Discovery of placental microRNAs associated with maternal insulin sensitivity during pregnancy
Bibliographic record
Abstract
CONTEXT: During pregnancy, maternal insulin sensitivity decreases, supporting transfer of nutrients to the fetus; when excessive, this can lead to gestational diabetes mellitus. The physiological decline in insulin sensitivity is likely caused by placental factors; however, the identity of these placental factors remains unclear. OBJECTIVE: To identify placental microRNAs (miRNAs) associated with maternal insulin sensitivity during pregnancy. DESIGN: A prospective pregnancy cohort study called Genetics of Glucose regulation in Gestation and Growth. We assessed insulin sensitivity using the Matsuda index during the second trimester of pregnancy, and microtranscriptome expression in placental samples collected at delivery. We accounted for confounders including maternal age, fetal sex, gestational age at delivery, gravidity and maternal body mass index, and surrogate variables, capturing sampling and technical variability. SETTING: Centre Hospitalier Universitaire de Sherbrooke, Canada. PARTICIPANTS: A total of 434 pregnancies were included. The mean (SD) maternal age was 28.6 (4.4) years; Matsuda index, 7.7 (4.9); and gestational age at delivery, 39.4 (1.4) weeks. MAIN OUTCOME MEASURE(S): Placental miRNAs expression (n = 952 miRNAs). RESULTS: We identified 18 placental miRNAs negatively and 1 miRNA positively correlated with Matsuda index (FDR p < 0.05). Gene ontology and tissue expression analysis of the genes targeted by the identified placental miRNAs suggest they may influence metabolic regulation, potentially acting as endocrine factors in skeletal muscle and adipose tissue and as paracrine factors within the placenta. CONCLUSIONS: We identified placental miRNAs that may act as endocrine and paracrine factors to modulate maternal insulin sensitivity during 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".