Review article: changes in the epidemiology of inflammatory bowel disease—clues for aetiology
Bibliographic record
Abstract
BACKGROUND: The changing epidemiology of inflammatory bowel disease (IBD) in both the developed and developing worlds may provide insights into disease aetiology. Factors that impact on the gut microbiome are leading aetiological candidates. AIM: To review epidemiological studies and trends that identify risk factors for the development of IBD. METHODS: Studies that identified factors associated with the development of IBD differentially in children and adults were reviewed. There was a focus on epidemiological studies and on studies that involve the gut microbiome. RESULTS: Use of antibiotics has been shown to be associated with development of Crohn's disease in childhood (odds ratio, OR = 2.75, 95% CI 1.72-4.38). Breastfeeding has been protective against developing IBD (OR=0.69, 95% CI 0.51-0.94), but there is a paucity of data exploring duration of breastfeeding and timing of introduction of bottled milk or table food. Antibiotics and diet changes can also impact on adults enhancing the risk for IBD. Both smoking (OR=1.76, 95% CI 1.40-2.22) and oral contraceptives (relative risk=1.46, 95% CI 1.26-1.70) increase the risk for Crohn's disease and their use is associated with worse outcomes in Crohn's disease. It is unclear if their impact is mediated through the gut microbiome. CONCLUSIONS: A leading aetiological clue for IBD based on epidemiological studies is the antecedent use of antibiotics both for children and adults. Some dietary changes may be a risk for adults but there is a paucity of dietary data in children prior to IBD development. Both antibiotic use and dietary changes have the potential to impact the gut microbiome, which in turn can alter the gut immune response.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".