Xenobiotic-Metabolizing Genes and Small-for-Gestational-Age Births
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Bibliographic record
Abstract
BACKGROUND: Little is known about the role of xenobiotic-metabolizing gene variants as risk factors for small-for-gestational-age (SGA) births or as modifiers for the effects of exposures such as maternal smoking. METHODS: We conducted 2 joint studies: a case-control design including 493 cases (birth weight below the 10th percentile according to gestational age and sex) and 472 controls (at or above the 10th percentile) and a family-based study (mother, father, and newborn) with approximately 250 case trios and a similar number of control trios. Logistic regression and a log-linear model were used to analyze the association between genetic variants such as CYP1A1*2A, CYP1A1*2B, CYP1A1*4, GSTT1, GSTM1, and XRCC3 and SGA. The interaction between genetic variants and maternal smoking was also studied. RESULTS: The odds ratio (OR) for the association of complete maternal GSTT1 deletion with SGA was 0.63 (95% confidence interval = 0.41-0.97), and that for the complete newborn GSTM1 deletion was 0.74 (0.55-0.98). Newborns with the partial GSTT1 deletion had an OR of 1.40 (1.01-1.95), and newborns homozygous for CYP1A1*2A had an OR of 4.28 (1.02-18.0). These results were coherent with the trio-based results. Significant interactions were observed between maternal smoking in the third trimester and CYP1A1*2A (P = 0.03), XRCC3 (P = 0.03), and newborn GSTT1 (P = 0.01). CONCLUSIONS: Certain genetic variants involved in the metabolism of xenobiotics increase the risk of SGA, as well as modify the effects of maternal smoking by increasing or decreasing its risk.
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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 it