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

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.

Targeted screening for Alzheimer’s disease clinical trials using data-driven disease progression models

“…This is achieved directly from the data distributions in diseased and healthy groups and without a priori -defined disease stages or biomarker cutpoints. The EBM, in its various versions, has been applied to a variety of diseases since 2011, e.g., (19, 8, 22, 23, 24, 25). For a detailed intuitive description of the EBM, we refer the reader to (22).…”

“…Here we employ the recently-developed kernel density estimation (KDE) EBM that copes naturally with the ceiling/floor effects seen in cognitive data (8), and gives a cleaner interpretation of the model by exploiting prior information on disease direction (22). To improve generalizability, we perform repeated 5-fold cross-validation (10 repeats) and combine all 50 sets of posterior samples of the EBM into a cross-validated positional density map (22).…”

“…Figure 1 Figure 2 shows a key component of the EBM -the normal/abnormal mixture models for each cognitive instrument (blue/orange solid lines, respectively), and the resulting cumulative probability of an event having occurred (dashed lines) (19). These sigmoidal event probabilities quantify divergence from normality (22) and provide a visualisation of the data-driven event threshold (akin to a data-driven biomarker cutpoint). Histograms show the AD (orange) and CN (blue) data from ADNI.…”

Inter-Cohort Validation of SuStaIn Model for Alzheimer’s Disease

“…This context helped deploying disease models that allowed the definition of new strategies for biomarker-informed patient staging ( Sperling et al, 2011 ). Among these algorithms, the family of event-based models (EBM) has been proven successful in defining discrete models for a wide battery of brain diseases ( Young et al, 2015 ; Eshaghi et al, 2018 ; Wijeratne et al, 2018 ; Venkatraghavan et al, 2019 ; Firth et al, 2020 ; Oxtoby et al, 2021 ), showing utility in fine-grained staging of patients ( Young et al, 2014 ). Generally, the assumption of these EBMs is that the sequence of events describing the disease progression is common for all subjects, which ignores the observed variation between individuals that may indicate the presence of subtypes of AD ( Poulakis et al, 2020 ).…”

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer’s disease

“…One model archetype that has found wide success in the context of neurodegenerative diseases [11][12][13][14] and AD specifically [15] is the event-based model (EBM) [13]. It is a data-driven probabilistic generative model that characterises the progression of a disease in the form of a single sequence of events which describes the relative order of measured markers turning from a normal state to a diseased state (i.e., abnormal state).…”