Omega‐3 Fatty Acids and Heart Rhythm, Rate, and Variability in Atrial Fibrillation
Bibliographic record
Abstract
Background Previous randomized control trials showed mixed results concerning the effect of omega-3 fatty acids (n-3 FAs) on atrial fibrillation (AF). The associations of n-3 FA blood levels with heart rhythm in patients with established AF are unknown. The goal of this study was to assess the associations of total and individual n-3 FA blood levels with AF type (paroxysmal versus nonparoxysmal), heart rate (HR), and HR variability in patients with AF. Methods and Results Total n-3 FAs, eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, and alpha-linolenic acid blood levels were determined in 1969 patients with known AF from the SWISS-AF (Swiss Atrial Fibrillation cohort). Individual and total n-3 FAs were correlated with type of AF, HR, and HR variability using standard logistic and linear regression, adjusted for potential confounders. Only a mild association with nonparoxysmal AF was found with total n-3 FA (odds ratio [OR], 0.97 [95% CI, 0.89-1.05]) and docosahexaenoic acid (OR, 0.93 [95% CI, 0.82-1.06]), whereas other individual n-3 FAs showed no association with nonparoxysmal AF. Higher total n-3 FAs (estimate 0.99 [95% CI, 0.98-1.00]) and higher docosahexaenoic acid (0.99 [95% CI, 0.97-1.00]) tended to be associated with slower HR in multivariate analysis. Docosapentaenoic acid was associated with a lower HR variability triangular index (0.94 [95% CI, 0.89-0.99]). Conclusions We found no strong evidence for an association of n-3 FA blood levels with AF type, but higher total n-3 FA levels and docosahexaenoic acid might correlate with lower HR, and docosapentaenoic acid with a lower HR variability triangular index.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".