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Record W2323761746 · doi:10.1158/1538-7445.am10-913

Abstract 913: 5,10-Methylenetetrahydrofolate reductase C677T allele frequency and childhood leukemia incidence in predominantly European ancestry countries

2010· article· en· W2323761746 on OpenAlexaboutno aff
Kimberly Johnson, Logan G. Spector

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsnot available
Fundersnot available
KeywordsMethylenetetrahydrofolate reductaseLeukemiaIncidence (geometry)Poisson regressionMedicineAlleleAllele frequencyRate ratioChildhood leukemiaDemographyGeneticsMinor allele frequencyInternal medicineOncologyConfidence intervalBiologyPopulationLymphoblastic LeukemiaEnvironmental health

Abstract

fetched live from OpenAlex

Abstract 5,10-Methylentetrahydrofolate reductase (MTHFR) single nucleotide polymorphisms (SNPs) have been intensely studied as cancer susceptibility alleles. For childhood leukemia, data have indicated that the most commonly studied MTHFR SNP, C677T, is associated with a slightly reduced risk of acute lymphoblastic leukemia (ALL). Both childhood leukemia incidence and MTHFR allele frequencies show variation worldwide. Our objective was to conduct an ecologic study to determine whether the MTHFR C677T allele frequency and childhood leukemia incidence are associated. We used cancer incidence data collected by the International Agency for Cancer Research from 22 registries that captured 19,025 leukemia cases diagnosed between the ages of 0 and 14 years from the United States, Canada, Australia, and 18 European countries; data were restricted to registries with majority European ancestry populations in order to minimize confounding by ancestry. MTHFR allele frequencies were gathered from the literature to match as closely as possible the registry populations. We modeled the association between childhood leukemia incidence and MTHFR C677T allele frequency using Poisson regression to calculate rate ratios (RRs) and 95% confidence intervals (CIs). Estimates of the minor C677T allele frequency ranged from 24% in Finland to 44% in Italy. There was a weak inverse correlation between leukemia incidence and MTHFR C677T allele frequency (r=0.19), that was stronger for ALL (r=0.30). Acute nonlymphoblastic leukemia (ANLL) incidence was positively correlated with MTHFR C677T allele frequency (r=0.36). Poisson regression results from linear models showed that the incidence of childhood leukemia decreased by 5% (95% CI 3%-6%) for every 5% increase in the C677T allele frequency. A similar pattern was observed for ALL with a RR of 0.93 (95% CI=0.92-0.95) but not acute non-lymphoblastic leukemia (ANLL) (RR=1.04, 95% CI=1.00-1.07). The significant inverse associations between the C677T allele frequency and leukemia and ALL incidence were specific to incidence rates for children diagnosed <10 years. These results are consistent with the results of case-control studies of the association between childhood leukemia and MTHFR allele frequency suggesting a modest risk reduction in association with the C677T allele. The strengths and limitations of ecological vs. case control study designs will be discussed in the context of these results to provide insight into the causal relationship between the MTHFR minor allele frequency and childhood leukemia risk. This research was supported by NIH grant T32 CA099936. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 913.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.389
Teacher spread0.344 · 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 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
Published2010
Admission routes1
Has abstractyes

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