Dietary Relationships between Obesity and Inflammatory Bowel Diseases: A Narrative Review of Diets Which May Promote Both Diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: The pandemic of obesity preceded global spread of Inflammatory Bowel diseases by almost 2 decades. A pathogenic relationship has been described between obesity and inflammatory bowel diseases, but Crohn`s disease may be selectively impacted. The role of diet in pathogenesis has also gained significant support in the last few decades. This review explores dietary relationships to account for epidemiological observations. Quantifiable indices for diets have been described including a glycemic index, inflammatory indices and levels of food processing. Meta-analyses have been published which examine each for effects on obesity and co-morbidities as well as Crohn's disease and ulcerative colitis. This review suggests that ultra-processed foods provide the best link between obesity and Crohn's disease explaining epidemiological observations. However, the other 2 types of dietary indices likely contribute to ulcerative colitis as well as to co-morbidities related to both obesity and inflammatory bowel diseases. The term ultra-processed foods cover a large number of additives and extensive work is needed to define individual or combined harmful effects. Furthermore, the interactions among the 3 main indices need clarification in order to precisely apply therapeutic diets to both diseases (obesity and inflammatory bowel disease).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".