Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques.

Motivated by data measuring progression of leishmaniosis in a cohort of US dogs, we develop a Bayesian longitudinal model with autoregressive errors to jointly analyze ordinal and continuous outcomes. Multivariate methods can borrow strength across responses and may produce improved longitudinal forecasts of disease progression over univariate methods. We explore the performance of our proposed model under simulation, and demonstrate that it has improved prediction accuracy over traditional Bayesian hierarchical models. We further identify an appropriate model selection criterion. We show that our method holds promise for use in the clinical setting, particularly when ordinal outcomes are measured alongside other variables types that may aid clinical decision making. This approach is particularly applicable when multiple, imperfect measures of disease progression are available.

Self-reported physical activity levels and clinical progression in early Parkinson's disease.

INTRODUCTION: This study investigates longitudinal changes in self-reported physical activity, measured by Physical Activity Scale of the Elderly (PASE), in early Parkinson's disease (PD) and matched healthy control (HC) participants in the Parkinson's Progression Marker Initiative (PPMI) and evaluates associations between physical activity and PD progression. METHODS: PPMI is a prospective, longitudinal study evaluating markers of progression in PD participants who are unmedicated at enrollment. PASE, a self-reported measure of physical activity, was administered to early PD (N = 380) and HC (N = 174). PASE was introduced after study launch and therefore administered at years 2, 3, and 4. PASE scores for PD and HC were compared with t-tests and changes over time were evaluated with generalized estimating equations. RESULTS: There were no differences in activity levels between PD and HC at any time point. However, PD participants had a longitudinal decrease in PASE from years two to four (p = 0.034), while HC did not (p = 0.89). In exploratory analyses controlling for age, sex, and disease duration, higher self-reported activity at year 2 were associated with slower progression of motor symptoms (p = 0.018), ADL performance (p < 0.0001), depression (p = 0.001), anxiety (p = 0.002), and cognitive decline (p = 0.016) over two years. These findings remained significant after adjusting for disease severity. CONCLUSION: There are no differences in self-reported physical activity between HC and early PD, but activity levels decline longitudinally in PD. Exploratory analyses show that higher self-reported physical activity is associated with less disease progression. Therefore, interventions to increase physical activity in early PD could potentially modify the disease course.

Predicting Progression in Parkinson's Disease Using Baseline and 1-Year Change Measures.

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 (ß= 0.351; 95% CI = 0.146, 0.555), male gender (ß= 3.090; 95% CI = 0.310, 5.869), and baseline (ß= -0.199; 95% CI = -0.315, -0.082) and 1-year change (ß= 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 (ß= -0.6229; 95% CI = -1.2910, 0.0452), baseline (ß= 7.232; 95% CI = 2.268, 12.195) and 1-year change (ß= 45.918; 95% CI = 35.994,55.843) in mean striatum SBR, and 1-year change in autonomic symptom score (ß= -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.

Feasibility and Safety of Multicenter Tissue and Biofluid Sampling for α-Synuclein in Parkinson's Disease: The Systemic Synuclein Sampling Study (S4).

BACKGROUND: α-synuclein is a lead Parkinson's disease (PD) biomarker. There are conflicting reports regarding accuracy of α-synuclein in different tissues and biofluids as a PD biomarker, and the within-subject anatomical distribution of α-synuclein is not well described. The Systemic Synuclein Sampling Study (S4) aims to address these gaps in knowledge. The S4 is a multicenter, cross-sectional, observational study evaluating α-synuclein in multiple tissues and biofluids in PD and healthy controls (HC). OBJECTIVE: To describe the baseline characteristics of the S4 cohort and safety and feasibility of this study. METHODS: Participants underwent motor and non-motor clinical assessments, dopamine transporter SPECT, biofluid collection (cerebrospinal fluid, saliva, and blood), and tissue biopsies (skin, sigmoid colon, and submandibular gland). Biopsy adequacy was determined based on presence of adequate target tissue. Tissue sections were stained with the 5C12 monoclonal antibody against unmodified α-synuclein. All specimens were acquired and processed in a standardized manner. Adverse events were systematically recorded. RESULTS: The final cohort consists of 82 participants (61 PD, 21 HC). In 68 subjects (83%), all types of specimens were obtained but only 50 (61%) of subjects had all specimens both collected and evaluable for α-synuclein. Mild adverse events were common, especially for submandibular gland biopsy, but only 1 severe adverse event occurred. CONCLUSION: Multicenter tissue and biofluid sampling for α-synuclein is feasible and generally safe. S4 will inform understanding of the concurrent distribution of α-synuclein pathology and biomarkers in biofluids and peripheral nervous system in PD.