generate practical and plausible counterfactual instances aligned with clinical reality. By analyzing these counterfactual instances and their differences from the original instances, we explore PD intervention strategies with duration-specific fine regulation of multidimensional features. Experiments conducted on 374 PD patients from the Parkinson’s Progression Markers Initiative (PPMI) demonstrate that the counterfactual instances generated by DCD-GANs surpass other state-of-the-art models in terms of similarity (0.307 ± 0.246), sparsity (0.513 ± 0.161), smoothness (0.238 ± 0.135), and trend consistency (0.100 ± 0.089). From these generated counterfactual instances, we develop three clinically feasible intervention strategies that address both motor and non-motor symptoms and identify corresponding patterns of PD with distinct progression differences. Validation on an independent cohort of 351 patients from the National Institute of Neurological Disorders and Stroke Parkinson’s Disease Biomarkers Program (PDBP) confirmed the framework’s robustness and generalizability. By offering precise, multidimensional intervention strategies that can address both motor and non-motor symptoms, the CISL-PD framework has the potential to enhance patient outcomes, reduce complications, improve overall quality of life, and guide clinical decision-making.

Association of CSF α-Synuclein Seeding Amplification Assay Results With Clinical Features of Possible and Probable Dementia With Lewy Bodies.

The clinical diagnosis of dementia with Lewy bodies (DLB) depends on identifying significant cognitive decline accompanied by core features of parkinsonism, visual hallucinations, cognitive fluctuations, and REM sleep behavior disorder (RBD). Hyposmia is one of the several supportive features. α-Synuclein seeding amplification assays (αSyn-SAAs) may enhance diagnostic accuracy by detecting pathologic αSyn seeds in CSF. In this study, we examine how different clinical features associate with CSF αSyn-SAA positivity in a large group of clinically diagnosed participants with DLB. Cross-sectional and longitudinal CSF samples from the multicentered observational cohort study of the DLB Consortium and similar studies within the Parkinson's Disease Biomarker Program, contributed by academic medical centers in the United States, underwent αSyn-SAA testing. Participants included those clinically diagnosed with DLB and 2 control cohorts. Associations between core DLB features and olfaction with αSyn-SAA positivity were evaluated using logistic regression. CSF samples from 191 participants diagnosed with DLB (mean age 69.9 ± 6.8, 15% female), 50 age-matched and sex-matched clinical control participants, and 49 younger analytical control participants were analyzed. Seventy-two percent (137/191) of participants with DLB had positive αSyn-SAAs vs 4% of the control groups. Among participants with DLB, those who were αSyn-SAA-positive had lower Montreal Cognitive Assessment scores (18.8 ± 5.7 vs 21.2 ± 5.2, p = 0.01), had worse parkinsonism on the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (33.8 ± 15.1 vs 25.6 ± 16.4, p = 0.001), were more likely to report RBD (114/133 [86%] vs 33/53 [62%], p < 0.0001), and had worse hyposmia on the University of Pennsylvania Smell Identification Test (UPSIT) (94/105 [90%] below 15th percentile vs 14/44 [32%], p < 0.0001). UPSIT percentile had the highest area under the curve (0.87, 95% CI 0.81-0.94) in predicting αSyn-SAA positivity and participants scoring at or below the 15th percentile of age and sex normative values had 18.3 times higher odds (95% CI 7.52-44.6) of having a positive αSyn-SAA test. Among 82 participants with longitudinal CSF samples, 81 (99%) had the same αSyn-SAA result for initial and follow-up specimens. A substantial proportion of clinically diagnosed participants with DLB had negative αSyn-SAA results. Hyposmia was the strongest clinical predictor of αSyn-SAA positivity. Hyposmia and αSyn-SAA may have utility in improving the diagnostic assessment of individuals with potential DLB. This study provided Class III evidence that CSF αSyn-SAA distinguishes patients with clinically diagnosed DLB from normal controls.

Lysosomal Polygenic Burden Drives Cognitive Decline in Parkinson's Disease with Low Alzheimer Risk.

