Changes in the gut microbiome of patients with type a aortic dissection - Jiang, Fei; Cai, Meiling; Peng, Yanchun; Li, Sailan; Liang, Bing; Ni, Hong; Lin, Yanjuan (Frontiers in Microbiology, 2023)
Objective:To investigate the characteristic changes in the gut microbiota of patients with 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 AAD patients and control subjects. Using the Shannon index and Chao1 and Jaccard-PCoA, the gut microbiota composition of 20 AAD patients and 20 healthy controls matched for gender, age, BMI, and geographical region was compared. 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 by liquid chromatography–mass spectrometry (LC/MS). Spearman correlation analysis was conducted to ascertain the relationships of gut microbiome and SCFAs with the clinical characteristics of patients. Results: The differences in gut microbiota alpha diversity between AAD patients and healthy controls were not statistically significant (Shannon index: p=0.19; Chao1: p=0.4), but microbiota composition (Beta diversity) was significantly different between the two groups (Jaccard-PCoA, 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 was capable of predicting AAD from gut microbiota composition with an accuracy of 87.5%. The SCFA content of AAD patients was higher than that of the control group, with the difference being statistically significant (p<0.05). And the different microflora and SCFAs were positively correlated with inflammatory cytokines. Conclusion: We demonstrated, for the first time, the presence of significant differences in the gut microbiome of AAD patients and healthy controls. The differential microbiome exhibited high predictive potential towards AAD and was positively correlated with inflammatory cytokines. Our results pave the way for the development of preventive and therapeutic treatment methods for AAD patients.