The effects of magnesium-zinc-calcium-vitamin D co-supplementation on biomarkers of inflammation, oxidative stress and pregnancy outcomes in gestational diabetes
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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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 | 0.001 |
| 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) | 0.002 | 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".