Genetic Determinants of 1,3-Butadiene Metabolism and Detoxification in Three Populations of Smokers with Different Risks of Lung Cancer
Bibliographic record
Abstract
Abstract Background: 1,3-Butadiene (BD) is an important carcinogen in tobacco smoke that undergoes metabolic activation to DNA-reactive epoxides. These species can be detoxified via glutathione conjugation and excreted in urine as the corresponding N-acetylcysteine conjugates. We hypothesize that single nucleotide polymorphisms (SNPs) in BD-metabolizing genes may change the balance of BD bioactivation and detoxification in White, Japanese American, and African American smokers, potentially contributing to ethnic differences in lung cancer risk. Methods: We measured the levels of BD metabolites, 1- and 2-(N-acetyl-L-cysteine-S-yl)-1-hydroxybut-3-ene (MHBMA) and N-acetyl-S-(3,4-dihydroxybutyl)-L-cysteine (DHBMA), in urine samples from a total of 1,072 White, Japanese American, and African American smokers and adjusted these values for body mass index, age, batch, and total nicotine equivalents. We also conducted a genome-wide association study to identify genetic determinants of BD metabolism. Results: We found that mean urinary MHBMA concentrations differed significantly by ethnicity (P = 4.0 × 10−25). African Americans excreted the highest levels of MHBMA followed by Whites and Japanese Americans. MHBMA levels were affected by GSTT1 gene copy number (P < 0.0001); conditional on GSTT1, no other polymorphisms showed a significant association. Urinary DHBMA levels also differed between ethnic groups (P = 3.3 × 10−4), but were not affected by GSTT1 copy number (P = 0.226). Conclusions: GSTT1 gene deletion has a strong effect on urinary MHBMA levels, and therefore BD metabolism, in smokers. Impact: Our results show that the order of MHBMA levels among ethnic groups is consistent with their respective lung cancer risk and can be partially explained by GSTT1 genotype. Cancer Epidemiol Biomarkers Prev; 26(7); 1034–42. ©2017 AACR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".