Low Carbohydrate Diet Containing Soy Protein and Fish Oil Reduces AOM/DSS‐Induced Colon Cancer, in Part, by an Acetate‐Mediated Reduction in T<sub>H</sub>17 Cell Differentiation
Bibliographic record
Abstract
ABSTRACT Since our previous studies found a low‐carbohydrate (CHO) diet containing soy protein and fish oil (i.e., 15%Amylose/Soy/FO) significantly reduced tobacco carcinogen‐induced lung nodules in A/J mice, breast tumors in C3(1)/Tag mice, and myelomalignancy in miR‐146a knockout mice, we asked herein if this CHO diet could also reduce colorectal cancer. We tested the efficacy of the 15%Amylose/Soy/FO diet in preventing colitis‐induced colorectal cancer using an azoxymethane/dextran sodium sulfate BALB/c mouse model. The 15%Amylose/Soy/FO diet significantly reduced colon tumor numbers compared to a Western diet and this was associated with a reduction in blood glucose, and a trend towards an increase in plasma β‐hydroxybutyrate and an increase in liver fatty acid synthase, suggesting a systemic metabolic shift from glucose to fatty acids as an energy source. In addition, our CHO diet reduced proinflammatory cytokines, induced a marked change in the fecal microbiome, an increase in cecal and fecal acetate, and a reduction in interleukin‐17A expressing cells in the colonic tumors of mice on the 15%Amylose/Soy/FO diet. Taken together, our results suggest the 15%Amylose/Soy/FO diet ameliorates colitis and prevents the subsequent development of colorectal cancer, in part by an acetate‐mediated reduction in TH17 cell development.
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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 | 0.001 |
| 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".