Environmental risk factors of inflammatory bowel disease: toward a strategy of preventative health
Bibliographic record
Abstract
The pathogenesis of inflammatory bowel disease (IBD) involves a complex interplay between genetic, environmental, and microbial factors. Many of these environmental determinants are modifiable, offering opportunities to prevent disease or delay its onset. Advances in the study of preclinical IBD cohorts offer the potential to identify biomarkers that predict individuals at high risk of developing IBD, enabling targeted environmental interventions aimed at reducing IBD incidence. This review summarizes findings from 79 meta-analyses on modifiable environmental factors associated with the development of IBD. Identified risk factors include smoking, Western diets, ultra-processed foods, and early life antibiotic use, while protective factors include breastfeeding, Mediterranean diets rich in fiber, plant-based foods, and fish, along with an active physical lifestyle. Despite the promise shown by observational data, interventional or randomized controlled studies evaluating the efficacy of modifying environmental risk factors remain limited and mostly focus on dietary intervention. This review aims to inform the design of higher quality interventional and randomized controlled studies for disease prevention while providing actionable guidance to healthcare providers on reducing the risk of developing IBD through environmental modifications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".