The effects of magnesium-zinc-calcium-vitamin D co-supplementation on biomarkers of inflammation, oxidative stress and pregnancy outcomes in gestational diabetes
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Diabetes is the most common medical condition in pregnant women and its complications affect both mother and fetus. The beneficial effects of vitamin D on gestational diabetes have been shown, though data on the effects of co-administration of vitamin D with other nutrients on pregnancy outcomes in gestational diabetes (GDM) are scarce. This study was aimed to determine the effects of magnesium-zinc-calcium-vitamin D co-supplementation on parameters of inflammation and oxidative stress, and pregnancy outcomes among women with GDM. METHODS: This randomized, double-blinded, placebo-controlled trial was conducted on 60 women with GDM not taking oral hypoglycemic agents. Patients were randomly assigned to take magnesium-zinc-calcium-vitamin D supplements (n = 30) or placebo (n = 30) for 6 weeks. Fasting blood samples were collected from participants at baseline and after the 6-week intervention to measure related biomarkers. RESULTS: Magnesium-zinc-calcium-vitamin D co-supplementation resulted in a significant reduction in serum high-sensitivity C-reactive protein (- 1.2 ± 3.5 vs. + 0.8 ± 2.0 mg/L, P = 0.01) and plasma malondialdehyde concentrations (- 0.3 ± 0.3 vs. + 0.3 ± 1.1 μmol/L, P = 0.003), as well as a significant increase in total antioxidant capacity levels (+ 38.2 ± 76.5 vs. -16.3 ± 93.5 mmol/L, P = 0.01), compared to placebo. We found a decreasing trend in newborns' weight (3089.8 ± 519.9 vs. 3346.3 ± 411.1 g, P = 0.05) and the rate of macrosomia (3.3% vs. 16.7%, P = 0.08) in the magnesium-zinc-calcium-vitamin D supplemented women. CONCLUSIONS: Overall, the findings of this study have demonstrated that magnesium-zinc-calcium-vitamin D co-supplementation for 6 weeks to women with GDM may reduce biomarkers of inflammation and oxidative stress. This study was retrospectively registered on 25 April 2017 in the Iranian website ( www.irct.ir ) for clinical trials registration ( http://www.irct.ir : IRCT201704225623N109).
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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 | 0.000 | 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 it