The Omega-3 Fatty Acid Eicosapentaenoic Acid (EPA) Correlates Inversely with Ischemic Brain Infarcts in Patients with Atrial Fibrillation
Bibliographic record
Abstract
The omega-3 fatty acid (n-3 FA) eicosapentaenoic acid (EPA) reduces stroke in patients with atherosclerotic cardiovascular disease. Whether EPA affects stroke or cerebral small vessel dis-ease in patients with atrial fibrillation (AF) remains uncertain. EPA, docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), and alpha-linolenic acid (ALA) were determined by gas chromatography in 1657 AF patients from the Swiss Atrial Fibrillation study. All patients underwent brain MRI to detect ischemic brain infarcts, classified as large noncortical or cortical infarcts (LNCCIs); markers of small vessel disease, classified as small noncortical infarcts (SNCIs), number of microbleeds, and white matter lesion (WML) volumes. Individual and total n-3 FAs (EPA + DHA + DPA + ALA) were correlated with LNCCIs and SNCIs using logistic regression, with numbers of microbleeds using a hurdle model, and WML volumes using linear regression. LNCCIs were detected in 372 patients (22.5%). EPA correlated inversely with the prevalence of LNCCIs (odds ratio [OR] 0.51 per increase of 1 percentage point EPA, 95% confidence interval [CI] 0.29-0.90). DPA correlated with a higher LNCCI prevalence (OR 2.48, 95%CI 1.49-4.13). No associations with LNCCIs were found for DHA, ALA, and total n-3 FAs. Neither individual nor total n-3 FAs correlated with markers of small vessel disease. In conclusion, EPA correlates inversely with the prevalence of ischemic brain infarcts, but not with markers of small vessel disease in patients with AF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".