Gut microbiota in adults with moyamoya disease: characteristics and biomarker identification

Abstract

Background and purposeWhen it comes to the onset of moyamoya disease (MMD), environmental variables are crucial. Furthermore, there is confusion about the relationship between the gut microbiome, an environmental variable, and MMD. Consequently, to identify the particular bacteria that cause MMD, we examined the gut microbiome of MMD individuals and healthy controls (HC).MethodsA prospective case-control investigation was performed from June 2021 to May 2022. The fecal samples of patients with MMD and HC were obtained. Typically, 16S rRNA sequencing was employed to examine their gut microbiota. The QIIME and R softwares were used to examine the data. The linear discriminant analysis effect size analysis was used to determine biomarkers. Multivariate analysis by linear models (MaAsLin)2 were used to find associations between microbiome data and clinical variables. Model performance was assessed using the receiver operating characteristic curve and the decision curve analysis.ResultsThis investigation involved a total of 60 MMD patients and 60 HC. The MMD group’s Shannon and Chao 1 indices were substantially lower than those of the HC cohort. β-diversity was significantly different in the weighted UniFrac distances. At the phylum level, the relative abundance of Fusobacteriota/Actinobacteria was significantly higher/lower in the MMD group than that in the HC group. By MaAsLin2 analysis, the relative abundance of the 2 genera, Lachnoclostridium and Fusobacterium, increased in the MMD group, while the relative abundance of the 2 genera, Bifidobacterium and Enterobacter decreased in the MMD group. A predictive model was constructed by using these 4 genera. The area under the receiver operating characteristic curve was 0.921. The decision curve analysis indicated that the model had usefulness in clinical practice.ConclusionsThe gut microbiota was altered in individuals with MMD, and was characterized by increased abundance of Lachnoclostridium and Fusobacterium and decreased abundance of Bifidobacterium and Enterobacter. These 4 genera could be used as biomarkers and predictors in clinical practice.