Xenobiotic-Metabolizing Genes and Small-for-Gestational-Age Births
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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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".