A62 UNCOVERING MICROBIAL DETERMINANTS OF IBS FLARE-UPS: COHORT-LEVEL VS PATIENT-SPECIFIC APPROACHES
Bibliographic record
Abstract
Abstract Background Irritable bowel syndrome (IBS) is a complex gastrointestinal disorder with a global prevalence of 3.8% (5.8% in Canada, using Rome IV criteria), with the gut microbiota implicated in its pathophysiology. Despite extensive research in the last decade, the microbial mechanisms underlying symptom flares remain unclear. We showed in the previous analysis of our longitudinal study that the strongest factors responsible for separating IBS flare-ups from asymptomatic periods were stool appearance (Bristol stool scale) and somatization, followed by pain scores. Although there were no significant differences in alpha microbial diversity, beta diversity changes associated to symptom flares occurred in 25% of patients. Aims Explore cohort-level versus personalized analyses in identifying microbial and metabolic predictors of flares in IBS patients. Methods Twenty-eight IBS patients (IBS-D n=20; IBS-C n=8) and 10 healthy controls (HC) were followed for 25 weeks, with weekly symptom assessment (Birmingham IBS, HAD, STAI questionnaires) and stool sample collected (950 samples), analyzed by 16S rRNA sequencing and metabolomics profiling. Discriminatory taxa and metabolites among groups and within individuals were identified using sparse PLS-DA and mixed-effect models. Results The integrated stool microbiome-metabolome analysis revealed 6 taxa and 8 metabolites in IBS-D, and 10 taxa and 18 metabolites in IBS-C cohort, that differed from HC (adjusted p<0.05). However, when comparing asymptomatic and flare-up timepoints within IBS cohorts, these microbial taxa and metabolites lost significance. Variance partitioning from mixed models indicated that individuals accounted for a larger share of the variance than cohort-level differences. Consequently, we performed time-course individual analysis in several patients, which identified about 40 bacterial taxa and 33 metabolites being altered during flare-up timepoints among both IBS-D and IBS-C patients. Furthermore, alterations in specific metabolic pathways were more prominent in individuals than in cohort comparisons. Conclusions Our results suggest that personalized analysis is essential for better understanding of symptom dynamics within individual patients. Group-level differences may highlight general alterations in microbiome that make IBS patients more susceptible to the effects of transient taxa and metabolites identified in individual analyses, which might then contribute to symptom flares in that individual. While these microbial and metabolic predictors still require further investigation, they emphasize the complexity of IBS pathophysiology and the need for a hybrid approach to improve biomarker discovery. Funding Agencies CIHR
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".