Gut microbial signatures of patients with diarrhea-predominant irritable bowel syndrome and their healthy relatives

Aims Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disorder, encompassing diarrhea-predominant irritable bowel syndrome (IBS-D). Here, we utilized 16S rDNA gene sequencing to identify potential microbial drivers of IBS-D.Methods and Results A total of 30 healthy relatives and 27 patients with IBS-D were recruited. Clinical data and fecal samples were collected from patients and controls. 16S rDNA gene sequencing was performed to obtain fecal bacterial data. Differences in community composition were evaluated utilizing analysis of similarity (ANOSIM) using Bray-Curtis dissimilarity. The Wilcoxon rank sum test was used to compare differences in taxa and functional pathways. Finally, the key gut microbiota was identified using the random forest algorithm. Gut microbiota diversity, estimated through the Observe, Chao1, and abundance-based coverage estimator (ACE) indices, was significantly lower in the IBS-D patients than in the healthy relatives. ANOSIM analysis further confirmed significant differences in the composition of the gut microbiota between IBS-D patients and healthy relatives, with an R value of 0.106 and a P-value of 0.005. Notably, the IBS-D patients exhibited a significant enrichment of specific bacterial genera, including Fusicatenibacter, Streptococcus, and Klebsiella, which may possess potential pathogenic properties. In particular, the bacterial genus Klebsiella demonstrated a positive correlation with irritable bowel syndrome severity scoring system scores. Conversely, healthy subjects showed enrichment of bacterial genera such as Alistipes, Akkermansia, and Dialister, which may be beneficial bacteria in IBS-D. Utilizing the random forest model, we developed a discriminative model for IBS-D based on differential bacterial genera. This model exhibited impressive performance, with an area under the curve value of 0.90. Additionally, our analysis did not reveal any gender-specific differences in the microbiota community composition among IBS-D patients.Conclusions Our findings offer preliminary insights into the potential relationship between intestinal microbiota and IBS-D. The identification model for IBS-D, grounded in gut microbiota, holds promising prospects for improving early diagnosis of IBS-D.