Study on the characteristics and correlation of fecal microbiota and metabolites in patients with acute lung injury after cardiopulmonary bypass based on 16S rRNA sequencing and non-targeted metabolomics analysis.

ALI and NALI patients following CPB showed distinct differences in gut microbial community structure based on beta diversity analysis.

• A prospective, single-center, case-control design enrolled 53 post-CPB patients: 21 in the ALI group and 32 in the non-ALI (NALI) group.
• Anosim analysis showed R = 0.14, P = 0.004, indicating significant separation of microbial communities between groups.
• Permanova analysis showed R2 = 0.058, P = 0.008, confirming distinct differences in microbial community structure.
• Postoperative fecal samples were analyzed using 16S rRNA sequencing.

ALI patients exhibited significant shifts in phylum-level gut microbiota composition compared to NALI patients.

• Bacillota (Firmicutes) was significantly increased in the ALI group.
• Bacteroidota was significantly reduced in the ALI group.
• Actinomycetota was also significantly reduced in the ALI group.
• These phylum-level differences were identified from 16S rRNA sequencing of postoperative fecal samples.

At the genus level, specific bacteria were differentially abundant between ALI and NALI patients.

• Streptococcus and Enterococcus were enriched in the ALI group.
• Bacteroides and Akkermansia were diminished in the ALI group.
• These genus-level differences were identified using 16S rRNA sequencing-based microbiome analysis of postoperative fecal samples.

Metabolomics analysis identified 130 differentially expressed metabolites between ALI and NALI patients, with the majority reduced in ALI.

• A total of 130 differentially expressed metabolites were identified using non-targeted metabolomics analysis.
• 109 of the 130 differentially expressed metabolites were significantly reduced in the ALI group.
• The metabolic differences primarily involved amino acid metabolic pathways, including phenylalanine, tryptophan, and tyrosine metabolism.
• Metabolomic analyses were performed on postoperative fecal samples.

A random forest model using gut microbiota genera demonstrated predictive value for ALI after CPB.

• Genera including Bacteroides, Corynebacterium, and Lactobacillus were identified as having high predictive value for ALI.
• The random forest model achieved an AUC > 0.7 for ALI prediction.
• The model was based on genus-level microbiota data from postoperative fecal samples of 53 CPB patients.

Combined microbiota-metabolite analysis revealed significant correlations between specific genera and differentially expressed metabolites, suggesting a role for the gut-lung axis in ALI development.

• Significant correlations were identified between specific genera and differentially expressed metabolites.
• These correlations were identified by integrating 16S rRNA microbiome data with non-targeted metabolomics data.
• The findings suggest a potential role for the gut-lung axis in the development of ALI following CPB.
• Both gut microbiota disruption and metabolic dysfunction were closely associated with adverse clinical outcomes in post-CPB ALI patients.