Biomarkers predicting adverse pregnancy outcomes in women living with obesity: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: : Systematic literature searches used predefined search terms in PubMed, Ovid Embase, Ovid MEDLINE, Scopus, and the Cochrane Central Register of Controlled Trials. Databases were searched from inception to August 2024. Study eligibility criteria: Interventional and observational studies comparing pregnancy outcomes amongst women with a pre- or early-pregnancy (<20 weeks' gestation) BMI ≥30 kg/m² according to presence or amount of any antenatally measured biomarker were included. Study appraisal and synthesis methods: Two reviewers independently assessed studies for inclusion against predefined inclusion and exclusion criteria. Risk of bias assessment was performed on included studies using the Newcastle-Ottawa Risk of Bias Tool. A narrative synthesis of eligible studies was constructed and data were meta-analysed, where possible, using random effects models. Certainty of evidence was assessed by GRADE rating. Results: 0%). Certainty of evidence regarding all associations was low or very low. Conclusions: Decreased adiponectin and increased insulin are associated with increased risk of adverse pregnancy outcomes in women with BMI ≥30 kg/m². However, the low number of studies available for inclusion and low certainty of evidence mean that biomarker-based risk-stratification within pregnant women with BMI ≥30 kg/m² is not currently feasible. Further research is required to find ways of reliably targeting investigations during maternity care towards the subset of women living with obesity who are at highest risk of adverse outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".