The Pros and Cons of Low Carbohydrate and Ketogenic Diets in the Prevention and Treatment of Cancer
Bibliographic record
Abstract
Ketogenic diets are low carbohydrate (CHO), high fat diets that are currently very popular for weight loss. Since cancer cells typically consume far more glucose than normal cells, low CHO diets are currently being considered as possible therapeutic regimens to manage cancer. However, our understanding of the safety and efficacy of such CHO-restricted diets in the prevention and treatment of cancer is still in its infancy. In this perspective we provide an overview of the current state of knowledge regarding the use of low CHO diets in the prevention and treatment of cancer. We also highlight the gaps in our knowledge regarding the potential usefulness of low CHO diets in cancer. While pre-clinical rodent studies have provided convincing evidence that CHO restriction may be effective in reducing cancer growth, there has not been sufficient attention given to the effect of these low CHO diets, that are often high in fats and low in soluble fiber, on inflammation. This is important, given that different fats have distinct effects on inflammation. As well, we demonstrate that short chain fatty acids, which are produced via the fermentation of fiber by our gut microbiome, have more anti-inflammatory properties than β-hydroxybutyrate, a ketone body produced during nutritional ketosis that is touted to have anti-inflammatory activity. Since chronic inflammation is strongly associated with cancer formation, defining the type of fats in low CHO diets may contribute to our understanding of whether these diets may work simply by reducing glucose bioavailability, or via modulation of inflammatory responses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".