NeuralCipher Research overview
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BackgroundParkinson's disease (PD) is a complex neurodegenerative disorder characterized by multifaceted molecular dysregulation. Integrating genetic approaches with metabolomics may help to systematically investigate potential links between metabolites, inflammatory proteins, and PD risk.MethodsIn this pilot discovery phase, untargeted Liquid Chromatography-Mass Spectrometry (LC-MS) was conducted in a small cohort (15 PD patients and 10 healthy controls). Differentially expressed metabolites (DEMs) were identified using variable importance in projection (VIP) > 1, |log2FC| ≥ 1, and false discovery rate (FDR)-adjusted q-values ResultsA total of 3,537 metabolites were identified, of which 570 were classified as DEMs, representing the features remaining significant after multiple-testing correction. Integration of Random Forest and LASSO identified 3-phenylpropionylglycine as a key candidate metabolite. KEGG analysis highlighted caffeine metabolism as the top enriched pathway. After FDR correction, MR analyses suggested inverse associations between genetically predicted levels of 3-phenylpropionylglycine (PFDR = 0.0077, OR = 0.82) and 7-methylxanthine (PFDR = 0.0349, OR = 0.83) and PD risk. Mediation analysis further indicated that SULT1A1 may partially mediate the association between 3-phenylpropionylglycine and PD.ConclusionThis exploratory study integrates pilot-scale metabolomics with genetic analyses to identify candidate metabolic signals and inflammatory pathways linked to PD. However, given the small metabolomics sample size, cross-cohort data integration, and potential biological mismatch between plasma-derived metabolites and genetically predicted metabolite levels, these findings should be considered hypothesis-generating and interpreted with caution.
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