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Record W4205975023 · doi:10.33590/emjoncol/20-00067

Phosphate and Oxysterols May Mediate an Inverse Relationship Between Atherosclerosis and Cancer

2020· article· en· W4205975023 on OpenAlexaff
Ronald B. Brown

Bibliographic record

VenueEMJ Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOxysterolCholesterolPhosphateCancerMediterranean dietDiseaseEndocrinologyInternal medicineMedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.128
GPT teacher head0.362
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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