Microbiome-based biomarkers to guide personalized microbiome-based therapies for Parkinson’s disease

Abstract We address an unmet challenge in Parkinson’s disease: the lack of biomarkers to identify the right patients for the right therapy, which is a main reason clinical trials for disease modifying treatments have all failed. The gut microbiome is a new target for treatment of Parkinson’s disease, with potential to halt disease progression. Our aim was to develop microbiome-based biomarkers to guide patient selection for microbiome-based clinical trials. We used microbial taxa that have been robustly associated with Parkinson’s disease across studies and at high significance as dysbiotic features of Parkinson’s disease. Using individual-level taxonomic relative abundance data, we classified patients according to their dysbiotic features, effectively defining microbiome-based subtypes of PD. We show that not all persons with Parkinson’s disease have a dysbiotic microbiome, and not all dysbiotic Parkinson’s disease microbiomes have the same features. Grounded in robust and reproducible data from differential abundance studies, we propose an intuitive and easily modifiable method to identify the optimal candidates for microbiome-based clinical trials, and subsequently, for treatments that are personalized for each individual’s dysbiotic features. We demonstrate the method for Parkinson’s disease. The concept, and the method, is generalizable for any disease with a microbiome component.