Are Maternal Dietary Patterns During Pregnancy Associated with the Risk of Gestational Diabetes Mellitus? A Systematic Review of Observational Studies
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Maternal nutritional status is a "key" contributor to Gestational Diabetes Mellitus (GDM). However, the role of maternal dietary patterns (DPs) during pregnancy remains poorly understood. Thus, we conducted a systematic review to assess associations between "a posteriori-derived" DPs and GDM. METHODS: A systematic search was conducted in PubMed, ScienceDirect, Web of Science, and Scopus for cohort, cross-sectional, and case-control studies published until June 2024. A total of twenty-eight studies involving 39,735 pregnant women were included, and their quality was evaluated by the Newcastle-Ottawa Scale. The 91 identified DPs were classified into four categories: "Westernized", "Nutritious", "Plant-based", and "Miscellaneous". RESULTS: Our findings do not reveal definitive associations between maternal DPs during pregnancy and GDM risk. Notably, "Westernized" DPs tended to be associated with an increased risk. However, a very small portion of patterns within this category exhibited protective associations. Conversely, "Nutritious" and "Plant-based" appear beneficial for GDM prevention in specific populations. The "Miscellaneous" category presented an almost equal distribution of DPs with both detrimental and protective associations, pinpointing the absence of a clear directional trend regarding GDM risk. CONCLUSIONS: The heterogeneity in findings can be attributed to geographic and sociocultural variations and methodological differences across studies. Thus, there is a need for more standardized research methodologies to provide more precise insights that will ultimately help develop effective and tailored dietary guidelines for GDM prevention.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".