Utility of Baseline Pathological, Neuroimaging and Clinical Markers for Progression Prognosis and Subtype Classification in Early Parkinson’s Disease: Implications for Cognitive Impairment

Abstract

AbstractBackgroundCognitive dysfunction is a well‐recognised PD symptom, but it is highly heterogenous and under‐assessed clinically. Interestingly, PD motor subtypes are associated with different risk of cognitive decline. However, subtype classification is currently reliant on subjective clinician assessment and may consequently be misdiagnosed. Therefore, objective biomarkers of PD progression are critically needed, but must be validated prior to clinical deployment. We assessed the utility of various clinical, neuroimaging and pathological measures as predictors of cognitive outcomes up to 5‐years follow‐up in early PD.MethodData was extracted from the Parkinson’s Progression Markers Initiative. Cognitive assessments were compiled into an overall cognition score via principal component analysis. Distinct subgroups were identified via Fuzzy C‐Means clustering on Year‐5 Unified Parkinson’s Disease Rating Scale (UPDRS) motor scores. Models predicting cluster membership were created via logistic regression and a stratified cross‐validation machine learning pipeline, comparing two predictor inputs: 1) baseline motor assessments; and 2) incorporating additional measures, including prodromal assessments (sleep, olfactory and autonomic function), neuroimaging (proxy SN volume, striatal DaT binding) and biofluid markers (CSF alpha‐syn, p‐tau, a‐beta, and serum IGF‐1) at baseline. Finally, cluster membership was used for retroactive assessment of baseline and follow‐up cognitive data.ResultsTwo clusters were identified, with cluster two (n = 117) demonstrating higher rigidity, lower DaT binding, worsened motor outcomes and increased mood dysfunction at follow‐up compared to tremor‐dominant cluster one (n = 183). Additionally, rigid‐dominant membership was predicted by male sex, higher p‐tau, along with lower a‐beta, predicting 40% of variance (n = 111). This was significantly higher (P<0.001) than baseline motor scores alone, only accounting for 18% of variance. This was supported by machine learning, whereby incorporating additional assessments corresponded to a classification accuracy of 62%, compared to 26% when omitted. Finally, higher probability of belonging to the rigid‐dominant cluster yielded significant negative correlations cognition at baseline and follow‐up.ConclusionIncreased likelihood of rigid‐dominance corresponded to more profound cognitive decline, which may in part result from neuropathological marker concentrations as highlighted by predictive models. Therefore, further tracking of this cohort, specifically conversion into MCI or dementia, could provide insight into pathological contribution to differences in cognitive decline between PD subtypes.