Circulating extracellular vesicles during pregnancy in women with type 1 diabetes: a secondary analysis of the CONCEPTT trial
Bibliographic record
Abstract
BACKGROUND: Extracellular vesicles are membrane vesicles that are released into the extracellular environment and accumulate in the circulation in vascular disease. We aimed to quantify circulating extracellular vesicles in pregnant women with type 1 diabetes and to examine associations between extracellular vesicle levels, continuous glucose measures, and pregnancy outcomes. METHODS: We used plasma samples from the Continuous Glucose Monitoring in Women with Type 1 Diabetes in Pregnancy Trial study and quantified circulating extracellular vesicles by flow cytometry (n = 163). Relationships with clinical variables were assessed by repeated measures correlation. Logistic regression was used to assess associations between elevated extracellular vesicle levels and pregnancy outcomes. RESULTS: Platelet extracellular vesicle levels were inversely associated with glucose time above range and glycaemic variability measures (P < 0.05). A weak positive association was observed between endothelial extracellular vesicles and mean amplitude of glycemic excursion (P < 0.05). In a univariate logistic regression model, high baseline endothelial extracellular vesicles was associated with increased risk of neonatal intensive care unit (NICU) admission (OR: 2.06, 1.03-4.10), and respiratory distress requiring ventilation (OR: 4.98, 1.04-23.92). After adjusting for HbA1c and blood pressure the relationship for NICU admission persisted and an association with hyperbilirubinemia was seen (OR: 2.56, 1.10-5.94). Elevated platelet extracellular vesicles were associated with an increased risk of NICU admission (OR: 2.18, 1.04-4.57), and hyperbilirubinemia (OR: 2.61, 1.11-6.12) after adjusting for HbA1c and blood pressure. CONCLUSIONS: High levels of extracellular vesicles in early pregnancy were associated with adverse neonatal outcomes. Assessment of extracellular vesicles may represent a novel approach to personalized care in type 1 diabetes pregnancy.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".