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

We address an unmet challenge in Parkinsons 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 Parkinsons 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 Parkinsons disease across studies and at high significance as dysbiotic features of Parkinsons 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 Parkinsons disease have a dysbiotic microbiome, and not all dysbiotic Parkinsons 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 individuals dysbiotic features. We demonstrate the method for Parkinsons disease. The concept, and the method, is generalizable for any disease with a microbiome component.