A Transcriptomic Approach to Sex Differences in Calcific Aortic Valve Stenosis in Patients with a Tricuspid Aortic Valve
Bibliographic record
Abstract
Background Valvular lesions in calcific aortic valve stenosis are sex-specific: female patients reach a similar level of severity as male patients but with less valvular calcification and more valvular fibrosis. We thus aim to assess the transcriptome of stenotic aortic valves according to patients' sex. Methods A total of 300 valves were collected, and genomewide gene expression was quantified using a microarray on 240. Among these, 62 female patients were matched with 62 male patients, for age (within 2 years), body mass index (within 2 kg/m 2 ), arterial pressure (within 10/5 mm Hg), diabetes (exact), hypertension (exact), and calcific aortic valve stenosis severity. Among the 60 remaining valves, 16 female and 16 male patients were similarly matched for real-time quantitative polymerase chain reaction analysis. Results Clinical and echocardiographic characteristics of the patients were comparable between female and male patients, except for the incidence of coronary artery disease and body surface area (greater in male patients). A total of 190 genes were regulated differently in female vs male patients—132 on autosomes, and 58 on sexual chromosomes. Differences were found in inflammation and lipid metabolism–associated genes. Genes linked to intensified fibrosis processes (eg, TGFβ2 , KIF1A , FRAS1 ) were overexpressed in female vs male patients. Genes associated with increased calcification were overexpressed in both male ( CPAMD8, STC2 ) and female ( RCN2, TPD52L1 ) patients. Genes involved in apoptosis ( CES4 , SFRP4 , TGFB2 ) were overexpressed in female vs male patients. Only KIF1A was validated by real-time quantitative polymerase chain reaction analyses. Conclusions This study provides evidence that sex may influence aortic valve gene expression through different mechanisms in female vs male individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".