Abstract
Abstract
Background: Irritable Bowel Syndrome (IBS) is heterogenous disorder of gut-brain interaction, with a key role for the dysregulated host-gut microbiota interplay. IBS subtyping is based only on symptoms of bowel habits, reflecting limited insight into underlying biological mechanisms.
Aims: This study aimed to define microbiota-based IBS phenotypes and to compare these to traditional stool-based subtyping.
Methods: The study utilised data from the Maastricht IBS cohort. Gut microbiota composition was analysed using shotgun metagenomic sequencing. Faecal volatile organic compounds (VOCs) were measured by gas chromatography mass spectrometry. Dietary intake and gastrointestinal and mental health symptoms were assessed using a food frequency questionnaire, the Dutch Healthy Diet-15 index, the Gastrointestinal Symptom Rating Scale, and Hospital Anxiety and Depression scores, respectively. Machine-learning approaches were applied to identify microbiota-based phenotypical clusters, which were compared with established Rome III subtypes, and associated with faecal VOCs, gastrointestinal symptom severity, diet and mental health.
Results: 178 IBS patients and 134 healthy controls were included. Gut microbiota composition distinguished IBS patients from healthy controls with an AUCROC 0·8. This discriminatory profile was not associated with Rome III subtypes, while statistically significant associations were found with faecal VOCs profiles (i.e. R=0·67, p=4·46e-4) and symptom severity scores of abdominal pain (p=0·05), reflux (p=0·03), and diarrhoea (p=0·01) and depression (p<0·001). Dietary associations varied across clusters.
Conclusion: These results suggest that gut microbiota profiling might provide a basis to define relevant IBS endotypes. Further exploration and validation efforts into this direction are needed using longitudinal studies to refine IBS patient stratification ultimately.