Exploring metabolic candidates and inflammatory mediation in Parkinson's disease: a pilot integrative metabolomics and Mendelian randomization study.
Exploring metabolic candidates and inflammatory mediation in Parkinson's disease: a pilot integrative metabolomics and Mendelian randomization study.
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The study site has not been established. Author addresses may differ from where the research occurred.
Dalian, CN · Author affiliation
Department of Neurology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.Location evidence
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Original abstract
BACKGROUND: Parkinson'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. METHODS: In 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 < 0.05 (Benjamini-Hochberg method), representing the metabolites remaining significant after multiple-testing correction applied directly at the metabolite discovery stage. Prioritized metabolites were selected by Random Forest, Least absolute shrinkage and selection operator (LASSO) regression, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Two-sample Mendelian randomization (MR) was performed using PD Genome-Wide Association Study (GWAS) data from FinnGen R12 (European ancestry: 5,861 cases, 494,487 controls) to evaluate putative causal associations, with FDR correction applied separately within the metabolite and inflammatory protein exposure sets. Mediation MR was used to explore potential mediating roles of 91 circulating inflammatory proteins. RESULTS: A 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. CONCLUSION: This 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.