NeuralCipher Research overview
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BackgroundParkinson's disease with motor complications (PD-MC) lacks effective diagnostic and therapeutic strategies. The perturbations of the gut microbiota and plasma metabolites are closely associated with the etiopathogenesis of PD. However, whether fecal microbiome dysbiosis and changed plasma metabolites are involved in PD progression, particularly in the development of PD-MC, is still unclear.MethodsIn this study, we performed an extensive multiomics analysis involving 108 PD patients for 16S rRNA gut microbiome profiling and 246 PD patients for plasma nontargeted metabolomics. Our findings revealed distinct gut microbiota and plasma metabolites associated with PD-MC. Utilizing these discriminative features, we developed a multivariate diagnostic model for PD-MC. The relationships between differential metabolites and microorganisms were evaluated using Spearman correlation analysis. Functional interpretation of the key metabolites was conducted through enrichment and pathway analysis, employing the KEGG and SMPDB databases.ResultsPD-MC patients had distinct gut microbial signatures as compared with PD without motor complications (PD-NMC) individuals and were increased in fecal Lactobacillus, Limosilactobacillus, Bifidobacterium, and Ligilactobacillus genera along with depleted Agathobacter. Moreover, metabolomic analysis revealed the differences in plasma 3-deoxysappanchalcone (3-DSC), 1,3-Dimethyluracil (1,3-DTl), Leucine, and N-Acetylisoleucine (N-AIL), Dodec-6-enoic acid (D-6-E), N-butyl Oleate (N-BO), and 4-hydroxyundecanoic acid (4-HUA) in PD-MC compared to PD-NMC. Spearman correlation analysis showed that the fecal microbiota aberrations in PD-MC patients were linked to plasma metabolic changes, indicating the association between key microbial populations and metabolomic profiles in PD-MC.ConclusionsThis study underscores the value of employing integrated multiomics profiling of the fecal microbiome and plasma metabolome to enhance the mechanistic understanding of PD-MC and to identify potential diagnostic biomarkers.
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