First trimester circulating miR-208b-3p and miR-26a-1-3p are relevant to the prediction of gestational hypertension
Bibliographic record
Abstract
BACKGROUND: Gestational hypertension (GH) is linked to an increased risk of cardiometabolic diseases for both mother and child, but we lack reliable biomarkers to identify high-risk women early in pregnancy. MicroRNAs (miRNAs) are small non-coding RNA that have emerged as promising biomarkers for pregnancy complications. We thus aimed to identify first trimester circulating miRNAs associated with GH and to build a miRNA-based algorithm to predict GH incidence. METHODS: We quantified miRNAs using next-generation sequencing in plasma samples collected at first trimester of pregnancy in Gen3G (N = 413, including 28 GH cases) and 3D (N = 281, including 21 GH cases) prospective birth cohorts. MiRNAs associated with GH in Gen3G (identified using DESeq2, p-value < 0.05) and replicated in 3D were included in a stepwise logistic regression model to estimate the probability of developing GH based on the miRNAs (normalized z-score counts) and maternal characteristics that contribute most to the model. RESULTS: We identified 28 miRNAs associated with the onset of GH later in pregnancy (p < 0.05) in the Gen3G cohort. Among these, three were replicated in the 3D cohort (similar fold change and p < 0.1) and were included in stepwise logistic regression models with GH-related risk factors. When combined with first trimester mean arterial pressure (MAP), miR-208b-3p and miR-26a-1-3p achieve an AUC of 0.803 (95%CI: 0.512-0.895) in Gen3G and 0.709 (95%CI: 0.588-0.829) in 3D. The addition of miR-208b-3p, and miR-26a-1-3p to the model significantly improves the prediction performance over that of MAP alone (p = 0.03). We then proposed low and high-risk thresholds, which could help identify women at very low risk of GH and those who could benefit from prevention monitoring throughout their pregnancy. CONCLUSION: The combination of circulating miR-208b-3p and miR-26a-1-3p with first trimester MAP offers good performance as early predictors of GH. Interestingly, these miRNAs target pathways related to the cardiovascular system and could thus be relevant to the pathophysiology of GH. These miRNAs thus provide a novel avenue to identify women at risk and could lead to even more adequate obstetrical care to reduce the risk of complications associated with GH.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".