Evidence of brain target engagement in Parkinson's disease and multiple sclerosis by the investigational nanomedicine, CNM-Au8, in the REPAIR phase 2 clinical trials.

BACKGROUND: Impaired brain energy metabolism has been observed in many neurodegenerative diseases, including Parkinson's disease (PD) and multiple sclerosis (MS). In both diseases, mitochondrial dysfunction and energetic impairment can lead to neuronal dysfunction and death. CNM-Au8® is a suspension of faceted, clean-surfaced gold nanocrystals that catalytically improves energetic metabolism in CNS cells, supporting neuroprotection and remyelination as demonstrated in multiple independent preclinical models. The objective of the Phase 2 REPAIR-MS and REPAIR-PD clinical trials was to investigate the effects of CNM-Au8, administered orally once daily for twelve or more weeks, on brain phosphorous-containing energy metabolite levels in participants with diagnoses of relapsing MS or idiopathic PD, respectively. RESULTS: Brain metabolites were measured using 7-Tesla 31P-MRS in two disease cohorts, 11 participants with stable relapsing MS and 13 participants with PD (n = 24 evaluable post-baseline scans). Compared to pre-treatment baseline, the mean NAD+/NADH ratio in the brain, a measure of energetic capacity, was significantly increased by 10.4% after 12 + weeks of treatment with CNM-Au8 (0.584 units, SD: 1.3; p = 0.037, paired t-test) in prespecified analyses of the combined treatment cohorts. Each disease cohort concordantly demonstrated increases in the NAD+/NADH ratio but did not reach significance individually (p = 0.11 and p = 0.14, PD and MS cohorts, respectively). Significant treatment effects were also observed for secondary and exploratory imaging outcomes, including ß-ATP and phosphorylation potential across both cohorts. CONCLUSIONS: Our results demonstrate brain target engagement of CNM-Au8 as a direct modulator of brain energy metabolism, and support the further investigation of CNM-Au8 as a potential disease modifying drug for PD and MS.

Structural MRI Ratios Fail to Distinguish Progressive Supranuclear Palsy From Parkinson Disease in Individual Patients.

Background and Objectives: Parkinson disease (PD) and progressive supranuclear palsy (PSP) are often difficult to differentiate in the clinic. The MR parkinsonism index (MRPI) has been recommended to assist in making this distinction. We aimed to assess the usefulness of this tool in our real-world practice of movement disorders. Methods: We prospectively obtained MRI scans on consecutive patients with movement disorders with a clinical indication for imaging and obtained measures of MRI regions of interest (ROIs) from our neuroradiologists. The authors reviewed all MRI scans and corrected any errors in the original ROI drawings for this analysis. We retrospectively assigned diagnoses using established consensus criteria from progress notes stored in our electronic medical record. We analyzed the data using multinomial logistic regression models and receiver operating curve analysis to determine the predictive accuracy of the MRI ratios. Results: MRI measures and consensus diagnoses were available on 130 patients with PD, 54 with PSP, and 77 diagnosed as other. The out-of-sample prediction error rate of our 5 regression models ranged from 45% to 59%. The average sensitivity and specificity of the 5 models in the testing sample were 53% and 80%, respectively. The positive predictive value of an MRPI ≥13.55 (the published cutoff) in our patients was 79%. Discussion: These results indicate that MRI measures of brain structures were not effective at predicting diagnosis in individual patients. We conclude that the search for a biomarker that can differentiate PSP from PD must continue.

Severity of depressive and motor symptoms impacts quality of life in Parkinson's disease patients at an academic movement clinic: A cross-sectional study.

