Personalized Metabolite Biomarker Predictions Reveal Heterogeneous Characteristics of Parkinson's Disease

Abstract

Understanding the heterogeneous nature of Parkinsons disease is crucial for improving diagnostic and treatment strategies that benefit distinct patient subgroups. Genome-scale metabolic models, when integrated with omics data, provide powerful frameworks for such investigations. 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. We first identified biomarkers for the general PD population using a consensus approach that prioritized changes consistent across the patient cohort. Then, we clustered patients based on the predicted metabolite secretion pattern of each patient to assess heterogeneity and identify potential patient subgroups. Three main clusters were detected, and the most discriminative metabolites underlying this grouping were determined. The power of the discriminative metabolites in grouping PD patients were confirmed with independent validation data to show the reliability and robustness of our approach. Predicted biomarkers for the general population of PD included both well-known disease markers, such as dopamine and eumelanin, and additional metabolites, such as salsolinol, leukotriene A4, heme metabolism products, calcitriol, 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 such as melatonin, sphingosine, and biliverdin, though not identified by the general approach, showed distinct secretion patterns across patient clusters. For instance, an undersecretion pattern of melatonin, possibly associated with the sleep disturbance symptom of PD, was detected exclusively in one subgroup. 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.