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Addressing Parkinson’s Disease risk analysis, early diagnosis, progression, and stratification using data-driven approaches in deeply phenotyped cohorts

Schalkamp, Ann-Kathrin 2023. Addressing Parkinson’s Disease risk analysis, early diagnosis, progression, and stratification using data-driven approaches in deeply phenotyped cohorts. PhD Thesis, Cardiff University.
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Research on Parkinson’s disease is entering a data-driven era, driven by the promise of precision medicine. However, the wealth of data from different modalities is often not being integrated for a holistic view. This thesis delves into deeply phenotyped cohorts, examining the potential of various data modalities throughout the Parkinson’s disease timeline: from risk analysis and early diagnosis to disease monitoring, progression, and stratification. In the first chapter, I advocate for a transition from traditional, broad diagnostic labels to quantifiable measures that sensitively capture inter-individual variations. I focus on one hallmark pathophysiological change: the loss of dopaminergic neurons, exploring its genetic basis and the role these neurons play in the genetic risk of Parkinson’s disease. I show that genetics associated with dopaminergic neurons inform most of the genetic risk of Parkinson’s disease (DaN PRS AUPRC: 0.720, overall PRS AUPRC: 0.697) and that genetics related to GABAergic neurons are involved in the loss of dopaminergic neurons. The second chapter aims to construct a population-wide screening tool for the early detection of Parkinson’s disease. I identify digital markers, as measured by smartwatches, to outperform existing risk factors and prodromal markers (p-value < 0.005, relative AUPRC improvement of 366.67%). This tool sensitively detects people at risk up to seven years prior to clinical diagnosis. The third chapter is centred around the progression of Parkinson’s disease. I provide a comprehensive overview of clinical and biological markers, extract the most likely sequence of these markers to become abnormal throughout the disease course, and assess the effect of medication (UPDRS III yearly progression decreased by 1.71±1.57 points). Additionally, I identify digital markers that can be continuously monitored to represent motor and non-motor aspects of the disease. In the final chapter, I address the heterogeneity observed in Parkinson’s disease. I quantitatively compare stratification approaches based on clinical, biological, or digital data and investigate the biological and clinical relevance of the extracted subtypes. I show a low similarity between clusterings (<0.24 normalised mutual information) highlighting the impact of feature selection and method on the subtypes. In summary, this thesis underscores the value of quantifiable measures, particularly digital markers, in various aspects of Parkinson’s disease research. Such data will enable the path towards precision medicine for Parkinson’s disease.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Medicine
Date of First Compliant Deposit: 25 January 2024
Last Modified: 25 Jan 2024 16:39

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