Metabolic Profiling of CSF from People Suffering from Sporadic and LRRK2 Parkinson’s Disease: A Pilot Study

CSF from unique groups of Parkinson’s disease (PD) patients was biochemically profiled to identify previously unreported metabolic pathways linked to PD pathogenesis, and novel biochemical biomarkers of the disease were characterized. Utilizing both 1H NMR and DI-LC-MS/MS we quantitatively profiled CSF from patients with sporadic PD (n = 20) and those who are genetically predisposed (LRRK2) to the disease (n = 20), and compared those results with age and gender-matched controls (n = 20). Further, we systematically evaluated the utility of several machine learning techniques for the diagnosis of PD. 1H NMR and mass spectrometry-based metabolomics, in combination with bioinformatic analyses, provided useful information highlighting previously unreported biochemical pathways and CSF-based biomarkers associated with both sporadic PD (sPD) and LRRK2 PD. Results of this metabolomics study further support our group’s previous findings identifying bile acid metabolism as one of the major aberrant biochemical pathways in PD patients. This study demonstrates that a combination of two complimentary techniques can provide a much more holistic view of the CSF metabolome, and by association, the brain metabolome. Future studies for the prediction of those at risk of developing PD should investigate the clinical utility of these CSF-based biomarkers in more accessible biomatrices. Further, it is essential that we determine whether the biochemical pathways highlighted here are recapitulated in the brains of PD patients with the aim of identifying potential therapeutic targets.


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: Results of univariate analysis comparing the mean concentrations of metabolites in CSF obtained from LRRK2 control and LRRK2 PD patients.
P9 Table S2: The results of the Metabolite Set Enrichment Analysis showing the metabolic pathways that were significantly perturbed when LRRK2 PD CSF was compared with their respective controls.
P10-15 Table S3: Results of univariate analysis comparing the mean concentrations of metabolites in the CSF obtained from LRRK2 control and LRRK2 PD patients.
P15 Table S4: The results of the metabolite set enrichment analysis showing the metabolic pathways that were significantly perturbed when sPD CSF was compared with their respective controls.
P16-20 Table S5: Results of univariate analysis comparing the mean concentrations of metabolites in CSF obtained from sPD and LRRK2 PD patients.
P21 Table S6: The results of the metabolite set enrichment analysis showing the metabolic pathways that were significantly perturbed when LRKK2 PD CSF was compared with sPD. Table S7: Results of univariate analysis comparing the mean concentrations of metabolites in CSF obtained from sPD controls and LRRK2 PD controls. P27 Table S8: Pairwise O-PLS-DA model performance metrics. Table S9: Optimized model parameters for each machine learning algorithm evaluated for prediction of sPD and LRRK2 PD in this study.

P28-29
P30 Table S10: The panel of metabolites as chosen using the RFE feature selection algorithm to generate our predictive models P31-32 Figure S1: Representative 1D 1 H NMR spectrum of CSF; identified and quantified metabolites are displayed.
P33 Figure S2: Investigation of outliers for each group by PCA (a-d) and Q2 vs. T 2 Hotelling plots (e,f).
P34 Figure S3: QQ-plots used to determine whether the metabolomics data for each metabolite are normally distributed and/or the corresponding p-value is inflated.
P35-42 Figure S4: Box-plots showing pair-wise comparisons of the concentration values of all metabolites in CSF.
P43 Figure S5: AUROC plot for the top three ML predictive models for the classification of LRRK2 PD as compared to their corresponding controls.
P44 Figure S6: AUROC plot for the top three ML-based predictive models for the classification of sPD as compared to their corresponding controls.
*: A significant difference in the average concentration values of certain metabolite between sPD and sPD control groups was observed.
**: A significant difference in the average concentration values of certain metabolite between LRKK2 and LRKK2 control groups was observed ***: A significant difference in the average concentration values of certain metabolite between LRKK2 and sPD groups was observed S43 Figure S5: AUROC plot for the top three ML-based predictive models for the classification of LRRK2 PD as compared to the corresponding controls.