Abstract
INTRODUCTION: Parkinson's disease (PD) is the most common movement disorder, and its prevalence is increasing rapidly worldwide with an ageing population. The UK Biobank is the world's largest and most comprehensive longitudinal study of ageing community volunteers. The cause of the common form of PD is multifactorial, but the degree of causal heterogeneity among patients or the relative importance of one risk factor over another is unclear. This is a major impediment to the discovery of disease-modifying therapies. METHODS: We used an integrated machine learning algorithm (IDEARS) to explore the relative effects of 1,753 measured non-genetic variables in 334,062 eligible UK Biobank participants, including 2,719 who had developed PD since their recruitment into the study. RESULTS: Male gender was the highest-ranked risk factor, followed by elevated serum insulin-like growth factor 1 (IGF-1), lymphocyte count, and neutrophil/lymphocyte ratio. A group of factors aligned with the symptoms of frailty also ranked highly. IGF-1 and neutrophil/lymphocyte ratio were also elevated in both sexes before PD diagnosis and at the point of diagnosis. DISCUSSION: The use of machine learning with the UK Biobank provides the best opportunity to explore the multidimensional nature of PD. Our results suggest that novel risk biomarkers, including elevated IGF-1 and NLR, may play a role in, or are indicative of PD pathomechanisms. In particular, our results are consistent with PD being a central manifestation of a systemic inflammatory disease. These biomarkers may be used clinically to predict future PD risk, improve early diagnosis and provide new therapeutic avenues.