Association of dietary inflammatory index with risk of gestational diabetes mellitus and preeclampsia: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Findings from observational studies have suggested a possible association between dietary inflammatory index (DII) and risk of gestational diabetes mellitus (GDM) and preeclampsia (PE). However, the results of these studies were inconclusive. A systematic review and meta-analysis was carried out to illuminate this association. Systematic literature search was conducted in PubMed, Web of Science, Cochrane Library, EMBASE, Scopus and other databases from inception until January 2023. The qualities of included studies were assessed using the Newcastle–Ottawa scale. Nine studies (seven cohort, two case–control) were included in the meta-analysis, including 11 423 participants from five different countries. The meta-analysis indicated that a 1-unit increase in the DII score, representing pro-inflammatory diet, was associated with 13 % higher risk of GDM (OR = 1·13; 95 % CI 1·02, 1·25, I2 = 68·4 %, P = 0·004) and 24 % higher risk of PE (OR = 1·24; 95 % CI 1·14, 1·35, I2 = 52·0 %, P = 0·125). Subgroup analysis found that this association was evident among studies with Chinese populations (OR = 1·16; 95 % CI 1·06, 1·28) and studies with mid pregnancy (OR = 1·20; 95 % CI 1·07, 1·34). The findings indicate that pro-inflammatory diet can increase the risk of GDM and PE. Considering some limitations in this study, more studies are needed to verify this association.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.003 | 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".