Genetics influence cognitive progression in Parkinson's disease, possibly through mechanisms related to Lewy and Alzheimer's disease pathology. Lysosomal polygenic burden has recently been linked to more severe Lewy pathology post mortem. To assess the influence of lysosomal polygenic burden on cognitive progression in Parkinson's disease patients with low Alzheimer's disease risk. Using Cox regression we assessed association between lysosomal polygenic scores and time to Montreal Cognitive Assessment score ≤ 21 in the Parkinson's Progression Markers Initiative cohort (n = 374), with replication in data from the Parkinson's Disease Biomarker Program (n = 777). Patients were stratified by Alzheimer's disease polygenic risk. The lysosomal polygenic score was associated with faster progression of cognitive decline in patients with low Alzheimer's disease risk in both datasets (P = 0.0032 and P = 0.0054, respectively). Our study supports complex interplay between genetics and neuropathology in Parkinson's disease-related cognitive impairment, emphasizing the role of lysosomal polygenic burden. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Inflammatory Blood Biomarkers Are Associated with Long-Term Clinical Disease Severity in Parkinson's Disease.

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.

Peripheral Sphingolipids as Potential Biomarkers of Parkinson disease Including Sex-Related Differences (P3-11.007)

<h3>Objective:</h3> Assessment of glucocerebrosidase pathways to better understand the pathophysiology of sphingolipids in Parkinson Disease (PD) associated with a GBA variant (GBAPD), including sex-specific features. <h3>Background:</h3> Sex-specific differences in both idiopathic PD and GBAPD have been reported. Determining blood-based measures in the glucocerebrosidase pathway, which is perturbed in GBAPD might be useful in understanding these differences. <h3>Design/Methods:</h3> Tandem mass spectrometry was utilized to assess levels of targeted sphingolipid substrates and products in 109 GBAPD (62 men, 47 women) and 118 controls (50 men, 68 women) evaluated from four major studies (Mount Sinai (Beth Israel), Parkinson Disease Biomarker Program, Harvard Biomarker Study, BioFIND). <h3>Results:</h3> GBAPD had elevated hexosylceramides when compared to controls (mean [SD] 652.6 [188.3] pmol/100 μl plasma vs 586.2 [172.8]; p=0.006), while ceramide was reduced compared to controls (498.5 [118.7] vs 530.6 [110.9]; p=0.036). Very long chain ceramides, in particular, were decreased in GBAPD compared to controls (420.16 [101.58] vs 448.53 [98.68]; p=0.034). Levels of hexosylsphingosine were increased in GBAPD compared controls (0.08 [0.04] vs. 0.07 [0.04]; p=0.001). Sex-specific differences in measured levels were found. While Hexosylceramides were increased in GBAPD compared to controls, this increase was driven by women, but not men. Alternatively, while ceramides and were decreased in GBAPD compared to controls, this decrease was driven by men and not by women. Finally, while levels of hexosylsphingosine were increased in GBAPD compared to controls, this difference was driven by GBAPD men compared controls, but not by GBAPD women compared to controls. <h3>Conclusion:</h3> While there are differences between GBAPD and controls that are detected in plasma, these depend on sex and suggest that men are preferentially shunting from hexosylceramide to hexosylsphingosine whereas women to ceramides. This supports sex-related pathophysiologic differences, and randomization schema for GBA related studies may consider accounting for sex. <b>Disclosure:</b> Roberto Ortega has nothing to disclose. Prof. Burbulla has nothing to disclose. Izolda Mileva has nothing to disclose. Mr. Taylor has nothing to disclose. Dr. Cuperlovic-Culf has nothing to disclose. Ms. Glickman has nothing to disclose. The institution of Dr. Pullman has received research support from Empire Clinical Research Investigator Program. Dr. Scherzer has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Sanofi. The institution of Dr. Scherzer has received research support from NIH. The institution of Dr. Scherzer has received research support from Michael J. Fox Foundation. The institution of Dr. Scherzer has received research support from American Parkinson’s Disease Association. Dr. Scherzer has received intellectual property interests from a discovery or technology relating to health care. The institution of Ms. Raymond has received research support from NIH. Gabriel Miltenyi-Miltenberger has nothing to disclose. Dr. Yoo has received research support from Edmond J Safra Foundation. William Nichols has nothing to disclose. Dr. Krainc has nothing to disclose. The institution of Dr. Bressman has received research support from Michael J Fox Foundation . The institution of Dr. Bressman has received research support from NIH . Lina Obeid has nothing to disclose. Yusuf Hannun, 9207 has nothing to disclose. The institution of Dr. Bennett has received research support from Prevail. The institution of Dr. Saunders-Pullman has received research support from NIH, Bigglesworth Family Foundation, Empire Clinical Research Investigatory Program.

Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts

The clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson’s Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson’s Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care.

APDM gait and balance measures fail to predict symptom progression rate in Parkinson's disease.

Parkinson's disease (PD) results in progressively worsening gait and balance dysfunction that can be measured using computerized devices. We utilized the longitudinal database of the Parkinson's Disease Biomarker Program to determine if baseline gait and balance measures predict future rates of symptom progression. We included 230, 222, 164, and 177 PD subjects with 6, 12, 18, and 24 months of follow-up, respectively, and we defined progression as worsening of the following clinical parameters: MDS-UPDRS total score, Montreal Cognitive Assessment, PDQ-39 mobility subscale, levodopa equivalent daily dose, Schwab and England score, and global composite outcome. We developed ridge regression models to independently estimate how each gait or balance measure, or combination of measures, predicted progression. The accuracy of each ridge regression model was calculated by cross-validation in which 90% of the data were used to estimate the ridge regression model which was then tested on the 10% of data left out. While the models modestly predicted change in outcomes at the 6-month follow-up visit (accuracy in the range of 66-71%) there was no change in the outcome variables during this short follow-up (median change in MDS-UPDRS total score = 0 and change in LEDD = 0). At follow-up periods of 12, 18, and 24 months, the models failed to predict change (accuracy in the held-out sets ranged from 42 to 60%). We conclude that this set of computerized gait and balance measures performed at baseline is unlikely to help predict future disease progression in PD. Research scientists must continue to search for progression predictors to enhance the performance of disease modifying clinical trials.

Genetically-informed prediction of short-term Parkinson’s disease progression

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.

Multi-modality machine learning predicting Parkinson\u2019s 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.

RNA sequencing of whole blood reveals early alterations in immune cells and gene expression in Parkinson's disease.

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.

Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning

Parkinson's disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better patient management and improve clinical trial design. Previous approaches to modelling Parkinson's disease progression assumed static progression trajectories within subgroups and have not adequately accounted for complex medication effects. Our objective was to develop a statistical progression model of Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects. In this longitudinal data study, data were collected for up to 7-years on 423 patients with early Parkinson's disease and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI) longitudinal observational study. A contrastive latent variable model was applied followed by a novel personalised input-output hidden Markov model to define disease states. Clinical significance of the states was assessed using statistical tests on seven key motor or cognitive outcomes (mild cognitive impairment, dementia, dyskinesia, presence of motor fluctuations, functional impairment from motor fluctuations, Hoehn and Yahr score, and death) not used in the learning phase. The results were validated in an independent sample of 610 patients with Parkinson's disease from the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP). PPMI data were download July 25, 2018, medication information was downloaded on Sept 24, 2018, and PDBP data were downloaded between June 15 and June 24, 2020. The model discovered eight disease states, which are primarily differentiated by functional impairment, tremor, bradykinesia, and neuropsychiatric measures. State 8, the terminal state, had the highest prevalence of key clinical outcomes including 18 (95%) of 19 recorded instances of dementia. At study outset 4 (1%) of 333 patients were in state 8 and 138 (41%) of 333 patients reached stage 8 by year 5. However, the ranking of the starting state did not match the ranking of reaching state 8 within 5 years. Overall, patients starting in state 5 had the shortest time to terminal state (median 2·75 [95% CI 1·75-4·25] years). We developed a statistical progression model of early Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects. Our predictive model discovered non-sequential, overlapping disease progression trajectories, supporting the use of non-deterministic disease progression models, and suggesting static subtype assignment might be ineffective at capturing the full spectrum of Parkinson's disease progression. Michael J Fox Foundation.

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.

β-Glucocerebrosidase activity in GBA-linked Parkinson disease: The type of mutation matters.

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. 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). 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). 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.

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

BackgroundParkinson’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 findingsIn 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.ConclusionsIn 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.

