Dietary Inflammatory Potential and the Risk of Serrated and Adenomatous Colorectal Polyps
Bibliographic record
Abstract
Studies of dietary inflammation potential and risks of colorectal cancer precursors are limited, particularly for sessile serrated lesions (SSLs). This study examines the association using the energy-adjusted dietary inflammatory index (E-DIITM), a measure of anti- and/or pro-inflammatory diet, in a large US colonoscopy-based case-control study of 3246 controls, 1530 adenoma cases, 472 hyperplastic polyp cases, and 180 SSL cases. Odds ratios (ORs) and 95% confidence intervals (CIs) were derived from logistic regression models. Analyses were stratified by participant characteristics, and urinary prostaglandin E2 metabolite (PGE-M) and high-sensitivity plasma C-reactive protein (hs-CRP) levels, inflammation biomarkers. Highest E-DII™ intake was associated with significantly increased risks of colorectal adenomas (OR 1.36, 95% CI 1.11, 1.67), and hyperplastic polyps (OR 1.43, 95% CI 1.06, 1.98), compared with participants consuming the lowest E-DII™ quartile. A similar, but non-significant, increased risk was also observed for SSLs (OR 1.41, 95% CI 0.82, 2.41). The positive association was stronger in females (pinteraction <0.001), normal weight individuals (ptrend 0.01), and in individuals with lower inflammatory biomarkers (ptrend 0.02 and 0.01 for PGE-M and hs-CRP, respectively). A high E-DII™ is associated with colorectal polyp risk, therefore promoting an anti-inflammatory diet may aid in preventing colorectal polyps.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".