PS-C17-6: MATERNAL ENDOTHELIAL MITOCHONDRIAL DYSFUNCTION IN GESTATIONAL DIABETES MELLITUS
Bibliographic record
Abstract
Objective: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes, and short and long-term maternal and neonatal cardiovascular risks. Our laboratory has shown that elevated levels of endothelial extracellular vesicles (EVs) in pregnant women is predictive of adverse pregnancy outcomes in type 1 diabetes. However, this matter has not been explored in GDM. We therefore investigated the impact of GDM on maternal vascular health, focusing on the mitochondria. Design and method: We determined the mitochondrial protein expression altered by GDM in rat kidneys by Western blot analysis. We examined the levels of circulating endothelial EVs and EV mitochondrial DNA (mtDNA) from rat plasma samples via flow cytometry and quantitative real-time PCR, respectively. We assessed the effects of high glucose exposure for 48 hours on endothelial mitochondrial function in human umbilical vein endothelial cells (HUVECs). Results: In a rat model of diet-induced GDM (Pereira, 2015 J Physiol), we observed significant decreases in expression of oxidative phosphorylation Complexes I (p < 0.05) and II (p < 0.01) in kidneys. We also observed a three-fold increase in late gestational levels of circulating endothelial EVs in GDM rats (p < 0.01) as well as increases in levels of EV-associated mtDNA, COX2 (p < 0.05) and ND2 (p < 0.01), suggesting mitochondrial dysfunction. In cultured HUVECs exposed to high glucose for 48 hours, we observed a significant decrease in Complex II expression (p < 0.01) and five-fold increases in EV mtDNA levels. This effect persisted even after removing high glucose exposure for 24 hours. Conclusion: These data show maternal endothelial dysfunction in GDM, accompanied by mitochondrial dysfunction and increased endothelial EV release. These findings suggest that endothelial mitochondrial health may represent a novel approach to reduce risks associated with 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".