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Observational Study
. 2021 Apr 12;144(3):975-988.
doi: 10.1093/brain/awaa461.

Sequence of clinical and neurodegeneration events in Parkinson's disease progression

Neil P Oxtoby  1 , Louise-Ann Leyland  2 , Leon M Aksman  1 , George E C Thomas  2 , Emma L Bunting  2 , Peter A Wijeratne  1 , Alexandra L Young  1   3 , Angelika Zarkali  2 , Manuela M X Tan  4   5 , Fion D Bremner  6 , Pearse A Keane  7   8 , Huw R Morris  4   5 , Anette E Schrag  4   5 , Daniel C Alexander  1 , Rimona S Weil  2   5   9
Affiliations
Observational Study

Sequence of clinical and neurodegeneration events in Parkinson's disease progression

Neil P Oxtoby et al. Brain. .

Abstract

Dementia is one of the most debilitating aspects of Parkinson's disease. There are no validated biomarkers that can track Parkinson's disease progression, nor accurately identify patients who will develop dementia and when. Understanding the sequence of observable changes in Parkinson's disease in people at elevated risk for developing dementia could provide an integrated biomarker for identifying and managing individuals who will develop Parkinson's dementia. We aimed to estimate the sequence of clinical and neurodegeneration events, and variability in this sequence, using data-driven statistical modelling in two separate Parkinson's cohorts, focusing on patients at elevated risk for dementia due to their age at symptom onset. We updated a novel version of an event-based model that has only recently been extended to cope naturally with clinical data, enabling its application in Parkinson's disease for the first time. The observational cohorts included healthy control subjects and patients with Parkinson's disease, of whom those diagnosed at age 65 or older were classified as having high risk of dementia. The model estimates that Parkinson's progression in patients at elevated risk for dementia starts with classic prodromal features of Parkinson's disease (olfaction, sleep), followed by early deficits in visual cognition and increased brain iron content, followed later by a less certain ordering of neurodegeneration in the substantia nigra and cortex, neuropsychological cognitive deficits, retinal thinning in dopamine layers, and further deficits in visual cognition. Importantly, we also characterize variation in the sequence. We found consistent, cross-validated results within cohorts, and agreement between cohorts on the subset of features available in both cohorts. Our sequencing results add powerful support to the increasing body of evidence suggesting that visual processing specifically is affected early in patients with Parkinson's disease at elevated risk of dementia. This opens a route to earlier and more precise detection, as well as a more detailed understanding of the pathological mechanisms underpinning Parkinson's dementia.

Keywords: Parkinson’s disease; dementia; disease progression; event-based model; vision.

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Figures

Figure 1
Figure 1
How the event-based model works. The event-based model is a statistical method for quantifying a sequence of observable abnormality in a set of disease-relevant features (biomarkers). The model works by assessing, at the group level, combinations of simultaneously normal and abnormal measurements in different biomarkers across individuals at multiple stages of disease progression. Top: In neurodegenerative disease progression (left to right), observable abnormality (vertical axis) across multiple features (A, B, C, D) likely proceeds in a cascade or sequence A→ B→C→D, as in an influential hypothetical model of Alzheimer’s disease progression (Jack et al., 2010; Jack Jr et al., 2013). Bottom: A cross-sectional sample of individuals (columns) at different stages of disease progression (horizontal axis) showing the corresponding observed combinations of normal (white) and degrees of abnormality (shades of colour) across the four features. A single individual sampled near the middle of the disease is shown in both panels: early events (A and B) have higher abnormality than later events (C and D). Whenever such an individual shows an elevated value of biomarker A, but a normal value for biomarker B, this adds evidence that A changes before B.
Figure 2
Figure 2
Event-based model of progression in Parkinson’s disease. Data-driven sequence of events in Parkinson’s disease progression colour coded by modality shown as: positional density (left); and cumulative abnormality (right) from repeated stratified 5-fold cross-validation. The estimated sequence of events is seen on the vertical axis, with ordering proceeding from top to bottom (earliest to latest event). Colour intensity represents the proportion (0 in white, 1 most intense) of the posterior distribution in which events (y-axis) appear in a particular position in the sequence (x-axis). This model is robust, having a similarity of BC = 0.60 ± 0.04 across 50 cross-validation folds. D15 = colour test; GCL = ganglion cell layer; GNT = Graded Naming Test; IPL = inner plexiform layer; L = left; R = right; RBDSQ = REM sleep behaviour disorder screening questionnaire; UPDRS = Unified Parkinson’s disease rating scale.
Figure 3
Figure 3
Patient staging results: discovery cohort. Top: Model stage showing most healthy controls at stage zero (inset); and patients at varying, but mostly early, stages. Bottom: Cross-validation accuracy across 50-folds from repeated stratified 5-fold cross-validation. Left: Mean and standard deviation (STD) absolute error in patient stage. Right: raw errors in patient stage. Overall mean absolute error was 1.5 ± 3.3 stages. CV = cross-validation.
Figure 4
Figure 4
Visual comparison of progression models in the discovery and external cohorts. Event-based models of Parkinson’s disease progression built on a subset of comparable clinical/cognitive/MRI features in the local cohort (left) and PPMI cohort (right). Both models suggest that Parkinson’s disease starts with classic symptoms of prodromal Parkinson’s disease (sleep/smell abnormality), with neurodegeneration occurring later in the substantia nigra (DWI/DTI) and cortex (MRI). Cognitive abnormality appears first in the MoCA score, preceding measures of verbal fluency.
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