Research overview
The text below is the source article’s original abstract under a verified reuse license. cc by
IntroductionSingle-omics studies have deepened our understanding of disease-related molecular processes, though integrated approaches are needed to uncover cross-system interactions. We previously reported changes in the serum metabolome and gut microbiome in Parkinson's disease (PD). To build on these findings, we conducted a multi-omics integration analysis to examine the interplay between the gut microbiome and human metabolism in PD.MethodsIn a community-based study of 113 PD patients from rural California, microbiome profiles were obtained via 16S rRNA gene sequencing of fecal samples. Serum metabolomic profiles were generated using untargeted high-resolution LC-MS. Residual matrices of metabolomics data were extracted after adjusting for age, sex, racial minority status, and study wave. We identified PD-associated bacterial genera and summarized their abundance using principal component analysis. Using this summary score as the dependent variable, we performed partial least squares (PLS) regression to identify serum metabolites associated with the PD-related gut microbiome. Pathway enrichment analysis was then conducted on selected metabolite features from the PLS model.ResultsWe identified 266 metabolite features and annotated 29 metabolic compounds associated with PD-related microbes (p ConclusionOur multi-omics integration analysis revealed that PD-associated gut microbiota are involved in host lipid metabolism, immune-related pathways, and potentially vitamin B-mediated regulation of kynurenine pathway metabolism, providing insights into potential microbiome-metabolome interactions in PD pathophysiology.
Scientific assessment
Research question
Assess the question within the original study’s scope.
Methods & sample
Check the source article for study design and sample details.
Key findings
An editorially approved findings summary is not yet published for this record.
Limitations
Generalizability, bias and clinical meaning require separate assessment.
Methods, samples and clinical conclusions are not inferred from bibliographic metadata.