Antibody biomarker for de novo Parkinson disease: attempted validation

Parkinson disease (PD) is a progressive neurodegenerative disease with motor symptoms that result from degeneration of midbrain dopaminergic neurons. Biomarker research seeks to identify the disease during the pre-symptomatic phase, which is a time when therapeutic intervention will be most helpful. Previously, we screened a combinatorial peptoid library to search for antibodies that are present at much higher levels in the serum of PD patients than in control subjects. One such compound, called the PD2 peptoid, was 84% accurate for the identification of de novo PD when employed as the capture agent in an enzyme-linked immunosorbent assay. This peptoid recognized an IgG3 antibody, and IgG3 levels were also found to be significantly higher in PD vs. control serum. In that study we used samples from the NINDS Parkinson’s Disease Biomarker Program. The current study sought to validate that finding using serum samples from de novo and control subjects in the Parkinson’s Progression Markers Initiative study. We found no difference in levels of antibodies captured by the PD2 peptoid in the de novo PD vs. control subjects, and no difference in IgG3 serum levels in the two groups. The failure to replicate our previous study appears to be due to the lack of difference in serum IgG3 levels between the PD and control subjects in the current study.

Evaluation of RNA Blood Biomarkers in the Parkinson's Disease Biomarkers Program.

There is a high misdiagnosis rate between Parkinson’s disease (PD) and atypical parkinsonian disorders (APD), such as progressive supranuclear palsy (PSP), the second most common parkinsonian syndrome. In our earlier studies, we identified and replicated RNA blood biomarkers in several independent cohorts, however, replication in a cohort that includes PSP patients has not yet been performed. To this end, we evaluated the diagnostic potential of nine previously identified RNA biomarkers using quantitative PCR assays in 138 blood samples at baseline from PD, PSP and healthy controls (HCs) nested in the PD Biomarkers Program. Linear discriminant analysis showed that COPZ1 and PTPN1 distinguished PD from PSP patients with 62.5% accuracy. Five biomarkers, PTPN1, COPZ1, FAXDC2, SLC14A1s and NAMPT were useful for distinguishing PSP from controls with 69% accuracy. Several biomarkers correlated with clinical features in PD patients. SLC14A1-s correlated with Unified Parkinson’s Disease Rating Scale total and part III scores. In addition, COPZ1, PTPN1 and MLST8, correlated with Montreal Cognitive Assessment (MoCA). Interestingly, COPZ1, EFTUD2 and PTPN1 were downregulated in cognitively impaired (CI) compared to normal subjects. Linear discriminant analysis showed that age, PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 distinguished CI from normal subjects with 65.9% accuracy. These results suggest that COPZ1 and PTPN1 are useful for distinguishing PD from PSP patients. In addition, the combination of PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 is a useful signature for cognitive impairment. Evaluation of these biomarkers in a larger study will be a key to advancing these biomarkers into the clinic.

Markers of impaired motor and cognitive volition in Parkinson's disease: Correlates of dopamine dysregulation syndrome, impulse control disorder, and dyskinesias

IntroductionDopaminergic therapy in Parkinson's disease (PD) can be associated with both motoric (e.g., dyskinesias) and neuropsychiatric adverse effects. Examples of the latter include Dopamine Dysregulation Syndrome (DDS) and impulse control disorder (ICD), which are separate but related behavioral/psychiatric complications of treatment in PD. Dysregulation of volition characterizes both dyskinesias and DDS/ICD; thus, we analyzed potential disease-related correlates in a large PD cohort. MethodsWe analyzed cross-sectional data from 654 participants collected through the NINDS Parkinson's Disease Biomarkers Program. DDS/ICD symptoms and dyskinesias were assessed using the Movement Disorders Society (revised) Unified Parkinson's Disease Rating Scale. Potential associated variables were selected from PD-validated or PD-specific scales of neuropsychiatric or motoric status. Multivariable models with DDS/ICD or dyskinesia presence outcomes were produced with backward stepwise regression to identify factors independently associated with DDS/ICD and/or dyskinesias. ResultsFifty-three (8.1%) participants endorsed DDS and/or ICD symptoms and 150 (22.9%) were dyskinetic. In multivariable analysis, psychosis was independently associated with both dyskinesias (p = 0.006) and DDS/ICD (p < 0.001). Unpredictable motor fluctuations (p = 0.026) and depression (p = 0.023) were also associated with DDS/ICD; female sex (p = 0.025), low tremor score (p = 0.001) and high akinesia-rigidity score (p < 0.001) were associated with dyskinesias. ConclusionsOur findings suggest that psychosis may be an important marker of impaired volition across motor and cognitive domains. Unpredictable motor fluctuations, psychosis, and depression may together comprise a phenotypic profile of patients at increased risk for DDS/ICD. Similarly, dyskinetic PD patients should be closely monitored for psychotic symptoms and treated appropriately.

