Toll-like Receptor Gene Polymorphisms and Preeclampsia Risk: A Case-Control Study and Data Synthesis
Bibliographic record
Abstract
BACKGROUND: Preeclampsia is a syndrome of exaggerated innate inflammatory response. It is plausible that this innate inflammation may be mediated by Toll-like receptors (TLRs). A portion of the familial susceptibility to the development of preeclampsia may be mediated by single nucleotide polymorphisms (SNPs) in the TLR gene sequences. Published reports vary in their finding an association between TLR SNPs and preeclampsia risk. METHODS: Common SNPs of the TLR2 (Arg753Gln) and co-segregating TLR4 (Asp299Gly and Thr399Ile) genes were screened in 94 women with pre-eclampsia and 176 healthy pregnancy controls. We performed a data synthesis of our findings with those in published reports to ascertain whether or not we could explain the apparent disparity in the literature. RESULTS: The presence of TLR2 (RR 2.57 [95% CI 1.31, 5.05]) and TLR4 (RR 2.06 [1.16, 3.67]) SNPs aggregated with early-onset (<34 + 0 weeks), but not late-onset (>or=34 + 0 weeks), preeclampsia. Through synthesis of these and the published data, TLR polymorphisms appear to lower thresholds for early-onset and severe preeclampsia, but not late-onset or mild disease. CONCLUSIONS: TLR2 and TLR4 SNPs appear to alter susceptibility to developing the maternal syndrome of preeclampsia. Data synthesis of these data and other studies strengthens the association for early-onset and severe disease, in particular. A definitive and fully powered cohort study is required.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".