Utility of Baseline Pathological, Neuroimaging and Clinical Markers for Prognosis in Early Parkinson's Disease

BackgroundCurrently, prognosis of Parkinson's Disease (PD) is limited. Emerging literature highlights potential of multi-modal biomarkers and neuroimaging to provide critical insight into clinical progression, potentially improving prediction of long-term outcomes.MethodsData were extracted from the Parkinson's Progression Markers Initiative (PPMI). Hierarchical clustering was applied to Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores at year-five follow-up, identifying two clusters. Differences in progression, as well as retrospective assessment of baseline differences, between clusters were explored for pathological biomarkers, neuroimaging, and prodromal measures. Additionally, logistic regression, receiver operating characteristic curve analyses and machine learning were employed to determine utility of variables at baseline as predictors of cluster membership.ResultsThe more impaired cluster demonstrated worse motor and non-motor outcomes, including higher rates of dementia and cognitive complaints at year-five, as well as more profound rigidity than cluster one. Further, retrospective comparisons showed cluster two performing worse in all prodromal measures and demonstrated lower striatal dopamine transporter and cognitive ability. Logistic regression determined that membership in this cluster was predicted by higher autonomic dysfunction and p-tau, along with reduced smell and alpha-syn, predicting 49.1% of variance (AUC = 0.92). This was significantly higher (p < 0.001) than the model including MDS-UPDRS scores alone, only accounting for 27.4% of variance (AUC = 0.74). Findings were corroborated by machine learning, whereby multi-modal assessment corresponded to 74% classification accuracy, compared to 60% with MDS-UPDRS alone.ConclusionPrediction of more marked impairment at year-five was substantially improved via multi-modal assessment, specifically, pathological biomarkers, suggesting that incorporating biomarkers into clinical criteria could enhance long-term prognosis.