The Effect of Ketogenic Diet on Shared Risk Factors of Cardiovascular Disease and Cancer
Bibliographic record
Abstract
Cardiovascular disease (CVD) and cancer are the first and second leading causes of death worldwide, respectively. Epidemiological evidence has demonstrated that the incidence of cancer is elevated in patients with CVD and vice versa. However, these conditions are usually regarded as separate events despite the presence of shared risk factors between both conditions, such as metabolic abnormalities and lifestyle. Cohort studies suggested that controlling for CVD risk factors may have an impact on cancer incidence. Therefore, it could be concluded that interventions that improve CVD and cancer shared risk factors may potentially be effective in preventing and treating both diseases. The ketogenic diet (KD), a low-carbohydrate and high-fat diet, has been widely prescribed in weight loss programs for metabolic abnormalities. Furthermore, recent research has investigated the effects of KD on the treatment of numerous diseases, including CVD and cancer, due to its role in promoting ketolysis, ketogenesis, and modifying many other metabolic pathways with potential favorable health effects. However, there is still great debate regarding prescribing KD in patients either with CVD or cancer. Considering the number of studies on this topic, there is a clear need to summarize potential mechanisms through which KD can improve cardiovascular health and control cell proliferation. In this review, we explained the history of KD, its types, and physiological effects and discussed how it could play a role in CVD and cancer treatment and prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".