A comprehensive meta-analysis on the association between vitamin C intake and gestational diabetes mellitus: Insights and novel perspectives
Bibliographic record
Abstract
BACKGROUND: Gestational Diabetes Mellitus (GDM) is a significant health concern in pregnant women and their offspring. Although Vitamin C is known to play a role in maintaining normal physiological processes, its relationship with GDM has not been fully elucidated. METHODS: We conducted a systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, analyzing data from 15 studies selected from PubMed, Embase, Web of Science, and the Cochrane Library up to May 16, 2023. These studies were selected based on inclusion criteria such as study design, outcome of interest, exposure factor, and data extractability. Quality assessment was performed using the Newcastle-Ottawa Scale. We assessed the heterogeneity between studies and conducted a sensitivity analysis. RESULTS: Data from 10,131 subjects, including 1304 diagnosed GDM cases, were analyzed. The meta-analysis showed that women in the low Vitamin C exposure group had higher odds of developing GDM (odds ratio 2.72, 95% CI:1.24-4.19). There was a greater likelihood of increased GDM risk with lower Vitamin C exposure (standardized mean difference: -0.71, 95% CI [-1.07 -0.36]). Subgroup analysis revealed that both internal and external Vitamin C exposure, along with exposure during the second or third trimester of pregnancy, was associated with higher GDM incidence rates under low Vitamin C exposure. Sensitivity analysis confirmed the robustness of the results, and no significant publication bias was detected. CONCLUSION: Low Vitamin C exposure during pregnancy may increase the risk of GDM. Given these findings, it could be beneficial for pregnant women to increase their intake of Vitamin C-rich foods and to ensure adequate blood Vitamin C levels as a preventive measure against GDM.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.057 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".