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Abstract LB-460: Effects of folic acid supplementation on mammary tumor progression

2011· article· en· W2330510000 on OpenAlexaff
Shaidah Deghan Manshadi, Lisa Ishiguro, Ruth Croxford, Kyoung‐Jin Sohn, Alan Medline, Richard Renlund, Young‐In Kim

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineBreast cancerCancerDMBAFolic acidFolic acid supplementationInternal medicinePhysiologyVitaminMammary tumorEndocrinologyCarcinogenesis

Abstract

fetched live from OpenAlex

Abstract Background: The role of folate in breast cancer is highly controversial. Although some epidemiologic studies have suggested a protective effect of high folate status on breast cancer risk, recent studies have suggested that high folate intake, largely from folic acid (the synthetic form of folate), and high plasma folate levels may increase breast cancer risk. In animal studies, folic acid supplementation was shown to promote the progression of established preneoplastic lesions of colon cancer. Folic acid intake in North America has drastically increased over the past decade due to folic acid fortification and widespread supplemental use. Almost 70% of breast cancer patients consume folic acid containing vitamin supplements after diagnosis. The benefits of folic acid supplementation in patients with breast cancer are unknown and there is a concern that folic acid supplementation may in fact adversely affect breast cancer progression. We therefore investigated the effects of folic acid supplementation on the progression of established mammary tumors in the DMBA rat model. Methods: Female Sprague Dawley rats were placed on a control diet containing 2 mg folic acid/kg diet and mammary tumors were initiated with DMBA at 7 weeks of age. When the sentinel tumor reached a diameter between 7–9 mm, rats were randomized to receive a diet containing 2 (control), 5, 8, or 10 mg of folic acid/kg diet for up to 12 weeks. Body weight and mammary tumor growth were measured weekly and plasma folate levels at necropsy were determined. At necropsy, the sentinel and all other mammary tumors were excised and histologically analyzed. Results: The final weight of the animals at necropsy was not significantly different among the 4 groups. Animals on the control and 10 mg folic acid/kg diet had significantly faster weight gain than those on the 8 mg folic acid/kg diet (p<0.02) while those on the 5 mg folic acid diet did not differ significantly from either group. Plasma folate levels significantly reflected the supplemental levels of folic acid in a dose-responsive manner (p<0.05). Sentinel tumor growth (mm2/week) was not significantly different among the 4 groups at 4, 7, and 12 post randomization. Sentinel tumor weight at necropsy was lower in the control group compared with the 5 and 8 (p<0.05) and with the 10 (p=0.051) mg folic acid/kg diet groups. Total weight of all mammary tumors was highest in 5 and 8 mg supplemented groups compared to the control group (p< 0.05, p=0.086). Conclusion: Our data suggest that folic acid supplementation may promote the progression of established mammary tumors, although no clear dose-responsive relationship was observed. Given the drastically increased folic acid intake and the mortality and morbidity of breast cancer in North America, the potential adverse effect of folic acid supplementation on breast cancer progression needs to be clarified. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-460. doi:10.1158/1538-7445.AM2011-LB-460

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.111
GPT teacher head0.465
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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