Genetic Association Between Single Nucleotide Polymorphisms in the Paraoxonase 1 (PON1) Gene and Small-for-Gestational-Age Birth in Related and Unrelated Subjects
Bibliographic record
Abstract
Paraoxonase 1 (PON1) protects against oxidative modification of low density lipoproteins. The PON1 gene has 3 polymorphisms considered strong determinants of PON1 levels: Q192R and L55M in the coding region and C-108T in the promoter region. PON1 levels are also influenced by smoking. The authors hypothesized that PON1 variants could increase the risk of vascular thrombosis, leading in turn to placental insufficiency and small-for-gestational-age (SGA) birth. The author compared PON1 variants and haplotypes between 448 newborn SGA cases and 431 newborn controls from Montréal, Québec, Canada (1998-2000) and studied the effects of interaction with maternal smoking. Transmission of the variants in case-parent trios was used as validation of the case-control results; the authors combined case-control and family data to analyze the associations of variants with SGA birth. In the case-control analysis, the T allele from C-108T increased the risk of SGA birth (additive odds ratio = 1.30, 95% confidence interval (CI): 1.06, 1.59), and the TRL haplotype (T from C-108T, R from Q192R, and L from L55M) was associated with an odds ratio of 1.51 (95% CI: 1.07, 2.15). Among smokers, the CRL haplotype was protective (odds ratio = 0.48, 95% CI: 0.28, 0.82). Case-parent trio results were compatible with case-control results.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".