The Impact of Ketogenic Diet on Colorectal Cancer Progression and the Co-evolution of Gut Microbiota: A Research Protocol
Bibliographic record
Abstract
Introduction: To date, the ketogenic diet (KD) has been shown to improve certain health conditions such as seizure. These positive effects have been partly mediated through the gut microbiome. However, research on KD’s impact on colorectal cancer (CRC) and the gut microbiome of cancer patients that use KD has been scant. This study aims to investigate the role of KD in the co-evolution of gut microbial composition and CRC progression. We hypothesize that KD alters overall species diversity through either elimination of harmful bacterial genera or perpetuation of beneficial gut microbiota which could ultimately be the mechanism underlying positive CRC outcomes. Methods: In order to test this hypothesis, we propose a murine study using ApcMin/+ (multiple intestinal neoplasia) CRC mouse models in C57BL/6J background. The mice will either be given normal chow (control group) or KD (87% fat) for 8 weeks. Mice will be euthanized at the end of the experiment and analyzed in terms of polyp size and polyp number in the small intestine and colon. Frozen colon tissue will also be used to extract mRNA for quantitative polymerase chain reaction (qPCR) analysis of TH17 cytokine production. Gut microbiome composition will be analyzed by sequencing of 16S rRNA genes. To compare microbiome structure between diet groups, alpha diversity will be used to measure the differences in gut microbial structure in the control and experimental groups. Results: We would expect that mice fed a KD would have altered microbiota diversity, a decreased level of cytokine production, as well as fewer and smaller polyps (as measured in the small intestine and colon). Discussion: Analysis of the gut microbiota post-treatment, in conjunction with assessment of cytokine levels will help to set correlations between microbial gut activity and CRC progression. Conclusion: The results of this experiment could give insight into the impact of KD on development and progression of CRC; which could be used to develop therapeutic or dietary interventions.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".