Genetically determined body mass index and maternal outcomes of pregnancy: a two-sample Mendelian randomization study
Bibliographic record
Abstract
Objective: Observational studies have described associations between obesity and adverse outcomes of pregnancy. Mendelian randomization (MR) takes advantage of the ‘natural’ genetic randomization to risk of an exposure such as body mass index (BMI) to study the effects of the exposure on outcomes. Similar to randomization in a clinical trial, this limits the potential for confounding and bias. Design: A two-sample MR study. Setting: Summary statistics from published genome wide association studies (GWAS) in European ancestry populations. Population or Sample: Instrumental variants for body mass index (BMI) were obtained from a study on 434,794 females. Female-specific genetic association estimates for outcomes were extracted from the sixth round of analysis of the FINNGEN cohort data. Methods: Inverse-variance weighted MR was used to assess the association between BMI and all outcomes. Sensitivity analyses with weighted median and MR-Egger were also performed. Results: A 1-SD increase in BMI was associated with higher risk of pre-eclampsia (OR 1.68, 95%CI 1.46-1.94, p=8.74x10-13), gestational diabetes (OR 1.67, 95%CI 1.46-1.92, p=5.35x10-14), polyhydramnios (OR 1.40, 95%CI 1.00-1.96, p=0.049). There was evidence suggestive of a potential association with higher risk of premature rupture of membranes (OR 1.16, 95%CI 1.00-1.36, p=0.050) and postpartum depression (OR 1.12, 95%CI 0.99-1.27, p=0.062). Conclusions: Higher maternal BMI is associated with marked increase in risk of pre-eclampsia, gestational diabetes and polyhydramnios. The relationship between BMI and premature rupture of membranes and postpartum depression should be assessed in further studies. Our study supports efforts to target BMI as a cardinal risk factor for maternal morbidity.
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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 | 0.044 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".