Abstract Body: Introduction: The human gut microbiome comprises a complex network of microorganisms and microbially-derived metabolites. The association of the gut microbiome with peripheral artery disease (PAD) is poorly understood. The goal of this study is to identify correlations of gut microbial markers with clinical features of PAD.
Methods: Study participants aged ≥ 40 years with documented PAD and non-PAD controls each provided fecal samples. Microbial DNA extracted from fecal samples was analyzed using shotgun metagenomics. Untargeted fecal metabolite quantification was performed using nuclear magnetic resonance. Raw sequence data were quality filtered using the KneadData pipeline followed by taxonomic annotation using Kraken2 against the UHGG database (v2.0.2) and species abundance estimation using Bracken. A species network was constructed using SparCC. Modules of positively correlated species were constructed and analyzed using the Weighted Gene Co-expression Network Analysis package in R. The first principal component of each module (ME) and metabolite concentration and sample traits were analyzed by Spearman correlations. Network visualization was done in Cytoscape.
Results: Among 78 patients with PAD, 40 (51.3%) had chronic limb threatening ischemia (CLTI) and 38 (48.7%) had symptoms of claudication. A network consisting of 1644 species (nodes) and 23,108 connections (edges) was clustered into 17 modules that were further grouped into 4 meta-modules. Node degree and clustering coefficients were reduced with increased PAD severity (p<0.001). The majority of modules correlated with claudication were also positively correlated with concentrations of specific microbe-derived metabolites, including butyrate, nicotinate, and glutamate. The total relative abundance of species belonging to these modules was higher in the claudication group than in the CLTI group. A module that was negatively associated with CLTI was enriched with known butyrate producers such as Faecalibacterium spp., Agathobaculum butyriciproducens and Ruminococcus bromii, whereas a module that was positively associated with CLTI was enriched with oral microbes such as Granulicatella spp., Streptococcus oralis, Actinomyces. oris, and Oribacterium. sinus.
Conclusions: In a species correlation network analysis, we identified specific microbial and metabolomic signatures linked to the clinical severity of PAD, underscoring the importance of interaction patterns beyond abundance-based changes.