N‐3 Polyunsaturated Fatty Acids: Relationship to Inflammation in Healthy Adults and Adults Exhibiting Features of Metabolic Syndrome
Bibliographic record
Abstract
Individuals with metabolic syndrome (MetS) have a higher risk of type 2 diabetes and cardiovascular disease, therefore, research has been directed at reducing various components that contribute to MetS and associated metabolic impairments, including chronic low-grade inflammation. Epidemiological, human, animal and cell culture studies provide evidence that dietary n-3 polyunsaturated fatty acids (n-3 PUFA), including alpha-linolenic acid (18:3n-3, ALA), eicosapentaenoic acid (20:5n-3, EPA) and/or docosahexaenoic acid (22:6n-3, DHA) may improve some of the components associated with MetS. The current review will discuss recent evidence from human observational and intervention studies that focused on the effects of ALA, EPA or DHA on inflammatory markers in healthy adults and those with one or more features of MetS. Observational studies in healthy adults support the recommendation that a diet rich in n-3 fatty acids may play a role in preventing and reducing inflammation, whereas intervention studies in healthy adults have yielded inconsistent results. The majority of intervention studies in adults with features of MetS have reported a benefit for some inflammatory measures; however, other studies using high n-3 fatty acid doses and long supplementation periods have reported no effect. Overall, the data reviewed herein support recommendations for regular fatty fish consumption and point toward health benefits in terms of lowering inflammation in adults with one or more features of MetS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".