The Parkinson's disease (PD) research field has seen the advent of several promising biomarkers and a deeper understanding of the clinical features of the disease from the earliest stages of pathology to manifest disease. Despite progress, a biologically based PD staging system does not exist. Such staging would be a useful framework within which to model the disease, develop and validate biomarkers, guide therapeutic development, and inform clinical trials design. We propose that the presence of aggregated neuronal α-synuclein, dopaminergic neuron dysfunction/degeneration, and clinical signs and symptoms identifies a group of individuals that have Lewy body pathology, which in early stages manifests with what is now referred to as prodromal non-motor features and later stages with the manifestations of PD and related Lewy body diseases as defined by clinical diagnostic criteria. Based on the state of the field, we herein propose a definition and staging of PD based on biology. We present the biologic basis for such a staging system and review key assumptions and evidence that support the proposed approach. We identify gaps in knowledge and delineate crucial research priorities that will inform the ultimate integrated biologic staging system for PD.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Objective

To examine the baseline prevalence and longitudinal evolution in non-motor symptoms (NMS) in a prospective cohort of, at baseline, patients with de novo Parkinson's disease (PD) compared with healthy controls (HC).

Methods

Parkinson's Progression Markers Initiative (PPMI) is a longitudinal, ongoing, controlled study of de novo PD participants and HC. NMS were rated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I score and other validated NMS scales at baseline and after 2 years. Biological variables included cerebrospinal fluid (CSF) markers and dopamine transporter imaging.

Results

423 PD subjects and 196 HC were enrolled and followed for 2 years. MDS-UPDRS Part I total mean (SD) scores increased from baseline 5.6 (4.1) to 7.7 (5.0) at year 2 in PD subjects (p<0.001) versus from 2.9 (3.0) to 3.2 (3.0) in HC (p=0.38), with a significant difference between the groups (p<0.001). In the multivariate analysis, higher baseline NMS score was associated with female sex (p=0.008), higher baseline MDS-UPDRS Part II scores (p<0.001) and more severe motor phenotype (p=0.007). Longitudinal increase in NMS severity was associated with the older age (0.008) and lower CSF Aβ1-42 (0.005) at baseline. There was no association with the dose or class of dopaminergic therapy.

Conclusions

This study of NMS in early PD identified clinical and biological variables associated with both baseline burden and predictors of progression. The association of a greater longitudinal increase in NMS with lower baseline Aβ1-42 level is an important finding that will have to be replicated in other cohorts.

Trial registration

ClinicalTrials.gov identifier: NCT01141023.

Objective

To determine clinical and biological variables that predict time to initiation of symptomatic therapy in de novo Parkinson's disease patients.

Methods

Parkinson's Progression Markers Initiative is a longitudinal case-control study of de novo, untreated Parkinson's disease participants at enrolment. Participants contribute a wide range of motor and non-motor measures, including biofluids and imaging biomarkers. The machine learning method of random survival forests was used to examine the ability of baseline variables to predict time to initiation of symptomatic therapy since study enrollment (baseline).

Results

There were 423 PD participants enrolled in PPMI and 33 initial baseline variables. Cross-validation results showed that the three-predictor subset of disease duration (time from diagnosis to enrollment), the modified Schwab and England activities of daily living scale, and the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score modestly predicted time to initiation of symptomatic therapy (C = 0.70, pseudo-R (2) = 0.13). Prediction using the three variables was similar to using the entire set of 33. None of the biological variables increased accuracy of the prediction. A prognostic index for time to initiation of symptomatic therapy was created using the linear and nonlinear effects of the three top variables based on a post hoc Cox model.

Interpretation

Our findings using a novel machine learning method support previously reported clinical variables that predict time to initiation of symptomatic therapy. However, the inclusion of biological variables did not increase prediction accuracy. Our prognostic index constructed, based on the group-level survival curve can provide an indication of the risk of initiation of ST for PD patients based on functions of the three top predictors.

Background

Cognitive decline creates substantial morbidity and cost in Parkinson's disease (PD) and clinicians have limited tools for counseling patients on prognosis. We aimed to use data from a randomized, controlled trial of isradipine in Parkinson's disease (STEADY-PD III) to determine which objective cognitive domain deficits drive patient complaints of cognitive symptoms.

Methods

Neuro-Quality of Life (Neuro-QoL) Cognition: General Concerns (GC), and Cognition: Executive Function (EF) (subjective measures), were administered at baseline, 1, 2, and 3 years in 324 people with PD. Baseline Montreal Cognitive Assessment (MoCA) was divided into 4 domains: visuospatial/executive, memory, attention, and language (objective measures). Spearman rank correlations and multiple regression models adjusted for other clinical variables evaluated associations between baseline Neuro-QoL domains and individual MoCA domains. Multiple regression models evaluated the association between baseline MoCA domain performance and Neuro-QoL change over three years. Cox proportional hazards predicted development of PD-MCI based on baseline and time-varying Neuro-QoL reporting.