Introduction: Parkinson's disease (PD) is a neurodegenerative disease with motor and non-motor manifestations that have been previously reported to affect patient quality of life (QoL). Our objective is to investigate the factors that contribute to QoL in a cohort of PD patients receiving care at a major academic institution. Methods: In this cross-sectional study of 124 participants (71.77% male, mean age 65.20, mean UPDRS-III score 11.25), we analyzed if certain clinical features such as UPDRS-III, QIDS-C, and total disease duration contributed to QoL as measured by two different metrics (PDQ-39 and EQ-5D) in PD patients at a university Movement Disorders Clinic. Results: Motor symptoms of PD, with the exception of tremor, as well as depression and specific depressive symptoms were significantly and positively correlated with lower QoL metrics for patients with Parkinson's, with total depressive symptom severity (QIDS-C16 Total score) contributing most to QoL scores. Of the specific depressive and motor symptoms, anhedonia and rigidity contributed the most to QoL. Disease duration was significantly correlated with lower QoL for participants with Parkinson's according to the QoL metric PDQ-39 but not ED-5D. Parkinson's patients with access to high-quality healthcare are at risk for having diminished QoL due to both depressive and motor symptoms. Conclusion: While severity of motor symptoms certainly impacted QoL in our cohort, our findings suggest that depressive symptoms contribute more to impaired QoL than severe motor symptoms do. This phenomenon suggests that concomitant depression in PD as well as one's psychological adjustment to disability may have a greater impact on QoL than severe motor symptoms.

How to manage the initiation of apomorphine therapy without antiemetic pretreatment: A review of the literature.

Introduction: Pretreatment with the antiemetic trimethobenzamide has been recommended practice in the United States (US) to address the risk of nausea and vomiting during initiation of apomorphine treatment. However, trimethobenzamide is no longer being manufactured in the US, and despite the recent update to the US prescribing information, there may be uncertainty regarding how to initiate apomorphine. Methods: To better understand why antiemetic pretreatment was recommended and if it is necessary when initiating apomorphine therapy, we performed a literature review of subcutaneous apomorphine therapy initiation with and without antiemetic pretreatment in patients with PD. Results: Three studies were identified as providing relevant information on antiemetic prophylaxis with initiation of injectable apomorphine. The first study demonstrated that nausea was significantly more common in patients who received 3-days of trimethobenzamide pretreatment compared with those who did not, while the primary endpoint of second study found no significant effect on the binary incidence of nausea and/or vomiting on Day 1 of apomorphine treatment. In the third study, which used a slow titration scheme for apomorphine, transient nausea was reported in just 23.1% of the antiemetic nonusers. Conclusions: Based on the reviewed trials and our clinical experience, we suggest that subcutaneous apomorphine therapy can be initiated using a slow titration scheme without antiemetic pretreatment.

BLENDS: Augmentation of Functional Magnetic Resonance Images for Machine Learning Using Anatomically Constrained Warping.

Introduction: Data augmentation improves the accuracy of deep learning models when training data are scarce by synthesizing additional samples. This work addresses the lack of validated augmentation methods specific for synthesizing anatomically realistic four-dimensional (4D) (three-dimensional [3D] + time) images for neuroimaging, such as functional magnetic resonance imaging (fMRI), by proposing a new augmentation method. Methods: The proposed method, Brain Library Enrichment through Nonlinear Deformation Synthesis (BLENDS), generates new nonlinear warp fields by combining intersubject coregistration maps, computed using symmetric normalization, through spatial blending. These new warp fields can be applied to existing 4D fMRI to create new augmented images. BLENDS was tested on two neuroimaging problems using de-identified data sets: (1) the prediction of antidepressant response from task-based fMRI (original data set n = 163), and (2) the prediction of Parkinson's disease (PD) symptom trajectory from baseline resting-state fMRI regional homogeneity (original data set n = 43). Results: BLENDS readily generates hundreds of new fMRI from existing images, with unique anatomical variations from the source images, that significantly improve prediction performance. For antidepressant response prediction, augmenting each original image once (2 × the original training data) significantly increased prediction R2 from 0.055 to 0.098 (p<1e-6), whereas at 10 × augmentation R2 increased to 0.103. For the prediction of PD trajectory, 10 × augmentation R2 increased from -0.044 to 0.472 (p<1e-6). Conclusions: Augmentation of fMRI through nonlinear transformations with BLENDS significantly improved the performance of deep learning models on clinically relevant predictive tasks. This method will help neuroimaging researchers overcome data set size limitations and achieve more accurate predictive models.

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.

Plasma MIA, CRP, and Albumin Predict Cognitive Decline in Parkinson's Disease.

