Genetically-Informed Prediction of Short-Term Parkinson’s Disease Progression

Abstract

Background: Parkinson’s disease (PD) treatments modify disease symptoms but do not halt progression, characterized by slow and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure.Objective: Development of models for short-term PD progression prediction, which could be useful for informing individual-level disease management, and shortening the time required to detect disease-modifying drug effects in clinical studies.Methods: PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36- months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in an optimized XGBoost framework. These predictions were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort.Results: 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing.Conclusions: Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.