Dietary Emulsifier–Induced Low-Grade Inflammation Promotes Colon Carcinogenesis
Bibliographic record
Abstract
The increased risks conferred by inflammatory bowel disease (IBD) to the development of colorectal cancer gave rise to the term "colitis-associated cancer" and the concept that inflammation promotes colon tumorigenesis. A condition more common than IBD is low-grade inflammation, which correlates with altered gut microbiota composition and metabolic syndrome, both present in many cases of colorectal cancer. Recent findings suggest that low-grade inflammation in the intestine is promoted by consumption of dietary emulsifiers, a ubiquitous component of processed foods, which alter the composition of gut microbiota. Here, we demonstrate in a preclinical model of colitis-induced colorectal cancer that regular consumption of dietary emulsifiers, carboxymethylcellulose or polysorbate-80, exacerbated tumor development. Enhanced tumor development was associated with an altered microbiota metagenome characterized by elevated levels of lipopolysaccharide and flagellin. We found that emulsifier-induced alterations in the microbiome were necessary and sufficient to drive alterations in major proliferation and apoptosis signaling pathways thought to govern tumor development. Overall, our findings support the concept that perturbations in host-microbiota interactions that cause low-grade gut inflammation can promote colon carcinogenesis. Cancer Res; 77(1); 27-40. ©2016 AACR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".