Association of genetic polymorphisms in UGT1A1 with breast cancer and plasma hormone levels.
Bibliographic record
Abstract
UDP-glucuronosyltransferases (UGTs) catalyze the detoxification and the elimination of a large number of endogenous and exogenous compounds in the liver and extrahepatic tissues. One of the UGT1A family members, UGT1A1, is involved in estradiol metabolism and, therefore, represents a candidate gene in breast carcinogenesis. A common insertion/deletion polymorphism in the TATA-box of the promoter region of UGT1A1 results in decreased initiation of transcription. In a previous study, we found a positive association between the UGT1A1 low-transcriptional alleles and premenopausal breast cancer risk in an African-American population. In the present study, we sought to determine whether the low-transcription UGT1A1 promoter allele, UGT1A1*28 [A(TA)(7)TAA], was associated with increased breast cancer risk among primarily Caucasian women in a nested case-control study within the Nurses' Health Study cohort. No significant association between the UGT1A1*28 [A(TA)(7)TAA] allele and breast cancer was observed. Compared with women homozygous for the UGT1A1*1 [A(TA)(6)TAA] allele, the relative risk was 0.80 (confidence interval, 0.49-1.29) for women homozygous for the UGT1A1*28 allele. The effect of the UGT1A1 genotype on plasma hormone levels in postmenopausal women not using hormone replacement was also evaluated, and overall, no significant differences in hormone levels by genotypes were observed. When restricted to women who had at least one UGT1A1*28 allele and a body mass index at blood draw of >27 kg/m(2), particularly in combination with the cytochrome p450c17alpha genotype, estrone and estradiol levels tended to vary by UGT1A1 genotypes. The results presented do not support a strong association between the UGT1A1 promoter polymorphism and the risk of breast cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".