AUTHOR=Jiang Fei , Cai Meiling , Peng Yanchun , Li Sailan , Liang Bing , Ni Hong , Lin Yanjuan TITLE=Changes in the gut microbiome of patients with type a aortic dissection JOURNAL=Frontiers in Microbiology VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2023.1092360 DOI=10.3389/fmicb.2023.1092360 ISSN=1664-302X ABSTRACT=Objective

To investigate the characteristic changes in the gut microbiota of patients with type A aortic dissection (AAD) and provide a theoretical basis for future microbiome-oriented interventional studies.

Methods

High-throughput 16S rDNA sequencing was performed on the stool samples of patients with and without (healthy control subjects) AAD. Using alpha and beta diversity analysis, we compared the gut microbiota composition of 20 patients with AAD and 20 healthy controls matched for gender, age, BMI, and geographical region. The accuracy of AAD prediction by differential microbiome was calculated using the random forest machine learning model. Targeted measurement of the plasma concentration of short-chain fatty acids (SCFAs), which are the main metabolites of the gut microbiome, was performed using gas chromatography–mass spectrometry (GC–MS). Spearman’s correlation analysis was conducted to determine the relationships of gut microbiome and SCFAs with the clinical characteristics of subjects.

Results

The differences in gut microbiota alpha diversity between patients with AAD and the healthy controls were not statistically significant (Shannon index: p = 0.19; Chao1: p = 0.4); however, the microbiota composition (beta diversity) was significantly different between the two groups (Anosim, p = 0.001). Bacteroidota was enriched at the phylum level, and the SCFA-producing genera Prevotella, Porphyromonas, Lachnospiraceae, and Ruminococcus and inflammation-related genera Fenollaria and Sutterella were enriched at the genus level in the AAD group compared with those in the control group. The random forest model could predict AAD from gut microbiota composition with an accuracy of 87.5% and the area-under-curve (AUC) of the receiver operating characteristic curve was 0.833. The SCFA content of patients with AAD was higher than that of the control group, with the difference being statistically significant (p < 0.05). The different microflora and SCFAs were positively correlated with inflammatory cytokines.

Conclusion

To the best of our knowledge, this is the first demonstration of the presence of significant differences in the gut microbiome of patients with AAD and healthy controls. The differential microbiome exhibited high predictive potential toward AAD and was positively correlated with inflammatory cytokines. Our results will assist in the development of preventive and therapeutic treatment methods for patients with AAD.