Abstract P176: Associations of Gestational Lipids and Apolipoproteins With Pregnancy Outcomes: The Hyperglycemia and Adverse Pregnancy Outcome Study
Bibliographic record
Abstract
Introduction: Gestational hyperlipidemia has traditionally been considered physiologic and benign, but the significance of inter-individual variation in lipid levels for maternal-fetal health are poorly understood. We examined associations of gestational lipids and apolipoproteins with adverse obstetric and neonatal outcomes. Methods: Data from the Hyperglycemia and Adverse Pregnancy Outcome Study were analyzed, including 1,813 mother-child dyads from 9 field centers in 6 countries: US (25%), Barbados (24%), UK (20%), China (16%), Thailand (8%), and Canada (7%). Fasting lipids and apolipoproteins were directly measured at a mean of 28 (range 23-34) weeks’ gestation. Cord blood was collected at delivery, neonatal anthropometrics were measured within 72 hours, and medical records were abstracted for obstetric outcomes. Logistic regression was utilized to test associations of lipids and apolipoproteins (per +1 SD; log-transformed if skewed) with pregnancy outcomes, adjusted for center, demographics, and maternal covariates such as BMI, blood pressure, and glycemia. Results: See Table for lipid and apolipoprotein levels in pregnant mothers. In fully adjusted models ( Table ), 1 SD higher log-triglycerides (i.e., ~2.7-fold higher triglyceride level) in late pregnancy was significantly associated with higher odds for preeclampsia (OR 1.53 [95% CI, 1.15-2.05]), large for gestational age infant (1.42 [1.21-1.67]), and infant insulin sensitivity <10 th percentile (1.25 [1.03-1.50]), but not with unplanned primary cesarean section or infant sum of skinfolds >90 th percentile. There were no significant associations of maternal HDL-C, LDL-C, or log-ApoB/A1 ratio with any outcome. Conclusion: Triglyceride levels in the latter half of pregnancy were uniquely associated with both maternal risks (preeclampsia) and neonatal risks (large for gestational age and insulin resistance), even after adjustment for maternal BMI, blood pressure, and glycemia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".