Integrating gut microbiome and host transcriptomics for the personalized management of IBD
Bibliographic record
Abstract
Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), affects an estimated 6.8 million individuals worldwide. Although biological or small molecule drug therapies can improve patient outcomes, loss of response to treatment over time remains high, highlighting the need for new precision medicine strategies. Dysbiosis of the gut microbiome is characterized by the loss of beneficial microbes and an overgrowth of pro-inflammatory pathobionts. In IBD, gut dysbiosis contributes to chronic intestinal inflammation via altered metabolite profiles and epithelial barrier disruption. Recent advancements in multi-omics integration offer approaches to better understand the pathogenesis of IBD. Epigenomic studies have revealed disease-specific DNA methylation and enhancer activation patterns that reshape immune pathways and compromise epithelial barrier integrity, key mechanisms in IBD pathophysiology. These molecular signatures allow for the stratification of IBD patients into distinct subgroups, allowing for more targeted therapeutic strategies. Here we explore the potential benefits of integrating gut microbiome and both host transcriptomics and epigenomics to improve disease management in IBD patients. While challenges remain - such as data standardization, computational complexity, and cost - the progression of multi-omics methodologies is expected to improve patient outcomes by reducing high treatment failure rates in IBD patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".