Current Evidence Supporting the Link Between Dietary Fatty Acids and Cardiovascular Disease
Bibliographic record
Abstract
Lack of consensus exists pertaining to the scientific evidence regarding effects of various dietary fatty acids on cardiovascular disease (CVD) risk. The objective of this article is to review current evidence concerning cardiovascular health effects of the main dietary fatty acid types; namely, trans (TFA), saturated (SFA), polyunsaturated (PUFA; n-3 PUFA and n-6 PUFA), and monounsaturated fatty acids (MUFA). Accumulating evidence shows negative health impacts of TFA and SFA; both may increase CVD risk. Policies have been proposed to reduce TFA and SFA consumption to less than 1 and 7 % of energy intake, respectively. Cardiovascular health might be promoted by replacing SFA and TFA with n-6 PUFA, n-3 PUFA, or MUFA; however, the optimal amount of PUFA or MUFA that can be used to replace SFA and TFA has not been defined yet. Evidence suggests of the potential importance of restricting n-6 PUFA up to 10 % of energy and obtaining an n-6/n-3 ratio as close as possible to unity, along with a particular emphasis on consuming adequate amounts of essential fatty acids. The latest evidence shows cardioprotective effects of MUFA-rich diets, especially when MUFA are supplemented with essential fatty acids; namely, docosahexaenoic acid. MUFA has been newly suggested to be involved in regulating fat oxidation, energy metabolism, appetite sensations, weight maintenance, and cholesterol metabolism. These favorable effects might implicate MUFA as the preferable choice to substitute for other fatty acids, especially given the declaration of its safety for up to 20 % of total energy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".