Parkinson's Disease

Identification of reproducible metabolites for Parkinson’s Disease diagnosis using clinical data and computational modelling

Parkinson’s disease (PD) is the second most common neurodegenerative disorder. Many studies have reported metabolomic analysis of different bio-specimens from Parkinson’s disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson’s disease. Therefore, we systematically reviewed the literature on the metabolomic studies of PD patients. The reported

diagnosis-associated metabolites were collected to reveal the consistency and reproducibility of PD metabolites. To comprehensively understand the dysfunctional metabolic pathways of PD, we effectively integrated PD metabolites with genome-scale computational modelling using an established pipeline 'XomicToModel'. A novel genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.