OBJECTIVE: Using a multi-cohort, discovery-replication-validation design, we sought new plasma biomarkers that predict which individuals with Parkinson's disease (PD) will experience cognitive decline. METHODS: In 108 discovery cohort PD individuals and 83 replication cohort PD individuals, we measured 940 plasma proteins on an aptamer-based platform. Using proteins associated with subsequent cognitive decline in both cohorts, we trained a logistic regression model to predict which patients with PD showed fast (> = 1 point drop/year on Montreal Cognitive Assessment [MoCA]) versus slow (< 1 point drop/year on MoCA) cognitive decline in the discovery cohort, testing it in the replication cohort. We developed alternate assays for the top 3 proteins and confirmed their ability to predict cognitive decline - defined by change in MoCA or development of incident mild cognitive impairment (MCI) or dementia - in a validation cohort of 118 individuals with PD. We investigated the top plasma biomarker for causal influence by Mendelian randomization (MR). RESULTS: A model with only 3 proteins (melanoma inhibitory activity protein [MIA], C-reactive protein [CRP], and albumin) separated fast versus slow cognitive decline subgroups with an area under the curve (AUC) of 0.80 in the validation cohort. The individuals with PD in the validation cohort in the top quartile of risk for cognitive decline based on this model were 4.4 times more likely to develop incident MCI or dementia than those in the lowest quartile. Genotypes at MIA single nucleotide polymorphism (SNP) rs2233154 associated with MIA levels and cognitive decline, providing evidence for MIA's causal influence. CONCLUSIONS: An easily obtained plasma-based predictor identifies individuals with PD at risk for cognitive decline. MIA may participate causally in development of cognitive decline. ANN NEUROL 2022;92:255-269.

Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI.

In resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correction pipelines consisting of head motion, independent components analysis, and non-neuronal physiological signal regressors in sequential or concatenated combinations. The pipelines are evaluated on a dataset of cognitively normal individuals with repeat imaging and on datasets of studies of Autism Spectrum Disorder, Major Depressive Disorder, and Parkinson's Disease. Extensive metrics of motion artifact removal are measured, including resting state network recovery, Quality Control-Functional Connectivity (QC-FC) correlation, distance-dependent artifact, network modularity, and test-retest reliability of multiple rs-fMRI analyses. The results reveal limitations in previously proposed metrics, including the QC-FC correlation and modularity quality, and identify more robust artifact removal metrics. The results also reveal limitations in the concatenated regression approach, which is outperformed by the sequential regression approach in the test-retest reliability metrics. Finally, pipelines are recommended that perform well based on quantitative and qualitative comparisons across multiple datasets and robust metrics. These new insights and recommendations help address the need for effective motion artifact correction to reduce noise and confounds in rs-fMRI.

Lateralization of Motor Signs Affects Symptom Progression in Parkinson Disease.

Background: Asymmetry of motor signs is a cardinal feature of Parkinson disease which may impact phenotypic expression. Objective: To investigate the relationship between lateralization of motor signs and symptom progression and severity during longitudinal observation for up to 4 years in a naturalistic study. Methods: We analyzed data prospectively collected during the NINDS Parkinson Disease Biomarker Project (PDBP). We defined the Movement Disorder Society Revision of the Unified Parkinson Disease Rating Scale (MDS-UPDRS) part II as the primary measure of symptom progression. Left side predominant subjects were those whose lateralized motor scores on the MDS-UPDRS part III were ≥2 points higher on the left side than on the right side of the body. Multiple regression models (controlled for age, gender, education years, ethnicity, levodopa equivalent daily dose (LEDD) at baseline, and years with PD) were used to estimate the rate of symptom progression comparing left predominant (LPD) with non-left predominant (NLPD) subjects. A sensitivity analysis was performed using the same multiple regression models in the subgroups of low (0-26) or high (>27) MDS-UPDRS II score at baseline to determine if PD severity influenced the results. Results: We included 390 participants, 177 LPD and 213 NLPD. We found that MDS-UPDRS part II progression from baseline to 48 months was faster in LPD compared to NLPD (0.6 points per year faster in LPD, p = 0.05). Additionally, the LPD group was statistically significantly worse at baseline and at 48 months in several subparts of the MDS-UPDRS and the Parkinson's Disease Questionnaire-39 (PDQ-39) mobility score. Significantly slower progression (difference of -0.8, p = 0.01) and lower score at 48 months (difference of -3.8, p = 0.003) was seen for NLPD vs. LPD in the group with lower baseline MDS-UPDRS part II score. Conclusion: Left side lateralization was associated with faster symptom progression and worse outcomes in multiple clinical domains in our cohort. Clinicians should consider using motor predominance in their counseling regarding prognosis.

