High maternal iron status, dietary iron intake and iron supplement use in pregnancy and risk of gestational diabetes mellitus: a prospective study and systematic review
Bibliographic record
Abstract
AIM: High iron measured using dietary intake and biomarkers is associated with Type 2 diabetes. It is uncertain whether a similar association exists for gestational diabetes mellitus. The aim of this systematic review was to conduct a cohort study examining first trimester body iron stores and subsequent risk of gestational diabetes, and to include these findings in a systematic review of all studies examining the association between maternal iron status, iron intake (dietary and supplemental) and the risk of gestational diabetes. METHODS: Serum samples from women with first trimester screening were linked to birth and hospital records for data on maternal characteristics and gestational diabetes diagnosis. Blood was analysed for ferritin, soluble transferrin receptor and C-reactive protein. Associations between iron biomarkers and gestational diabetes were assessed using multivariate logistic regression. A systematic review and meta-analysis, registered with PROSPERO (CRD42014013663) included studies of all designs published in English from January 1995 to July 2015 that examined the association between iron and gestational diabetes and included an appropriate comparison group. RESULTS: Of 3776 women, 3.4% subsequently developed gestational diabetes. Adjusted analyses found increased odds of gestational diabetes for ferritin (OR 1.41; 95% CI 1.11, 1.78), but not for soluble transferrin receptor (OR 1.00; 95% CI 0.97, 1.03) per unit increase of the biomarker. Two trials of iron supplementation found no association with gestational diabetes. Increased risk of gestational diabetes was associated with higher levels of ferritin and serum iron and dietary haem iron intakes. CONCLUSIONS: Increased risk of gestational diabetes among women with high serum ferritin and iron levels and dietary haem iron intakes warrants further investigation.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".