Association Between Omega‐3 Fatty Acid Intake and Dyslipidemia: A Continuous Dose–Response Meta‐Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background Previous results provide supportive but not conclusive evidence for the use of omega-3 fatty acids to reduce blood lipids and prevent events of atherosclerotic cardiovascular disease, but the strength and shape of dose-response relationships remain elusive. Methods and Results This study included 90 randomized controlled trials, reported an overall sample size of 72 598 participants, and examined the association between omega-3 fatty acid (docosahexaenoic acid, eicosapentaenoic acid, or both) intake and blood lipid changes. Random-effects 1-stage cubic spline regression models were used to study the mean dose-response association between daily omega-3 fatty acid intake and changes in blood lipids. Nonlinear associations were found in general and in most subgroups, depicted as J-shaped dose-response curves for low-/high-density lipoprotein cholesterol. However, we found evidence of an approximately linear dose-response relationship for triglyceride and non-high-density lipoprotein cholesterol among the general population and more evidently in populations with hyperlipidemia and overweight/obesity who were given medium to high doses (>2 g/d). Conclusions This dose-response meta-analysis demonstrates that combined intake of omega-3 fatty acids near linearly lowers triglyceride and non-high-density lipoprotein cholesterol. Triglyceride-lowering effects might provide supportive evidence for omega-3 fatty acid intake to prevent cardiovascular events.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".