Microbiota, a New Playground for the Omega-3 Polyunsaturated Fatty Acids in Cardiovascular Diseases
Bibliographic record
Abstract
Several cardioprotective mechanisms attributed to Omega-3 polyunsaturated fatty acids (PUFAs) have been studied and widely documented. However, in recent years, studies have supported the concept that the intestinal microbiota can play a much larger role than we had anticipated. Microbiota could contribute to several pathologies, including cardiovascular diseases. Indeed, an imbalance in the microbiota has often been reported in patients with cardiovascular disease and produces low-level inflammation. This inflammation contributes to, more or less, long-term development of cardiovascular diseases. It can also worsen the symptoms and the consequences of these pathologies. According to some studies, omega-3 PUFAs in the diet could restore this imbalance and mitigate its harmful effects on cardiovascular diseases. Many mechanisms are involved and included: (1) a reduction of bacteria producing trimethylamine (TMA); (2) an increase in bacteria producing butyrate, which has anti-inflammatory properties; and (3) a decrease in the production of pro-inflammatory cytokines. Additionally, omega-3 PUFAs would help maintain better integrity in the intestinal barrier, thereby preventing the translocation of intestinal contents into circulation. This review will summarize the effects of omega-3 PUFAs on gut micro-biota and the potential impact on cardiac health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".