Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation

Abstract

BackgroundBetter understanding and prediction of PD progression could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in PD. We also sought to assess the use of these models in the design of treatment trials in PD.MethodsA Bayesian multivariate predictive inference platform was applied to data from the Parkinson’s Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from PD patients and healthy controls to construct an ensemble of models to predict the annualised rate of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale parts II and III combined. We tested our overall explanatory power, as assessed by the coefficient of determination (R2), and replicated novel findings in an independent clinical cohort of PD patients from the Longitudinal and Biomarker Study in PD (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomized placebo-controlled trials within the out-of sample LABS-PD cohort.FindingsA total of 117 controls and 312 PD cases were available for analysis. Our model ensemble exhibited strong performance in-cohort (5-fold cross-validated R2=41%, 95% CI: 35% – 47%) and significant, though reduced, performance out-of-cohort (R2=9%, 95% CI: 4% – 16%). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort of 317 PD patients. These included significant replication of higher baseline motor score, male sex, and increased age, as well as a novel PD-specific epistatic interaction all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2.9%, 95%CI: 1.5–4.3%). CSF biomarkers at baseline showed a more modest (0.3%; 95%CI: 0.1–0.5%), but still significant effect on motor progression prediction. The simulations (n=5000) showed that incorporating the predicted rates of motor progression into the final models of treatment effect reduced the variability in the study outcome allowing significant differences to be detected at sample sizes up to 20% smaller than in naïve trials.InterpretationOur model ensemble confirmed established and identified novel predictors of PD motor progression. Improving existing prognostic models through machine learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment.FundingMichael J. Fox Foundation for Parkinson’s Research and National Institute of Neurological Disorders and Stroke (1P20NS092529-01).