The association of dietary total antioxidant capacity and gestational diabetes: a prospective cohort study from the Mothers and their children’s health (MATCH)
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: There is evidence to support the hypothesis that a diet rich in antioxidants can help safeguard against the development of gestational diabetes mellitus (GDM). This study aimed to investigate the association between dietary total antioxidant capacity (DTAC) during early pregnancy and the risk of GDM. SUBJECTS/METHODS: We included 1856 pregnant women in their first trimester from the Mothers and their Children's Health (MATCH) prospective cohort study. Prepregnancy dietary intake was assessed using a validated food frequency questionnaire (FFQ) and was used to calculate the DTAC score. Incident GDM was diagnosed based on the American Diabetes Association criteria. We estimated the association between DTAC and GDM using propensity score-based inverse probability weighting (IPW). RESULTS: Overall, 369 (14.6%) of the pregnant women were identified with GDM. The mean DTAC score and the corresponding standard deviation (SD) was 2.82± (2.56) mmol/100 g, with a range of 0.01 to 18.55. The adjusted risk of GDM decreased by 34% (95% CI = 10%, 52%, p = 0.023) for each DTAC score increase. The results showed that women in the highest quartile of DTAC had a lower risk of developing GDM compared to those in the lowest quartile (adjusted RR: 0.29, 95% CI: 0.12, 0.68, p = 0.005). CONCLUSION: DTAC in early pregnancy is significantly associated with a lower risk of GDM. Additional larger cohort studies are needed to validate these findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".