Gut microbiome composition differs between aortic stenosis and aortic regurgitation patients.

The gut microbiome has emerged as an important modulator of cardiovascular diseases, yet data on aortic valve disorders, particularly aortic stenosis (AS), remain limited. This study aimed to characterize gut microbiome differences in patients with bicuspid (BS) and tricuspid aortic stenosis (TS) using aortic regurgitation (AR) patients as a clinically comparable control group with similar age distribution, comorbidities, and metabolic profiles. A total of 122 patients were included in this prospective cross-sectional study: 33 AR, 22 BS, and 67 TS patients. Microbiome profiling was conducted from anal swabs using 16S rRNA gene sequencing. Beta diversity was assessed via UniFrac distances, and computed redundancy analysis was performed using linear discriminant analysis effect size. The groups were largely homogeneous regarding most clinical characteristics; AR patients showed only marginally worse renal function, while BS patients were slightly younger with fewer cases of diabetes. In beta diversity analyses, both TS and BS patients exhibited clearly distinct microbiome compositions compared with AR controls, independent of clinical parameters used as potential confounders. TS and BS patients differed only minimally from each other. AR patients showed higher abundances of Bacteroides, Faecalibacterium, Lachnoclostridium, and Alistipes, whereas AS patients exhibited increased levels of Corynebacterium, Anaerococcus, Peptoniphilus, and Finegoldia. Co-abundance network analysis revealed that AS patients displayed an extensive and highly interconnected bacterial network, characterized by strong correlations among taxa such as Bacteroides, Alistipes, Parabacteroides, and Faecalibacterium, rather than isolated changes in individual taxa. AR patients provide a clinically suitable control group for AS. AS patients show a distinct microbiome composition and a highly interconnected microbial network with three major hubs, warranting further mechanistic investigation.

© 2026. The Author(s).

PMID: 42493527