Changes in the gut microbiome of patients with type a aortic dissection

• Published in: Frontiers in microbiology Vol. 14; p. 1092360
• Main Authors: Jiang, Fei, Cai, Meiling, Peng, Yanchun, Li, Sailan, Liang, Bing, Ni, Hong, Lin, Yanjuan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 22.02.2023
• Subjects: 16S rDNA sequencing; aortic dissection; gut microbiome; metabolomics; Microbiology; SCFAs; 16S rDNA sequencing; SCFAs; gut microbiome; metabolomics; aortic dissection
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2023.1092360

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. 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. The differences in gut microbiota alpha diversity between patients with AAD and the healthy controls were not statistically significant (Shannon index:  = 0.19; Chao1:  = 0.4); however, the microbiota composition (beta diversity) was significantly different between the two groups (Anosim,  = 0.001). was enriched at the phylum level, and the SCFA-producing genera , , , and and inflammation-related genera and 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 (  < 0.05). The different microflora and SCFAs were positively correlated with inflammatory cytokines. 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.