Relationship between serum mineral levels in the second and third trimester of pregnancy and the risk of gestational diabetes mellitus: a retrospective cohort study
Bibliographic record
Abstract
Introduction Gestational diabetes mellitus (GDM) poses significant health risks for both the mother and fetus, and it also increases the mother’s risk of developing type 2 diabetes later in life. Mineral elements may play a crucial role in the development of GDM by influencing insulin metabolism. However, comprehensive studies on serum mineral levels during pregnancy remain limited. This study aims to evaluate the relationship between serum mineral levels in pregnant women during the second and third trimesters and the risk of developing GDM. Methods This retrospective cohort study included 17,224 singleton pregnancies delivered between 2016 and 2022 at a tertiary hospital in China. Maternal demographic data and serum mineral concentration information from the mid and late stages of pregnancy were collected through the hospital information system. Analyses were conducted using restricted cubic spline models and multivariate logistic regression models. Results The prevalence of GDM in this study was 15.07%. Chloride [ P for overall = 0.01; P for non-linear = 0.373; OR (95% CI) = 1.03 (1.01, 1.05)] showed a significant linear positive association with GDM. Additionally, serum levels of calcium ( P for non-linear < 0.001), potassium ( P for nonlinear = 0.036), and magnesium (P for nonlinear < 0.001) were found to have non-linear relationships with the risk of GDM. The interactions between calcium and magnesium [OR (95% CI) = 0.05 (0.01, 0.27), P for interaction < 0.001], potassium and magnesium [OR (95% CI) = 0.11 (0.03, 0.37), P for interaction < 0.001], and potassium and chloride [OR (95% CI) = 1.06 (1.01, 1.11), P for interaction < 0.001] were significant. Discussion The study indicates that specific serum mineral levels in pregnant women are closely associated with the risk of gestational diabetes mellitus. A deeper understanding of the mechanisms and interactions of these minerals could aid in developing effective prevention and treatment strategies, thereby reducing the incidence of GDM and improving pregnancy outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".