The Association Between Cadmium Exposure and Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective Several studies have evaluated the association of cadmium exposure with the risk of gestational diabetes mellitus (GDM). However, the findings among these studies have been inconsistent. To further investigate the relationship, we carried out a meta-analysis to clarify the relationship between cadmium exposure and GDM risk. Methods Five databases (Scopus, PubMed, Web of Science, Cochrane, and CNKI) were searched for eligible studies until September 09, 2021. The quality of eligible studies was evaluated using the Newcastle–Ottawa quality assessment scale (NOS). The summary odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by random-effects models due to high heterogeneity. Sensitivity analysis was performed to explore the robustness of the results. Publication bias was evaluated by Egger's test and Begg's test. We also conducted meta-regression analysis and subgroup analysis to assess the potential sources of heterogeneity. Results A total of 10 studies with 32,000 participants related to our issue were included. Comparing the highest vs. lowest categories of cadmium exposure, no significant association was observed between cadmium exposure and the risk of GDM (OR = 1.16, 95% CI = 0.92–1.46, and P = 0.206). No publication bias was found in Begg's and Egger's tests (all P > 0.05). Meta-regression suggested that publication year was the potentially heterogeneous source ( P = 0.034). Subgroup analysis of publication year showed that the OR of studies before the year of 2016 was 4.05 (95% CI = 1.87–8.76, P < 0.001), and prospective cohort studies showed a borderline increased GDM risk (OR = 1.15, 95% CI = 0.99–1.33, and P = 0.061). Conclusion Our results indicated no significant association between cadmium exposure and GDM risk. Further high-quality prospective studies, especially those using standard analytic methods for cadmium exposure, are warranted to confirm the results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".