A11 MICROBIAL AND METABOLOMIC PROFILES IDENTIFY SUBSETS OF DIARRHEA PREDOMINANT IRRITABLE BOWEL SYNDROME
Bibliographic record
Abstract
Abstract Background Irritable bowel syndrome (IBS) patients are subtyped by predominant bowel habit rather than pathophysiological mechanisms and this may underlie challenges in identifying more effective targets for designing new treatments. Metabolomics and microbial analysis can distinguish IBS patients from healthy controls but it is unknown if they can identify pathophysiological IBS subgroups. Aims To phenotype subgroups of IBS patients using metabolomics and microbial analysis and determine if these subgroups have different underlying pain signalling mechanisms. Methods Symptom history, stool and urine were collected from 30 diarrhea predominant (IBS-D) and 30 constipation predominant (IBS-C) IBS patients (Rome IV). Liquid Chromatography-Mass Spectrometry quantified 130 metabolites in stool and urine. The GA-map™ Dysbiosis Test targeting ≥300 bacteria on different taxonomic levels was used to identify stool microbial composition. Multivariate OPLS discriminatory analysis assessed metabolomics and microbial profiles. To assess potential effects on pain signalling, the effect of stool supernatant on dissociated dorsal root ganglia (DRG) neuron responses to capsaicin (10nM) was assessed using Ca2+ imaging. Results Within both IBS-D and IBS-C, combined stool/urine metabolomic profiles of patients with a dysbiosis-like (DL) IBS (onset following antibiotics, enteric infection, or travel) were distinct from patients with a non-DL IBS onset (IBS-D R2=0.7, Q2=0.5; IBS-C R2=0.5, Q2=0.4); fecal glutamic acid and urinary pyruvic acid were the main metabolites driving separation. However, microbial profiles of DL vs non-DL onset could only be discriminated in IBS-D (R2=0.8, Q2=0.4). In the patients with a DL IBS onset, stool metabolomic profiles of the 7 IBS-C discriminated from the 8 IBS-D patients (R2=0.9, Q2=0.8). Profile differences were not seen between IBS-C and IBS-D with a non-DL onset of IBS. In preliminary studies, incubation of DRG neurons with stool supernatant from 1 DL IBS-D and 1 non-DL IBS-D increased peak [Ca2+]i responses to capsaicin compared to incubation with media (DL: 5.5±0.9 vs 2.3±0.7; non-DL: 6.9±0.7 vs 3.9±0.4% ΔF/F). Similarly, the number of responsive neurons to capsaicin was increased after incubation with IBS stool supernatant vs media (DL: 27% vs 8%; non-DL 19% vs 12%). Conclusions Different metabolomic and bacterial profiles between DL and non-DL onset of IBS-D suggests a novel means to better phenotype clinically defined IBS subgroups. While initial results with stool supernatants from both a DL and non-DL IBS-D patient suggest increased pain signalling in DRG neurons, more studies are needed to determine if there are differences between these two subgroups as well as healthy controls. Funding Agencies CIHRSoutheastern Ontario Academic Medical Organization (SEAMO)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".