Dietary fat and carbohydrate intake during early pregnancy and risk of gestational diabetes
Bibliographic record
Abstract
Dietary intake is known to influence gestational diabetes (GDM), but there is a little consensus on the optimal distribution of dietary fat and carbohydrate intake during pregnancy for GDM prevention. We aimed to investigate the impact of macronutrient intake distribution during the second trimester on the risk of GDM. Women who were with singleton pregnancies and without pre‐existing diabetes were included. Participants were asked to recall second trimester dietary intake using a validated food frequency questionnaire and in addition underwent a 3‐hour oral glucose tolerance test. Of 205 participants, 46 (22.4%) had GDM assessed at 30±2.6 (mean±SD) weeks gestation. Women who had GDM compared to those free of GDM consumed higher % intake of total fat (mean±SD: 37±5.2 v. 34±5.3 %, respectively) and lower % intake of carbohydrate (49±6.2 v. 52±6.2 %) (both p=0.01). After adjustment for age, ethnicity, family history of diabetes, prepregnancy BMI, and pregnancy weight gain, % total fat and % carbohydrate intake were individually associated with GDM (odds ratio per 5% increase 1.54 [95% CI 1.04–2.29] and 0.68 [0.49–0.93], respectively). In conclusion, dietary intakes higher in total fat and lower in carbohydrate, potential modifiable behavioral determinants, during early pregnancy were associated with increased risk for GDM later in pregnancy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".