Association of Dietary Pattern during Pregnancy and Gestational Diabetes Mellitus: A Prospective Cohort Study in Northern China.
Bibliographic record
Abstract
OBJECTIVE: To examine the association of maternal dietary patterns during pregnancy with gestational diabetes mellitus (GDM) in northern China. METHODS: The dietary intakes of pregnant women were recorded twice by 24-hour dietary recalls for three days prior to having been diagnosed with GDM, at 5-15 and 24-28 gestational weeks, respectively. GDM was diagnosed, and serum glycosylated hemoglobin (HbA1c) was measured at 24-28 weeks. Dietary patterns were assessed by factor analysis. The association of the dietary pattern with GDM and HbA1c was examined by multiple logistic models. RESULTS: Of 753 participants, 64 (8.5%) were diagnosed with GDM. Four dietary patterns were identified: Western pattern (dairy, baked/fried food and white meat), traditional pattern (light-colored vegetables, fine grain, red meat and tubers), mixed pattern (edible fungi, shrimp/shellfish and red meat) and prudent pattern (dark-colored vegetables and deep-sea fish). Compared with the prudent pattern, both the Western pattern and the traditional pattern were associated with an increased risk of GDM (aOR = 4.40, 95% CI: 1.58-12.22; aOR = 4.88, 95% CI: 1.79-13.32) and a high level of HbA1c (aOR = 12.37, 95% CI: 1.47-103.91; aOR = 26.23, 95% CI: 2.54-270.74). Compared to the lowest quartile (Q), Q3 of the Western pattern scores and Q3-Q4 of the traditional pattern scores were associated with a higher risk of GDM. CONCLUSION: The consumption of the Western pattern or the traditional pattern during pregnancy may increase the risk of GDM.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".