Exploring Fetal Sex Dimorphism in the Risk Factors of Gestational Diabetes Mellitus—A Prospective Cohort Study
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is a common pregnancy complication. Its etiology remains incompletely understood. Studies in recent years suggest that fetal sex may affect maternal metabolic milieu during pregnancy. We sought to assess whether there is fetal sex dimorphism in the risk factors of GDM. In a prospective pregnancy cohort in Shanghai, China, we studied 2435 singleton pregnant women without pre-existing diabetes. GDM was diagnosed according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG)’ criteria. Log-binomial models were applied to obtain the adjusted relative risk (aRR). A total of 380 (15.6%) women developed GDM. Family history of diabetes was associated with an increased risk of GDM in women bearing a female fetus [aRR 1.74 (1.27-2.40), p<0.001], but not in women bearing a male fetus (p=0.68) (test for interaction, p=0.03). Alcohol drinking was associated with an increased risk of GDM in women bearing a male fetus only (p=0.023), although the test for interaction did not reach statistical significance (p=0.055). In conclusion, family history of diabetes was associated with an increased risk of GDM in women bearing a female fetus only in this Chinese pregnancy cohort. There may be a need to consider fetal sex dimorphism in evaluating the risk factors 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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".