Identifying Potential Targets for the Interception of Inflammatory Bowel Disease: Toward Precision Prevention
Bibliographic record
Abstract
There is growing recognition that inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is preceded by a prolonged preclinical phase marked by subtle but measurable changes in the immune system, gut microbiome, and epithelial barrier function. These early alterations, often detectable years before diagnosis, offer a window of opportunity for disease interception. In this review, we examine the current evidence for environmental, microbial, and molecular factors that may contribute to the initiation of IBD, with a particular focus on modifiable risk pathways. We discuss preventive strategies across different levels of risk-from lifestyle and environmental interventions in the general population to more targeted approaches in individuals with familial predisposition, such as first-degree relatives. We also highlight recent findings on emerging biomarkers, including anti-flagellin antibodies, anti-GM-CSF autoantibodies, glycome, and integrin-targeted immune responses, that could guide precision prevention efforts. While most evidence to date has focused on CD, we also review preclinical insights relevant to UC. As the field moves toward earlier identification of at-risk individuals, the concept of "precision prevention"-matching interventions to individual risk and biology-may ultimately shift the paradigm of IBD care from treatment to prevention.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".