Circulating miR-206 and Wnt-signaling are associated with cardiovascular complications and a history of preeclampsia in women
Bibliographic record
Abstract
Women with a history of preeclampsia (PE) have increased risk of cardiovascular disease (CVD) later in life. However, the molecular determinants underlying this risk remain unclear. We sought to understand how circulating miRNA levels are affected by prior PE, and related to biological pathways underpinning cardiovascular disease. RNA sequencing was used to profile plasma levels of 2578 miRNAs in a retrospective study of women with a history of PE or normotensive pregnancy, in two independent cohorts with either acute coronary syndrome (ACS) (n = 17-18/group) or no ACS (n = 20/group). Differential miRNA alterations were assessed in relation to a history of PE (within each cohort) or ACS (across cohorts), and compared with miRNAs previously reported to be altered during PE. A history of PE was associated with altered levels of 30 and 20 miRNAs in the ACS and non-ACS cohorts, respectively, whereas ACS exposure was associated with alterations in 259 miRNAs. MiR-206 was identified at the intersection of all comparisons relating to past/current PE and ACS exposure, and has previously been implicated in atherogenic activities related to hepatocytes, vascular smooth muscle cells and macrophages. Integration of all differentially altered miRNAs with their predicted and experimentally validated targets in silico revealed a number of highly targeted genes with potential atherogenic functions (including NFAT5, CCND2 and SMAD2), and one significantly enriched KEGG biological pathway (Wnt signaling) that was shared between all exposure groups. The present study provides novel insights into miRNAs, target genes and biological pathways that may underlie the long-term cardiovascular sequelae of PE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".