Differences in Fecal Microbiomes and Metabolomes of People With vs Without Irritable Bowel Syndrome and Bile Acid Malabsorption

Abstract

Background & aims: Irritable bowel syndrome (IBS) is a heterogeneous disorder, but diagnoses and determination of subtypes are made based on symptoms. We profiled the fecal microbiomes of patients with and without IBS to identify biomarkers of this disorder.

Methods: We collected fecal and urine samples from 80 patients with IBS (Rome IV criteria; 16-70 years old) and 65 matched individuals without IBS (control individuals), along with anthropometric, medical, and dietary information. Shotgun and 16S ribosomal RNA amplicon sequencing were performed on feces, whereas urine and fecal metabolites were analyzed by gas chromatography and liquid chromatography-mass spectrometry. Co-occurrence networks were generated based on significant Spearman correlations between data. Bile acid malabsorption (BAM) was identified in patients with diarrhea by retention of radiolabeled selenium-75 homocholic acid taurine.

Results: Patients with IBS had significant differences in network connections between diet and fecal microbiomes compared with control individuals; these were accompanied by differences in fecal metabolomes. We did not find significant differences in fecal microbiota composition among patients with different IBS symptom subtypes. Fecal metabolome profiles could discriminate patients with IBS from control individuals. Urine metabolomes also differed significantly between patients with IBS and control individuals, but most discriminatory metabolites were related to diet or medications. Fecal metabolomes, but not microbiomes, could distinguish patients with IBS with vs those without BAM.

Conclusions: Despite the heterogeneity of IBS, patients have significant differences in urine and fecal metabolomes and fecal microbiome vs control individuals, independent of symptom-based subtypes of IBS. Fecal metabolome analysis can be used to distinguish patients with IBS with vs those without BAM. These findings might be used for developing microbe-based treatments for these disorders.

Keywords: Machine Learning; Ruminococcus gnavus; SeHCAT; Stratification.