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