FADS1 (Fatty Acid Desaturase 1) Genotype Associates With Aortic Valve FADS mRNA Expression, Fatty Acid Content and Calcification
Bibliographic record
Abstract
Background: Aortic stenosis (AS) contributes to cardiovascular mortality and morbidity but disease mechanisms remain largely unknown. Recent evidence associates a single nucleotide polymorphism rs174547 within the FADS1 gene, encoding FADS1 (fatty acid desaturase 1), with risk of several cardiovascular outcomes, including AS. FADS1 encodes a rate-limiting enzyme for ω-3 and ω-6 fatty acid metabolism. The aim of this study was to decipher the local transcriptomic and lipidomic consequences of rs174547 in tricuspid aortic valves from patients with AS. Methods: Expression quantitative trait loci study was performed using data from Illumina Human610-Quad BeadChip, Infinium Global Screening Arrays, and Affymetrix Human Transcriptome 2.0 arrays in calcified and noncalcified aortic valve tissue from 58 patients with AS (mean age, 74.2; SD, 5.9). Fatty acid content was assessed in aortic valves from 25 patients with AS using gas chromatography. Δ 5 and Δ 6 desaturase activity was assessed by the product-to-precursor ratio. Results: The minor C-allele of rs174547, corresponding to the protective genotype for AS, was associated with higher FADS2 mRNA levels in calcified valve tissue, whereas FADS1 mRNA and other transcripts in proximity of the single nucleotide polymorphism were unaltered. In contrast, the FADS1 Δ 5-desaturase activity and the FADS2 Δ 6-desaturase activity were decreased. Finally, docosahexaenoic acid was decreased in calcified tissue compared with non-calcified tissue and C-allele carriers exhibited increased docosahexaenoic acid levels. Overall desaturase activity measured with ω-3 fatty acids was higher in C-allele carriers. Conclusions: The association between the FADS1 genotype and AS may implicate effects on valvular fatty acids.
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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 | 0.001 | 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) | 0.002 | 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".