The Effects of Myo-Inositol and B and D Vitamin Supplementation in the db/+ Mouse Model of Gestational Diabetes Mellitus
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is a growing concern, affecting an increasing number of pregnant women worldwide. By predisposing both the affected mothers and children to future disease, GDM contributes to an intergenerational cycle of obesity and diabetes. In order to stop this cycle, safe and effective treatments for GDM are required. This study sought to determine the treatment effects of dietary supplementation with myo-inositol (MI) and vitamins B2, B6, B12, and D in a mouse model of GDM (pregnant db/+ dams). In addition, the individual effects of vitamin B2 were examined. Suboptimal B2 increased body weight and fat deposition, decreased GLUT4 adipose tissue expression, and increased expression of inflammatory markers. MI supplementation reduced weight and fat deposition, and reduced expression of inflammatory markers in adipose tissue of mice on suboptimal B2. MI also significantly reduced the hyperleptinemia observed in db/+ mice, when combined with supplemented B2. MI was generally associated with adipose tissue markers of improved insulin sensitivity and glucose uptake, while the combination of vitamins B2, B6, B12, and D was associated with a reduction in adipose inflammatory marker expression. These results suggest that supplementation with MI and vitamin B2 could be beneficial for the treatment/prevention of GDM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".