A ketogenic diet rich in fish oil is superior to other fats in preventing NNK-induced lung cancer in A/J mice
Bibliographic record
Abstract
Given that ketogenic diets (KDs) are extremely high in dietary fat, we compared different fats in KDs to determine which was the best for cancer prevention. Specifically, we compared a Western and a 15% carbohydrate diet to seven different KDs, containing either Western fats or fats enriched in medium chain fatty acids (MCTs), milk fat (MF), palm oil (PO), olive oil (OO), corn oil (CO) or fish oil (FO) for their ability to reduce nicotine-derived nitrosamine ketone (NNK)-induced lung cancer in mice. While all the KDs tested were more effective at reducing lung nodules than the Western or 15% carbohydrate diet, the FO-KD was most effective at reducing lung nodules. Correlating with this, mice on the FO-KD had low blood glucose and the highest β-hydroxybutyrate level, lowest liver fatty acid synthase/carnitine palmitoyl-1a ratio and a dramatic increase in fecal Akkermansia. We found no liver damage induced by the FO-KD, while the ratio of total cholesterol/HDL was unchanged on the different diets. We conclude that a FO-KD is superior to KDs enriched in other fats in reducing NNK-induced lung cancer, perhaps by being the most effective at skewing whole-body metabolism from a dependence on glucose to fats as an energy source.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".