Results

Higher MoCA memory performance was associated with better Neuro-QoL-GC (β = 0.75, SE = 0.391, p = 0.05) and Neuro-QoL-EF (β = 0.81, SE = 0.36, p = 0.02) at baseline. There was a trend for baseline MoCA memory to predict the degree of subjective cognitive decline on the Neuro-QoL-EF (β = 0.70, SE = 0.42, p = 0.09). Baseline depression and anticholinergic use were associated with worsened Neuro-QoL-EF and Neuro-QoL-GC. Increasing subjective cognitive complaints in Neuro-QoL-EF were associated with development of PD-MCI over 3 years of follow-up (HR = 0.95, CI = 0.90-1.0, p = 0.039).

Conclusions

Objective memory impairment may be a stronger predictor than executive or visuospatial dysfunction for the presence of subjective cognitive complaints in early PD.

Background

Improved prediction of Parkinson's disease (PD) progression is needed to support clinical decision-making and to accelerate research trials.

Objectives

To examine whether baseline measures and their 1-year change predict longer-term progression in early PD.

Methods

Parkinson's Progression Markers Initiative study data were used. Participants had disease duration ≤2 years, abnormal dopamine transporter (DAT) imaging, and were untreated with PD medications. Baseline and 1-year change in clinical, cerebrospinal fluid (CSF), and imaging measures were evaluated as candidate predictors of longer-term (up to 5 years) change in Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) score and DAT specific binding ratios (SBR) using linear mixed-effects models.

Results

Among 413 PD participants, median follow-up was 5 years. Change in MDS-UPDRS from year-2 to last follow-up was associated with disease duration (β= 0.351; 95% CI = 0.146, 0.555), male gender (β= 3.090; 95% CI = 0.310, 5.869), and baseline (β= -0.199; 95% CI = -0.315, -0.082) and 1-year change (β= 0.540; 95% CI = 0.423, 0.658) in MDS-UPDRS; predictors in the model accounted for 17.6% of the variance in outcome. Predictors of percent change in mean SBR from year-2 to last follow-up included baseline rapid eye movement sleep behavior disorder score (β= -0.6229; 95% CI = -1.2910, 0.0452), baseline (β= 7.232; 95% CI = 2.268, 12.195) and 1-year change (β= 45.918; 95% CI = 35.994,55.843) in mean striatum SBR, and 1-year change in autonomic symptom score (β= -0.325;95% CI = -0.695, 0.045); predictors in the model accounted for 44.1% of the variance.

Conclusions

Baseline clinical, CSF, and imaging measures in early PD predicted change in MDS-UPDRS and dopamine-transporter binding, but the predictive value of the models was low. Adding the short-term change of possible predictors improved the predictive value, especially for modeling change in dopamine-transporter binding.

Background

The penetrance of common genetic risk variants for Parkinson's disease (PD) is low. Pesticide exposure increases PD risk, but how exposure affects penetrance is not well understood.

Objective

To determine the relationship between occupational pesticide exposure and PD in people with LRRK2 and GBA risk variants.

Methods

Participants of the Parkinson's Progression Markers Initiative (PPMI) with a LRRK2-G2019 S or GBA risk variant provided information about occupational pesticide exposure. We compared exposure in carriers with and without PD. Among carriers with PD, we used Cox proportional hazard models to compare time-to impairment in balance, cognition, and activities of daily living (ADLs) between participants with and without prior occupational pesticide exposure.

Results

378 participants with a risk variant provided exposure information; 176 with LRRK2-G2019 S (54 with and 122 without PD) and 202 with GBA variants (47 with and 155 without PD). Twenty-six participants reported pesticide exposure. People with a GBA variant and occupational pesticide exposure had much higher odds of PD (aOR: 5.4, 95% CI 1.7-18.5, p < 0.01). People with a LRRK2 variant and a history of occupational pesticide exposure had non-significantly elevated odds of PD (aOR 1.3, 95% CI 0.4-4.6, p = 0.7). Among those with PD, pesticide exposure was associated with a higher risk of balance problems and cognitive impairment in LRRK2-PD and functional impairment in GBA-PD, although associations were not statistically significant.

Conclusions

Occupational pesticide exposure may increase penetrance of GBA-PD and may be associated with faster symptom progression. Further studies in larger cohorts are necessary.

Background

Aggregation of α-synuclein is central to the pathophysiology of PD. Biomarkers related to α-synuclein may be informative for PD diagnosis/progression.

