The effects of magnesium supplementation on gene expression related to inflammatory markers, vascular endothelial growth factor, and pregnancy outcomes in patients with gestational diabetes.
Bibliographic record
Abstract
Magnesium has been introduced as one of the micronutrients with several metabolic benefits, mainly anti-inflammatory properties. The aim of this study was to evaluate the effects of magnesium supplementation on gene expression of inflammatory markers, vascular endothelial growth factor (VEGF), and pregnancy outcomes in women diagnosed with gestational diabetes mellitus (GDM). This randomized, double-blinded, placebo-controlled trial was conducted among 36 women, aged 18-40 years old, diagnosed with GDM. Study participants were randomly allocated into two groups to receive either 250 mg/day magnesium oxide (n = 18) or placebo (n = 18) for six weeks. Gene expression related to inflammatory markers and VEGF was assessed using peripheral blood mononuclear cells (PBMCs) of women with GDM, via RT-PCR method. Quantitative results of RT-PCR demonstrated that magnesium supplementation downregulated gene expression levels of interleukin-8 (IL-8) (P = 0.03) and tumor necrosis factor-α (TNF-α) (P = 0.006) and upregulated gene expression levels of transforming growth factor beta (TGF-β) (P = 0.03) in PBMCs of women with GDM, compared with placebo. Magnesium supplementation did not significantly affect gene expression of IL-1 and vascular endothelial growth factor. Additionally, magnesium administration resulted in a lower incidence of newborn hyperbilirubinemia (11.1% versus 44.4%, P = 0.02) and newborn hospitalization (11.1% versus 44.4%, P = 0.02) compared with placebo. Overall, magnesium supplementation for six weeks significantly decreased gene expression levels of IL-8 and TNF-α, and increased TGF-β in women with GDM. Therefore, magnesium supplementation might be recommended to decrease metabolic complications in women with GDM, due to its beneficial effects on gene expression of inflammatory markers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".