Probable REM Sleep Behavior Disorder Is a Risk Factor for Symptom Progression in Parkinson Disease.

Background: The literature is conflicting on whether rapid eye movement sleep behavior disorder (RBD) is associated with more rapid progression of Parkinson disease (PD). Objective: We aimed to determine (1) how stable probable RBD (pRBD) is over time and (2) whether it predicts faster PD progression. Methods: We evaluated participants in the Parkinson's Disease Biomarker Project (PDBP) who were prospectively assessed every 6-12 months with a series of motor, non-motor, disability, and health status scales. For aim 1, we calculated the incidence and disappearance rates of pRBD and compared stability of pRBD in PD with control subjects. For aim 2, we developed multiple regression models to determine if pRBD at baseline influenced the rate of change or average value at 48 months of 10 outcome variables. Results: We found that pRBD was a less stable diagnosis for PD than controls. In pRBD+ subjects, the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III score progressed 2.78 points per year faster (p < 0.01), MDS-UPDRS total score progressed 3.98 points per year faster (p < 0.01), a global composite outcome (GCO) worsened by 0.09 points per year faster (p = 0.02), and Parkinson's Disease Questionnaire (PDQ-39) mobility score progressed 2.57 percentage points per year faster (p < 0.01). The average scores at 48 months were 8.89 (p = 0.02) and 14.3 (p = 0.01) points higher for pRBD+ in MDS-UPDRS part III and total scores, respectively. Conclusions: Our study confirms that pRBD detected at the start of a study portends more rapid progression of PD. Knowing this could be useful for enriching clinical trials with fast progressors to accelerate discovery of a disease modifying agent.

Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.

INTRODUCTION: Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonance imaging, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF), to predict an individual's current and future severity over up to 4 years and to elucidate the most prognostic brain regions. METHODS: ReHo and fALFF are measured for 82 Parkinson's Disease subjects and used to train machine learning predictors of baseline clinical and future severity at 1 year, 2 years, and 4 years follow-up as measured by the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Predictive performance is measured with nested cross-validation, validated on an external dataset, and again validated through leave-one-site-out cross-validation. Important predictive features are identified. RESULTS: The models explain up to 30.4% of the variance in current MDS-UPDRS scores, 55.8% of the variance in year 1 scores, and 47.1% of the variance in year 2 scores (p < 0.0001). For distinguishing high and low-severity individuals at each timepoint (MDS-UPDRS score above or below the median, respectively), the models achieve positive predictive values up to 79% and negative predictive values up to 80%. Higher ReHo and fALFF in several regions, including components of the default motor network, predicted lower severity across current and future timepoints. CONCLUSION: These results identify an accurate prognostic neuroimaging biomarker which may be used to better inform enrollment in trials of neuroprotective treatments and enable physicians to counsel their patients.

Oral Levodopa Formulation Does Not Affect Progression of Parkinson Disease.

OBJECTIVE: Motor fluctuations develop in most patients treated with carbidopa/levodopa for Parkinson disease. The continuous dopamine stimulation hypothesis suggests that longer-acting forms of levodopa might improve outcomes, but this has been inadequately tested in humans. We undertook to determine if there is any difference in symptom progression rate among patients taking immediate-release carbidopa/levodopa (IR), controlled-release carbidopa/levodopa (CR), or carbidopa/levodopa/entacapone (CLE) using standard outcome measures in a naturalistic study. METHODS: We evaluated Parkinson disease subjects prospectively followed for up to 48 months in the Parkinson's Disease Biomarker Project. Bayesian linear or generalized linear mixed-effects models were developed to determine if oral levodopa formulation influenced the rate of symptom progression as measured by 8 outcome measures. RESULTS: At baseline, the IR, CR, and CLE groups were similar except that the CR group had milder disease and was represented at only 1 site, and the CLE group had a longer disease duration. In the primary analysis, there was no difference in rate of symptom progression as measured by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale Part II, Part IV, or total score. In the secondary exploratory analysis, there was no difference in progression rate as measured by change in levodopa equivalent daily dose, Montreal Cognitive Assessment, Parkinson's Disease Questionnaire mobility subscore, Schwab and England Activities of Daily Living Scale, or a global composite outcome. CONCLUSIONS: We found no difference in symptom progression rate in patients taking IR, CR, or CLE. This clinical observation supports pharmacokinetic studies demonstrating that none of these oral levodopa formulations achieve continuous dopamine stimulation.

