Universal continuous severity traits underlying hundreds of Parkinson’s disease clinical features

The generation of deeply phenotyped patient cohorts offers an enormous potential to identify disease subtypes but are currently limited by the cohort size and the heterogeneity of the clinical assessments collected across different cohorts. Identifying the universal axes of clinal severity and progression is key to accelerating our understanding of how disease manifests and progresses. These universal axes would accelerate our understanding of how Parkinsons disease (PD) manifests and progresses through which patients may be appropriately compared appropriately stratified, and personalised therapeutic strategies and treatments can be developed and targeted. We developed a Bayesian multiple phenotype mixed model incorporating the genetic relationships between individuals which is able to reduce a wide-array of different clinical measurements into a smaller number of continuous underlying factors named phenotypic axis. We identify three principal axes of PD patient phenotypic variation which are reproducibly found across three independent, deeply and diversely phenotyped cohorts. Together they explain over 75% of the observed clinical variation and remain robustly captured with a fraction of the clinically-recorded features. The most influential axis was associated with the genetic risk of Alzheimers disease (AD) and involves genetic pathways associated with neuroinflammation. Our results suggest PD patients with a high genetic risk for AD are more likely to develop a more aggressive form of PD including, but not limited to, dementia.