Omega-3 and omega-6 fatty acids have distinct effects on endothelial fatty acid content and nitric oxide bioavailability
Bibliographic record
Abstract
Treatment with high dose icosapent ethyl (IPE), an ethyl ester of the omega-3 fatty acid eicosapentaenoic acid (EPA), significantly reduced ischemic events in patients with either cardiovascular disease (CV) or diabetes plus other risk factors (REDUCE-IT) but the mechanism is not well understood. We compared the effects of EPA, docosahexaenoic acid (DHA), and the omega-6 fatty acid arachidonic acid (AA) on bioavailability of nitric oxide (NO) and fatty acid composition. Human umbilical vein endothelial cells (HUVECs) were pretreated with EPA, DHA, or AA (10 µM). Cells were stimulated with calcium ionophore and NO and peroxynitrite (ONOO−) were measured using porphyrinic nanosensors. Levels of EPA, DHA, AA and other fatty acids were measured by gas chromatography (GC). EPA treatment caused the greatest NO release (18%, p < 0.001) and reduction in ONOO− (13%, p < 0.05) compared to control; the [NO]/[ ONOO−] ratio increased by 35% (p < 0.001). DHA treatment increased NO levels by 12% (p < 0.01) but had no effect on ONOO− release. AA did not affect either NO or ONOO− release. Fatty acid treatments increased their respective levels in endothelial cells. EPA levels increased 10-fold to 4.59 mg/g protein (p < 0.001) with EPA treatment and the EPA/AA ratio increased by 10-fold (p < 0.001) compared to vehicle. Only EPA increased docosapentaenoic acid (DPA, omega-3) levels by 2-fold (p < 0.001). AA alone decreased the EPA/AA ratio 4-fold (p<0.001). These findings support a preferential benefit of EPA on endothelial function and omega-3 fatty acid content.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".