Advanced MRI techniques for transcranial high intensity focused ultrasound targeting.

Magnetic resonance guided high intensity focused ultrasound is a novel, non-invasive, image-guided procedure that is able to ablate intracranial tissue with submillimetre precision. It is currently FDA approved for essential tremor and tremor dominant Parkinson's disease. The aim of this update is to review the limitations of current landmark-based targeting techniques of the ventral intermediate nucleus and demonstrate the role of emerging imaging techniques that are relevant for both magnetic resonance guided high intensity focused ultrasound and deep brain stimulation. A significant limitation of standard MRI sequences is that the ventral intermediate nucleus, dentatorubrothalamic tract, and other deep brain nuclei cannot be clearly identified. This paper provides original, annotated images demarcating the ventral intermediate nucleus, dentatorubrothalamic tract, and other deep brain nuclei on advanced MRI sequences such as fast grey matter acquisition T1 inversion recovery, quantitative susceptibility mapping, susceptibility weighted imaging, and diffusion tensor imaging tractography. Additionally, the paper reviews clinical efficacy of targeting with these novel MRI techniques when compared to current established landmark-based targeting techniques. The paper has widespread applicability to both deep brain stimulation and magnetic resonance guided high intensity focused ultrasound.

Prediction of Individual Progression Rate in Parkinson's Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Stability.

Parkinson's disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study was to develop a model using clinical measures and biomechanical measures of gait and postural stability to predict an individual's PD progression over two years. Data from 160 PD subjects were utilized. Machine learning models, including XGBoost and Feed Forward Neural Networks, were developed using extensive model optimization and cross-validation. The highest performing model was a neural network that used a group of clinical measures, achieved a PPV of 71% in identifying fast progressors, and explained a large portion (37%) of the variance in an individual's progression rate on held-out test data. This demonstrates the potential to predict individual PD progression rate and enrich trials by analyzing clinical and biomechanical measures with machine learning.

