Docosahexaenoic acid supplementation in gestational diabetes mellitus and neonatal metabolic health biomarkers
Bibliographic record
Abstract
Background and objective Gestational diabetes mellitus (GDM) “programs” an elevated risk of metabolic dysfunctional disorders in the offspring, and has been associated with elevated leptin and decreased adiponectin levels in cord blood. We sought to assess whether docosahexaenoic acid (DHA) supplementation in GDM affects neonatal metabolic health biomarkers especially leptin and adiponectin. Methods In a randomized controlled trial, singleton pregnant women with de novo diagnosis of GDM at 24–28 weeks of gestation were randomized to dietary supplementation of 500 mg DHA per day (intervention, n = 30) until delivery or standard care (control, n = 38). The primary outcomes were cord blood leptin and total adiponectin concentrations. Secondary outcomes included high-molecular-weight (HMW) adiponectin and insulin-like growth factor-1 (IGF-1) concentrations in cord blood, maternal glycemic control post-intervention and birth weight ( z score). In parallel, 38 euglycemic pregnant women were recruited for comparisons of cord blood biomarkers. Results There were no significant differences in cord serum leptin, total and HMW adiponectin and IGF-1 concentrations between DHA supplementation and control groups (all p > 0.05). Maternal fasting and 2-h postprandial blood glucose levels at 12–16 weeks post-intervention were similar between the two groups. The newborns in the DHA group had higher birth weight z scores ( p = 0.02). Cord blood total and HMW adiponectin concentrations were significantly lower in GDM vs. euglycemic pregnancies. Conclusion Docosahexaenoic acid supplementation at 500 mg/day in GDM women did not affect neonatal metabolic biomarkers including leptin, adiponectin and IGF-1. The results are reassuring in light of the absence of influence on neonatal adipokines (leptin and adiponectin), and potential benefits to fetal growth and development. Clinical Trial Registration: Clinicaltrials.gov, NCT03569501.
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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 | 0.001 | 0.001 |
| 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".