Abstract 11668: Transcriptomic Analysis Reveals Sex Differences in Gene Expression Profiling of Stenotic Aortic Valves
Bibliographic record
Abstract
Introduction: In aortic stenosis (AS), women present less aortic valve calcification and more aortic valve fibrosis than men for the same hemodynamic severity. However, the specific mediators that drive the fibro-calcific differences between men and women remain unclear. We aim to assess the transcriptome of stenotic aortic valves explanted during aortic valve replacement, according to patient’s sex. Methods: Transcriptomic profile was obtained from 240 explanted human aortic valves. Among these 240 patients (120 women and 120 men), 62 women were matched with 62 men for age (within 2 years), body mass index (within 2 kg/m 2 ), arterial pressure (within 10/5 mmHg), diabetes (exact), hypertension (exact) and AS severity (Table 1). Genes were classified in 6 key processes of AS development: oxidative stress, inflammation, lipid metabolism, fibrosis, apoptosis and calcification following a literature review. Results: One hundred and ninety (190) genes were differently regulated between men and women: 132 on autosomes and 58 on sexual chromosomes. Among these genes, 106 were over-expressed and 84 were under-expressed in women compared to men (Figure 1). Different genes involved in processes of inflammation, lipid metabolism and calcification were up-regulated both in women and men (women: NET1 , KIF1A, CES1, RCN2; men: FERMT3, APOD, CPAMD8, STC2 ). Genes involved in apoptosis ( SFRP4 ) and fibrosis processes ( TGFβ2 , FRAS1 ) were overexpressed in women. Conclusions: This study provides evidence that sex may influence aortic valve gene expression through different mechanisms in females and males, favoring pro-fibrotic and pro-apoptotic processes in women.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".