Combinations of<i>CYP2A6*4</i>and Glutathione S-Transferases Gene Polymorphisms Modify the Association Between Maternal Secondhand Smoke Exposure During Pregnancy and Small-for-Gestational-Age
Bibliographic record
Abstract
INTRODUCTION: Risk of small-for-gestational-age (SGA) birth varied considerably in women exposed to secondhand smoke (SHS) during pregnancy. We examined whether this variation was explained by mothers' one Phase I (CYP2A6*4, activation of tobacco toxics) and two Phase II (GSTM1 and GSTT1, detoxification) metabolic genotypes. METHODS: We enrolled 468 Chinese pregnant women (115 delivering SGA and 353 delivering non-SGA newborns) shortly before delivery. SHS exposure during pregnancy was defined as self-reported daily exposure time being more than 0 minute. We fitted multivariable logistic regression models to examine whether CYP2A6*4, GSTM1, and GSTT1 gene polymorphsims and their combinations modified the association between SHS exposure and SGA. RESULTS: In the total sample, more mothers of SGA newborns were exposed to SHS during pregnancy than mothers of non-SGA newborns (38.3% vs. 31.4%). CYP2A6*4, GSTM1, and GSTT1 genes alone could not modify the association between SHS exposure and SGA. The combination of CYP2A6*4 and GSTT1 high-risk genotypes (CYP2A6*1/*1 and GSTT1-absent [high-risk] vs. other combinations as a whole [low-risk]) significantly (P value, .045) modified the association between SHS exposure and SGA. Among mothers with high-risk genotypes, SHS during pregnancy was significantly associated with SGA (confounder-adjusted odds ratio, 2.31 [95% confidence interval, 1.20-4.42]). Among mothers with low-risk genotypes, however, SHS exposure during pregnancy was not associated with SGA (1.14 [0.64-2.04]). CONCLUSIONS: Chinese pregnant women with the combination of CYP2A6*1/*1 and GSTT1-absent genotypes are at particularly high-risk of SHS-related SGA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".