Association between Early Pregnancy Maternal Folate and Glycemic Indices at Oral Glucose Tolerance Test: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Folate plays a crucial role in fetal development, but its relationship with maternal glucose metabolism remains inconclusive. Recent meta-analyses have suggested a correlation between high folate and risk of gestational diabetes mellitus in pregnancy; however, its association with different glycemic parameters has not yet been explored. Objectives: This study aims to comprehensively synthesize evidence and test the association between early pregnancy circulating folate (<16 wk of gestation) and glycemic indices measured during oral glucose tolerance testing (OGTT) at 24-28 wk. Methods: We conducted a systematic search of databases up to 25 June, 2025, examining the relationship between early pregnancy folate and maternal glycemic indices. Study quality was assessed by Newcastle-Ottawa Scale. Standardized effect sizes (std. β coefficients) for serum/plasma folate were pooled using a random-effects model. Subgroup and sensitivity analyses were performed to account for between-study heterogeneity. Results: >70%). Conclusions: Our analysis suggests a possible association between higher early pregnancy folate levels and higher glucose levels at the time of OGTT. However, these findings should be interpreted cautiously, given the methodological limitations and the limited number of studies included in this review.This trial was registered at PROSPERO as CRD42021255022.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".