Impact of dietary inflammatory index on gestational diabetes mellitus in normal and overweight women: a systematic review and meta-analysis of observational studies.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To systematically investigate the association between the dietary inflammatory index (DII) and gestational diabetes mellitus (GDM), with a focus on the role of BMI in this relationship. METHODS AND STUDY DESIGN: A comprehensive search was conducted in PubMed, Embase, Web of Science, The Cochrane Library, Medline, CINAHL Complete, Chinese Periodical Full-text Database, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and China Wanfang Database for rele-vant observational studies published up to August 2023. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. The pooled effect size was calculated using a random-effects model. Sub-group and meta-regression analyses were performed to explore potential sources of heterogeneity. RESULTS: The study included 54,058 participants from 10 studies. Pregnant women with a higher DII, indicating a pro-inflammatory diet, had a significantly increased risk of GDM compared to those with a lower DII, indicating an anti-inflammatory diet (pooled OR: 1.17, 95% CI: 1.01-1.36; I²=70%, p <0.001). Subgroup analyses revealed a stronger association in normal weight stratification (OR: 1.25, 95%CI: 1.04-1.51), case-control studies (OR: 1.45, 95%CI: 1.03-2.05), Asia (OR: 1.26, 95%CI: 1.10-1.43), Europe (OR: 1.27, 95%CI: 1.09-1.48), 3-day dietary record as a dietary assessment tool (OR: 1.30, 95%CI: 1.16-1.46), physical activity adjustment (OR: 1.28, 95%CI: 1.13-1.46), and energy intake adjustment (OR: 1.33, 95%CI: 1.19-1.48). Meta-regression analysis confirmed that geographical region significantly influenced heterogeneity between studies (p <0.05). CONCLUSIONS: An elevated DII is independently linked to a higher risk of GDM, especially in women of normal weight.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".