Using genome-scale metabolic models and the TAMBOOR algorithm, the study predicts patient-specific metabolite secretion changes in Parkinson’s disease, identifies consensus and cluster-specific biomarkers (including dopamine, salsolinol, vitamin D3, retinal, melatonin, biliverdin), and stratifies…

By uncovering heterogeneous, patient-level metabolic signatures and candidate biomarkers that intersect known PD-related pathways and potentially modifiable targets, the work can inform biomarker-driven patient stratification and hypothesis-driven therapeutic or diagnostic development, although…

Understanding the heterogeneous nature of Parkinson's disease is crucial for improving diagnostic and treatment strategies that benefit distinct patient subgroups. Genome-scale metabolic models (GEMs) comprising biochemical reactions and corresponding genes provide powerful frameworks for such investigations when integrated with omics data. Here, we predicted patient-specific metabolite secretion patterns in the form of oversecretion/undersecretion by the TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions (TAMBOOR) algorithm, which links gene expression changes to metabolites through GEMs. We first identified consensus PD biomarkers, and, subsequently stratified patients into three metabolically distinct clusters based on predicted secretion patterns. Consensus biomarkers included both well-known markers, such as dopamine and eumelanin, and additional metabolites, like salsolinol, vitamin D3, and retinal, with potential roles in PD mechanism and symptoms. A subset of the predictions also indicated that some well-known characteristics may not be consistently exhibited in all patients. Furthermore, certain metabolites like melatonin, and biliverdin, though not identified by the consensus approach, showed distinct secretion patterns across patient clusters. Our study emphasizes the importance of individual-level analysis, which has a high potential to investigate heterogeneity in the disease metabolism. Furthermore, it gives insights into the ways of patient classification that can guide more effective diagnostic and treatment strategies.