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Record W4210585563 · doi:10.3390/obesities2010007

Obesity and Cancer: Potential Mediation by Dysregulated Dietary Phosphate

2022· article· en· W4210585563 on OpenAlexaff
Ronald B. Brown

Bibliographic record

VenueObesities · 2022
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsObesityEndocrinologyCancerPhosphateOverweightInternal medicineMedicinePopulationPhysiologyBiologyEnvironmental healthBiochemistry

Abstract

fetched live from OpenAlex

Next to smoking, obesity is the second leading preventable risk factor for cancer, but increasing rates of obesity and overweight are estimated to overtake smoking as the leading preventable cancer risk factor. Few research studies have investigated the dysregulated endocrine metabolism of dietary phosphate as a potential mediating factor in the association of obesity with cancer. Phosphate toxicity, the accumulation of excess phosphate in the body from dysregulated phosphate metabolism, is associated with tumorigenesis. High levels of hormones that regulate phosphate metabolism, such as parathyroid hormone and fibroblast growth factor 23, are also associated with obesity, providing a potential link between obesity and phosphate toxicity. Increased dietary intake of inorganic phosphate is linked to excessive consumption of foods processed with phosphate additives, and consumption of ultra-processed foods is associated with an increase in the incidence of obesity. Sugar-sweetened beverages provide the single largest source of sugar and energy intake in the U.S. population, and colas containing phosphoric acid are associated with tumorigenesis, suggesting another potential connection between obesity and cancer. Furthermore, dietary phosphate is positively correlated with increases in obesity, central obesity, and metabolic syndrome. The present perspective article proposes that dysregulated dietary phosphate potentially mediates the association of obesity with cancer.

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 score0.265
Threshold uncertainty score0.371

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 head0.007
GPT teacher head0.237
Teacher spread0.231 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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