Extracellular Vesicles as an Index for Endothelial Injury and Cardiac Dysfunction in a Rodent Model of GDM
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) increases risk of adverse pregnancy outcomes and maternal cardiovascular complications. It is widely believed that maternal endothelial dysfunction is a critical determinant of these risks, however, connections to maternal cardiac dysfunction and mechanisms of pathogenesis are unclear. Circulating extracellular vesicles (EVs) are emerging biomarkers that may provide insights into the pathogenesis of GDM. We examined the impact of GDM on maternal cardiac and vascular health in a rat model of diet-induced obesity-associated GDM. We observed a >3-fold increase in circulating levels of endothelial EVs (p < 0.01) and von Willebrand factor (p < 0.001) in GDM rats. A significant increase in mitochondrial DNA (mtDNA) within circulating extracellular vesicles was also observed suggesting possible mitochondrial dysfunction in the vasculature. This was supported by nicotinamide adenine dinucleotide deficiency in aortas of GDM mice. GDM was also associated with cardiac remodeling (increased LV mass) and a marked impairment in maternal diastolic function (increased isovolumetric relaxation time [IVRT], p < 0.01). Finally, we observed a strong positive correlation between endothelial EV levels and IVRT (r = 0.57, p < 0.05). In summary, we observed maternal vascular and cardiac dysfunction in rodent GDM accompanied by increased circulating endothelial EVs and EV-associated mitochondrial DNA. Our study highlights a novel method for assessment of vascular injury in GDM and highlights vascular mitochondrial injury as a possible therapeutic target.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".