查看文章

Over 70% of people with REM-Sleep Behaviour Disorder (RBD) will go on to develop one of the neurodegenerative alpha-synucleinopathies within 12 years, most commonly Parkinson’s disease (PD). Symptom-tracking remains the dominant clinical approach to RBD prognosis due to sub-optimal specificity/sensitivity of pathology-detection methods and inadequate prognostic biomarkers of disease progression. In particular, there have been limited efforts to explore combinations of biomarkers.

The primary aim of this thesis was to identify novel prognostic biomarkers of RBD phenoconversion to PD. Frequentist statistical analysis was used to closely examine differences between groups and identify potential individual biomarkers, while machine learning was enlisted to develop a prognostic model to combine biomarkers and predict RBD prognosis.