Docosahexaenoic Acid, but Not Eicosapentaenoic Acid, Supplementation Reduces Vulnerability to Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: The potential health benefits of ω-3 polyunsaturated fatty acids (PUFAs) usually are studied using a combination of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). This combination reduces vulnerability to experimentally induced atrial fibrillation (AF). It is unknown whether EPA and DHA have differential effects when taken alone. Using a model of pacing-induced atrial hemodynamic overload, we investigated the individual effects of EPA and DHA on vulnerability to AF and atrial remodeling. METHODS AND RESULTS: Thirty-four dogs were randomized into 3 groups, all of which underwent simultaneous atrial and ventricular pacing at 220 beats per minute for 14 days. One group received purified DHA (≈1 g/d) orally for 21 days beginning 7 days before pacing began. Similarly, 1 group received ≈1 g/d purified EPA. In a third (control) group (No-PUFAs), 8 dogs received ≈1 g/d olive oil, and 12 were unsupplemented. Electrophysiological and echocardiographic measurements were taken at baseline and 21 days. Atrial tissue samples were collected at 21 days for histological and molecular analyses. Persistent AF inducibility was significantly reduced by DHA compared with No-PUFAs median [25-75 percentiles], 0% [0%-3%] for DHA versus 3.1% [2.2%-11%] for No-PUFAs; P=0.007) but not by EPA (3.4% [1.9%-8.9%]). DHA also reduced atrial fibrosis compared with No-PUFAs (11 ± 6% versus 20 ± 4%, respectively; P<0.05), whereas EPA did not (15 ± 5%; P>0.05). CONCLUSIONS: DHA is more effective than EPA in attenuating AF vulnerability and atrial remodeling in structural remodeling-induced AF.
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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) | 0.001 | 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 | 0.001 |
| 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".