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.

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. Fox Insight is sponsored by The Michael J. Fox Foundation for Parkinson's Research.

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

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).

l on behalf of the Fox Insight Study 1