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Abstract B128: Change in use of folic acid-containing supplements after a diagnosis of colorectal cancer

2008· article· en· W2049241678 on OpenAlexaboutno aff
Rebecca Holmes, Lin Li, Yingye Zheng, John D. Potter, John A. Baron, Loı̈c Le Marchand, Mariana C. Stern, Gail McKeown‐Eyssen, Polly A. Newcomb, Robert W. Haile, Paul J. Limburg, Cornelia M. Ulrich

Bibliographic record

VenueCancer Prevention Research · 2008
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerMedicineThymidylate synthaseCancerInternal medicineMultivitaminBody mass indexCancer preventionLogistic regressionVitaminOncologyFluorouracil

Abstract

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Abstract B128 Background The B vitamin folate affects colorectal carcinogenesis through effects on nucleotide synthesis and possibly DNA methylation. Animal models and epidemiologic evidence suggest that folate from diet and supplements can prevent the development of colorectal cancer (CRC), but that high folate intake after adenomas or cancer are established may accelerate progression or recurrence. The chemotherapeutic agent 5-fluorouracil inhibits thymidylate synthase, a folate-metabolizing enzyme, and the effect of folate intake on treatment efficacy is unknown. Supplement use and folate intake are high among adults in the U.S., particularly with food supply fortification, and higher still among cancer patients and survivors. However, few studies have addressed use of folic acid-containing supplements (FAS) among colorectal cancer patients, how use changes after diagnosis, and what factors determine changes in FAS use. Methods The Colon Cancer Family Registry (CCFR) is a multicenter study of colorectal cancer cases, their family members and controls, recruited since 1998 at six sites in the United States, Australia and Canada. The current analysis includes 1,092 CRC cases with epidemiologic data available from questionnaires administered at enrollment, asking about supplement use and other risk factors before diagnosis, and from follow-up questionnaires about 5 years later. Baseline characteristics for cases who began using FAS (including multivitamins) after diagnosis were compared to those for cases who used FAS neither before nor after diagnosis. We used logistic regression models to evaluate associations between age, sex, CCFR site, race, education, income, lifetime exercise, smoking, body mass index, and diet and change in FAS use, adjusting each model for age, sex and site, when appropriate. Results FAS use before CRC diagnosis was 35.4%, while 55.1% of cases used FAS after diagnosis. Women were more likely to begin FAS use after diagnosis (OR 1.60, 95% CI 1.17-2.19). Current smokers were less likely than nonsmokers to begin FAS use (OR 0.63, 95% CI 0.40-0.97), as were those consuming more red meat (OR 0.38, 95% CI 0.21-0.70 for those in the highest versus lowest intake categories, ptrend=0.012). Subjects with higher fruit intake were more likely to begin FAS use (OR 1.95, 95% CI 1.16-3.30 for highest versus lowest intake, ptrend=0.013). We also observed a suggestive association between lifetime physical activity and change in FAS use for active versus less active subjects (OR 1.48, 95% CI 1.01-2.16), though there was no trend with increasing levels of physical activity (ptrend=0.27). Finally, we found that residents of non-U.S. countries were less likely to begin FAS use (OR 0.57, 95% CI 0.33-0.96 for Ontario, Canada and OR 0.21, 95% CI 0.11-0.38 for Australia). Conclusions: Our analysis showed substantial increases in the use of folic acid-containing supplements after diagnosis with colorectal cancer in CCFR participants, especially among women, U.S. residents, nonsmokers, and those who consumed more fruit and less meat. This study begins to characterize CRC patients likely to be using FAS, and suggests that its use is widespread. This finding is notable given evidence that folate may accelerate progression of colorectal cancer, and the unknown effect that FAS intake may have on the efficacy of cancer treatment and on survival. Citation Information: Cancer Prev Res 2008;1(7 Suppl):B128.

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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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.275
GPT teacher head0.485
Teacher spread0.210 · 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.

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

Citations0
Published2008
Admission routes1
Has abstractyes

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