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.

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 (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.

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.

Basic clinical features do not predict dopamine transporter binding in idiopathic REM behavior disorder

REM sleep behavior disorder (RBD) is strongly associated with development of Parkinson’s Disease and other α-synuclein-related disorders. Dopamine transporter (DAT) binding deficit predicts conversion to α-synuclein-related disorders in individuals with RBD. In turn, identifying which individuals with RBD have the highest likelihood of having abnormal DAT binding would be useful. The objective of this analysis was to examine if there are basic clinical predictors of DAT deficit in RBD. Participants referred for inclusion in the RBD cohort of the Parkinson Progression Markers Initiative were included. Assessments at the screening visit including DAT SPECT imaging, physical examination, cognitive function screen, and questionnaire-based non-motor assessment. The group with DAT binding deficit (n = 49) was compared to those without (n = 26). There were no significant differences in demographic or clinical features between the two groups. When recruiting RBD cohorts enriched for high risk of neurodegenerative disorders, our data support the need for objective biomarker assessments.

The clinical characteristics of patients with rapid eye movement sleep behavior disorder (RBD) are not associated with reduced dopamine transporter binding, an established imaging biomarker of Parkinson’s Disease (PD). Because around 80 percent of patients with RBD develop PD, there is great hope that research on these patients will help uncover early signs of the disease and guide the development of neuroprotective therapies. Lana Chahine at The University of Pittsburgh, USA, and colleagues in the Parkinson Progression Markers Initiative (PPMI) Sleep Working Group analyzed the clinical features of 75 individuals with RBD. They found no significant differences in demographic features or in motor and non-motor symptoms between RBD patients with dopamine transporter binding deficit and those without. These findings highlight the need to assess dopamine transporter binding to determine the future risk of PD.