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.

Ser(P)-1292 LRRK2 in urinary exosomes is elevated in idiopathic Parkinson's disease.

Mutations in Leucine-rich repeat kinase 2 (LRRK2) enhance levels of the autophosphorylated LRRK2 protein and are the most common known cause of inherited Parkinson's disease (PD). LRRK2 has been further implicated in susceptibility to idiopathic PD in genetic association studies. The objective of this study was to compare autophosphorylated Ser(P)-1292 LRRK2 levels from biobanked urine samples with clinical data in PD patients and controls. Ser(P)-1292 LRRK2 levels were measured from urine exosome fractions from 79 PD patients and 79 neurologically healthy controls enrolled in the Parkinson Disease Biomarker Program at the University of Alabama at Birmingham. Ser(P)-1292 LRRK2 levels were higher in men than women (P < .0001) and elevated in PD patients when compared with controls (P = .0014). Ser(P)-1292 LRRK2 levels were higher in PD cases with worse cognition and correlated with poor performance in MoCA (r = -0.2679 [-0.4628 to -0.0482]), MDS-UPDRS subscales 1 and 2 (r = 0.2239 [0.0014-0.4252], 0.3404 [0.1276-0.5233], respectively), Epworth Sleepiness Scale (r = 0.3215 [0.1066-0.5077]), and Modified Schwab and England Activities of Daily Living Scales (r = -0.4455 [-0.6078 to -0.2475]). Ser(P)-1292 LRRK2 levels predicted those with worse cognitive impairment in PD patients with some success (c = 0.73). Urinary exosome Ser(P)-1292 LRRK2 levels are elevated in idiopathic PD and correlated with the severity of cognitive impairment and difficultly in accomplishing activities of daily living. These results implicate biochemical changes in LRRK2 in idiopathic PD. © 2016 International Parkinson and Movement Disorder Society.

Cognitive impairment in Parkinson's disease: Association between patient-reported and clinically measured outcomes

BackgroundIn Parkinson's disease, the association between objective and patient-reported measures of cognitive dysfunction is unknown and highly relevant to research and clinical care. ObjectiveTo determine which cognitive domain-specific Montreal Cognitive Assessment (MoCA) subscores are most strongly associated with patient-reported cognitive impairment on question 1 (Q1) of the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). MethodsWe analyzed data from 759 PD participants and 481 persons without PD with in a retrospective, cross sectional analysis using data from the NINDS Parkinson's Disease Biomarkers Program (PDBP), a longitudinal, multicenter biomarker study. The relationship between a patient-reported cognitive rating (MDS-UPDRS q1.1) and objective cognitive assessments (MoCA) was assessed using multinomial logistic regression modeling and the outcomes reported as conditional odds ratios (cOR's) representing the relative odds of a participant reporting cognitive impairment that is “slight” versus “normal” on MDS-UPDRSq1.1 for a one unit increase in a MoCA sub-score, adjusted for age and education. ResultsIn PD participants, changes in visuospatial-executive performance and memory had the most significant impact on subjective cognitive impairment. A 1-point increase in visuospatial-executive function decreased the chance of reporting a MDS-UPDRS Q1 score of “slight” versus “normal” by a factor of 0.686 (p < 0.001) and each 1 point improvement in delayed recall decreased the odds of reporting “slight” cognitive impairment by a factor of 0.836 (p < 0.001). ConclusionsConversion from a PD patient's report of “normal” to “slight” cognitive impairment may be associated with changes in visuospatial-executive dysfunction and memory more than other cognitive domains.