Omega-3 polyunsaturated fatty acids focusing on eicosapentaenoic acid and docosahexaenoic acid in the prevention of cardiovascular diseases: a review of the state-of-the-art
Bibliographic record
Abstract
INTRODUCTION: : An epidemiological study of Greenlandic Inuit suggested the importance of omega-3 polyunsaturated fatty acids (PUFAs) in preventing ischemic heart disease. After this landmark study, large-scale epidemiological studies have examined the benefits of omega-3 PUFAs in the prevention of cardiovascular diseases. AREAS COVERED: : This article reviews studies on omega-3 PUFAs, and identifies issues relevant to cardiovascular risk. EXPERT OPINION: : Recent studies have focused on the anti-inflammatory effects of omega-3 PUFAs and specialized pro-resolving mediators. High-purity eicosapentaenoic acid (EPA) ethyl ester and EPA/docosahexaenoic acid (DHA) preparations have been developed primarily for the treatment of hypertriglyceridemia. Various trials on the cardiovascular protective effects of omega-3 PUFAs have been reported, but the results have not been consistent. Some issues of the trials have been suggested, such as using low-dose omega-3 PUFAs and not including hypertriglyceridemia in subject selection criteria. REDUCE-IT study that used a high dose of high-purity EPA preparation showed a relative reduction in cardiovascular events, but, the STRENGTH study that used a high dose of EPA/DHA preparation did not support this benefit. This article reviews the roles of omega-3 PUFAs in cardiovascular diseases, including progress in understanding the molecular mechanisms and recent large-scale clinical trials.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".