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

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disease affecting about 5 million people worldwide with no disease-modifying therapies. We sought blood-based biomarkers in order to provide molecular characterization of individuals with PD for diagnostic confirmation and prediction of progression. METHODS AND FINDINGS: In 141 plasma samples (96 PD, 45 neurologically normal control [NC] individuals; 45.4% female, mean age 70.0 years) from a longitudinally followed Discovery Cohort based at the University of Pennsylvania (UPenn), we measured levels of 1,129 proteins using an aptamer-based platform. We modeled protein plasma concentration (log10 of relative fluorescence units [RFUs]) as the effect of treatment group (PD versus NC), age at plasma collection, sex, and the levodopa equivalent daily dose (LEDD), deriving first-pass candidate protein biomarkers based on p-value for PD versus NC. These candidate proteins were then ranked by Stability Selection. We confirmed findings from our Discovery Cohort in a Replication Cohort of 317 individuals (215 PD, 102 NC; 47.9% female, mean age 66.7 years) from the multisite, longitudinally followed National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) Cohort. Analytical approach in the Replication Cohort mirrored the approach in the Discovery Cohort: each protein plasma concentration (log10 of RFU) was modeled as the effect of group (PD versus NC), age at plasma collection, sex, clinical site, and batch. Of the top 10 proteins from the Discovery Cohort ranked by Stability Selection, four associations were replicated in the Replication Cohort. These blood-based biomarkers were bone sialoprotein (BSP, Discovery false discovery rate [FDR]-corrected p = 2.82 × 10-2, Replication FDR-corrected p = 1.03 × 10-4), osteomodulin (OMD, Discovery FDR-corrected p = 2.14 × 10-2, Replication FDR-corrected p = 9.14 × 10-5), aminoacylase-1 (ACY1, Discovery FDR-corrected p = 1.86 × 10-3, Replication FDR-corrected p = 2.18 × 10-2), and growth hormone receptor (GHR, Discovery FDR-corrected p = 3.49 × 10-4, Replication FDR-corrected p = 2.97 × 10-3). Measures of these proteins were not significantly affected by differences in sample handling, and they did not change comparing plasma samples from 10 PD participants sampled both on versus off dopaminergic medication. Plasma measures of OMD, ACY1, and GHR differed in PD versus NC but did not differ between individuals with amyotrophic lateral sclerosis (ALS, n = 59) versus NC. In the Discovery Cohort, individuals with baseline levels of GHR and ACY1 in the lowest tertile were more likely to progress to mild cognitive impairment (MCI) or dementia in Cox proportional hazards analyses adjusting for age, sex, and disease duration (hazard ratio [HR] 2.27 [95% CI 1.04-5.0, p = 0.04] for GHR, and HR 3.0 [95% CI 1.24-7.0, p = 0.014] for ACY1). GHR's association with cognitive decline was confirmed in the Replication Cohort (HR 3.6 [95% CI 1.20-11.1, p = 0.02]). The main limitations of this study were its reliance on the aptamer-based platform for protein measurement and limited follow-up time available for some cohorts. CONCLUSIONS: In this study, we found that the blood-based biomarkers BSP, OMD, ACY1, and GHR robustly associated with PD across multiple clinical sites. Our findings suggest that biomarkers based on a peripheral blood sample may be developed for both disease characterization and prediction of future disease progression in PD.

Finding useful biomarkers for Parkinson's disease.

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

Comparison of Globus Pallidus Interna and Subthalamic Nucleus in Deep Brain Stimulation for Parkinson Disease: An Institutional Experience and Review.

Deep Brain Stimulation (DBS) has revolutionized the lives of patients of Parkinson disease, offering therapeutic options to those not benefiting entirely from medications alone. With its proven track record of outperforming the best medical management, the goal is to unlock the full potential of this therapy. Currently, the Globus Pallidus Interna (GPi) and Subthalamic Nucleus (STN) are both viable targets for DBS, and the choice of site should focus on the constellation of symptoms, both motor and nonmotor, which are key determinants to quality of life. Our article sheds light on the specific advantages and drawbacks of the two sites, highlighting the need for matching the inherent properties of a target with specific desired effects in patients. UT Southwestern Medical Center has a robust and constantly evolving DBS program and the narrative from our center provides invaluable insight into the practical realities of DBS. The ultimate decision in selecting a DBS target is complex, ideally made by a multidisciplinary team, tailored towards each patient's profile and their expectations, by drawing upon scientific evidence coupled with experience. Ongoing research is expanding our knowledge base, which should be dynamically incorporated into an institute's DBS paradigm to ensure that patients receive the optimal therapy.

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.

Caffeine, creatine, GRIN2A and Parkinson's disease progression.

Caffeine is neuroprotective in animal models of Parkinson's disease (PD) and caffeine intake is inversely associated with the risk of PD. This association may be influenced by the genotype of GRIN2A, which encodes an NMDA-glutamate-receptor subunit. In two placebo-controlled studies, we detected no association of caffeine intake with the rate of clinical progression of PD, except among subjects taking creatine, for whom higher caffeine intake was associated with more rapid progression. We now have analyzed data from 420 subjects for whom DNA samples and caffeine intake data were available from a placebo-controlled study of creatine in PD. The GRIN2A genotype was not associated with the rate of clinical progression of PD in the placebo group. However, there was a 4-way interaction between GRIN2A genotype, caffeine, creatine and the time since baseline. Among subjects in the creatine group with high levels of caffeine intake, but not among those with low caffeine intake, the GRIN2A T allele was associated with more rapid progression (p=0.03). These data indicate that the deleterious interaction between caffeine and creatine with respect to rate of progression of PD is influenced by GRIN2A genotype. This example of a genetic factor interacting with environmental factors illustrates the complexity of gene-environment interactions in the progression of PD.