Objectives

To analyze α-synuclein in CSF in drug-naïve PD, healthy controls, and prodromal PD in the Parkinson's Progression Markers Initiative.

Methods

Over up to 36-month follow-up, CSF total α-synuclein and its association with MDS-UPDRS motor scores, cognitive assessments, and dopamine transporter imaging were assessed.

Results

The inception cohort included PD (n = 376; age [mean {standard deviation} years]: 61.7 [9.62]), healthy controls (n = 173; age, 60.9 [11.3]), hyposmics (n = 16; age, 68.3 [6.15]), and idiopathic rapid eye movement sleep behavior disorder (n = 32; age, 69.3 [4.83]). Baseline CSF α-synuclein was lower in manifest and prodromal PD versus healthy controls. Longitudinal α-synuclein decreased significantly in PD at 24 and 36 months, did not change in prodromal PD over 12 months, and trended toward an increase in healthy controls. The decrease in PD was not shown when CSF samples with high hemoglobin concentration were removed from the analysis. CSF α-synuclein changes did not correlate with longitudinal MDS-UPDRS motor scores or dopamine transporter scan.

Conclusions

CSF α-synuclein decreases early in the disease, preceding motor PD. CSF α-synuclein does not correlate with progression and therefore does not reflect ongoing dopaminergic neurodegeneration. Decreased CSF α-synuclein may be an indirect index of changes in the balance between α-synuclein secretion, solubility, or aggregation in the brain, reflecting its overall turnover. Additional biomarkers more directly related to α-synuclein pathophysiology and disease progression and other markers to be identified by, for example, proteomics and metabolomics are needed. © 2019 International Parkinson and Movement Disorder Society.

BACKGROUND: Identifying a meaningful progression metric for Parkinsons disease (PD) that reflects heterogeneity remains a challenge. OBJECTIVE: To assess the frequency and baseline predictors of progression to clinically relevant motor and non-motor PD milestones. METHODS: Using data from the Parkinsons Progression Markers Initiative (PPMI) de novo PD cohort, we monitored 25 milestones across six domains (walking and balance; motor complications; cognition; autonomic dysfunction; functional dependence; activities of daily living). Milestones were intended to be severe enough to reflect meaningful disability. We assessed the proportion of participants reaching any milestone; evaluated which occurred most frequently; and conducted a time-to-first-event analysis exploring whether baseline characteristics were associated with progression. RESULTS: Half of participants reached at least one milestone within five years. Milestones within the cognitive, functional dependence, and autonomic dysfunction domains were reached most often. Among participants who reached a milestone at an annual follow-up visit and remained active in the study, 82% continued to meet criteria for any milestone at one or more subsequent annual visits and 55% did so at the next annual visit. In multivariable analysis, baseline features predicting faster time to reaching a milestone included age (p < 0.0001), greater MDS-UPDRS total scores (p < 0.0001), higher GDS-15 depression scores (p = 0.0341), lower dopamine transporter binding (p = 0.0043), and lower CSF total α-synuclein levels (p = 0.0030). Symptomatic treatment was not significantly associated with reaching a milestone (p = 0.1639). CONCLUSION: Clinically relevant milestones occur frequently, even in early PD. Milestones were significantly associated with baseline clinical and biological markers, but not with symptomatic treatment. Further studies are necessary to validate these results, further assess the stability of milestones, and explore translating them into an outcome measure suitable for observational and therapeutic studies.

Background

Cerebrospinal fluid (CSF) levels of monoamine metabolites may represent biomarkers of Parkinson's disease (PD).

Objective

The aim of this study was quantification of multiple metabolites in CSF from PD versus healthy control subjects (HCs), including longitudinal analysis.

Methods

Absolute levels of multiple monoamine metabolites in CSF were quantified by liquid chromatography coupled with tandem mass spectrometry from 161 individuals with early PD and 115 HCs from the Parkinson's Progression Marker Initiative and de novo PD (DeNoPA) studies.

Results

Baseline levels of homovanillic acid (HVA) and 3,4-dihydroxyphenylacetic acid (DOPAC) were lower in individuals with PD compared with HCs. HVA levels correlated with Movement Disorder Society Unified Parkinson's Disease Rating Scale total scores (P < 0.01). Both HVA/dopamine and DOPAC/dopamine levels correlated with caudate nucleus and raw DOPAC with putamen dopamine transporter single-photon emission computed tomography uptake ratios (P < 0.01). No metabolite changed over 2 years in drug-naive individuals, but some changed on starting levodopa treatment.

Conclusions

HVA and DOPAC CSF levels mirrored nigrostriatal pathway damage, confirming the central role of dopaminergic degeneration in early PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.