Differences in the Presentation and Progression of Parkinson's Disease by Sex.

BACKGROUND: Previous studies reported various symptoms of Parkinson's disease (PD) associated with sex. Some were conflicting or confirmed in only one study. OBJECTIVES: We examined sex associations to PD phenotypes cross-sectionally and longitudinally in large-scale data. METHODS: We tested 40 clinical phenotypes, using longitudinal, clinic-based patient cohorts, consisting of 5946 patients, with a median follow-up of 3.1 years. For continuous outcomes, we used linear regressions at baseline to test sex-associated differences in presentation, and linear mixed-effects models to test sex-associated differences in progression. For binomial outcomes, we used logistic regression models at baseline and Cox regression models for survival analyses. We adjusted for age, disease duration, and medication use. In the secondary analyses, data from 17 719 PD patients and 7588 non-PD participants from an online-only, self-assessment PD cohort were cross-sectionally evaluated to determine whether the sex-associated differences identified in the primary analyses were consistent and unique to PD. RESULTS: Female PD patients had a higher risk of developing dyskinesia early during the follow-up period, with a slower progression in activities of daily living difficulties, and a lower risk of developing cognitive impairments compared with male patients. The findings in the longitudinal, clinic-based cohorts were mostly consistent with the results of the online-only cohort. CONCLUSIONS: We observed sex-associated contributions to PD heterogeneity. These results highlight the necessity of future research to determine the underlying mechanisms and importance of personalized clinical management. © 2020 International Parkinson and Movement Disorder Society.

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

BACKGROUND: 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. METHODS: 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). FINDINGS: 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). INTERPRETATION: 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. FUNDING: Michael J Fox Foundation.

Data Sharing Goals for Nonprofit Funders of Clinical Trials.

Sharing clinical trial data can provide value to research participants and communities by accelerating the development of new knowledge and therapies as investigators merge data sets to conduct new analyses, reproduce published findings to raise standards for original research, and learn from the work of others to generate new research questions. Nonprofit funders, including disease advocacy and patient-focused organizations, play a pivotal role in the promotion and implementation of data sharing policies. Funders are uniquely positioned to promote and support a culture of data sharing by serving as trusted liaisons between potential research participants and investigators who wish to access these participants' networks for clinical trial recruitment. In short, nonprofit funders can drive policies and influence research culture. The purpose of this paper is to detail a set of aspirational goals and forward thinking, collaborative data sharing solutions for nonprofit funders to fold into existing funding policies. The goals of this paper convey the complexity of the opportunities and challenges facing nonprofit funders and the appropriate prioritization of data sharing within their organizations and may serve as a starting point for a data sharing toolkit for nonprofit funders of clinical trials to provide the clarity of mission and mechanisms to enforce the data sharing practices their communities already expect are happening.

Correction: Data Sharing Goals for Nonprofit Funders of Clinical Trials.

[This corrects the article DOI: 10.2196/23011.].

Fox Insight collects online, longitudinal patient-reported outcomes and genetic data on Parkinson's disease.

Fox Insight is an online, longitudinal health study of people with and without Parkinson's disease with targeted enrollment set to at least 125,000 individuals. Fox Insight data is a rich data set facilitating discovery, validation, and reproducibility in Parkinson's disease research. The dataset is generated through routine longitudinal assessments (health and medical questionnaires evaluated at regular cycles), one-time questionnaires about environmental exposure and healthcare preferences, and genetic data collection. Qualified Researchers can explore, analyze, and download patient-reported outcomes (PROs) data and Parkinson's disease- related genetic variants at https://foxden.michaeljfox.org. The full Fox Insight genetic data set, including approximately 600,000 single nucleotide polymorphisms (SNPs), can be requested separately with institutional review and are described outside of this data descriptor.

Comparison of an Online-Only Parkinson's Disease Research Cohort to Cohorts Assessed In Person.

BACKGROUND: Online tools for data collection could be of value in patient-oriented research. The Fox Insight (FI) study collects data online from individuals with self-reported Parkinson's disease (PD). Comparing the FI cohort to other cohorts assessed through more traditional (in-person) observational research studies would inform the representativeness and utility of FI data. OBJECTIVE: To compare self-reported demographic characteristics, symptoms, medical history, and PD medication use of the FI PD cohort to other recent observational research study cohorts assessed with in-person visits. METHODS: The FI PD cohort (n = 12,654) was compared to 3 other cohorts, selected based on data accessibility and breadth of assessments: Parkinson's Progression Markers Initiative (PPMI; PD n = 422), Parkinson's Disease Biomarker Program (PDBP; n = 700), and PD participants in the LRRK2 consortium without LRRK2 mutations (n = 508). Demographics, motor and non-motor assessments, and medications were compared across cohorts. Where available, identical items on surveys and assessments were compared; otherwise, expert opinion was used to determine comparable definitions for a given variable. RESULTS: The proportion of females was significantly higher in FI (45.56%) compared to PPMI (34.36%) and PDBP (35.71%). The FI cohort had greater educational attainment as compared to all other cohorts. Overall, prevalence of difficulties with motor experiences of daily living and non-motor symptoms in the FI cohort was similar to other cohorts, with only a few significant differences that were generally small in magnitude. Missing data were rare for the FI cohort, except on a few variables. DISCUSSION: Patterns of responses to patient-reported assessments obtained online on the PD cohort of the FI study were similar to PD cohorts assessed in-person.