Phosphate and Oxysterols May Mediate an Inverse Relationship Between Atherosclerosis and Cancer
Bibliographic record
Abstract
The peer-reviewed literature has reported an inverse relationship between atherosclerosis and cancer for almost 100 years, but no causative mechanism has been established to explain this puzzling relationship. More recent research has reported an association between tumourigenesis and phosphate toxicity from dysregulated phosphate metabolism, and an association has also been reported between atherosclerosis and cholesterol oxidation products or oxysterols. The present review article synthesises these research findings and proposes that an inverse relationship between the associated risk of cancer and atherosclerosis may be mediated by tumourigenic and atherogenic dietary patterns containing inverse proportions of dietary phosphate and oxysterols. Low-fat animal-based foods generally have reduced cholesterol and oxysterol levels and relatively higher protein and phosphate levels, and dietary patterns containing these foods are associated with reduced atherosclerosis risk and increased cancer risk. By comparison, full-fat animal-based foods are higher in cholesterol and oxysterols and relatively lower in protein and phosphate, and dietary patterns containing these foods are associated with increased atherosclerosis risk and reduced cancer risk. Fruits, vegetables, and plant-based fats generally have lower phosphate levels and no cholesterol, and dietary patterns associated with increased amounts of these foods, such as the Mediterranean diet, reduce risk for both cancer and cardiovascular disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".