Dietary fatty acids, dietary patterns, and lipoprotein metabolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Few studies have reviewed the impact of dietary fat and dietary patterns on lipoprotein metabolism. This review intends to provide perspective on this topic, while focusing primarily on the studies that assessed intravascular lipoprotein kinetics in humans using isotope methodologies. RECENT FINDINGS: Data suggest that dietary saturated fatty acids slow the clearance of LDL apolipoprotein (apo)B-100 and of apoA-I from the circulation, whereas possibly increasing also apoA-I production. Dietary trans fats reduce the clearance of LDL apoB-100, whereas increasing the clearance of apoA-I. n-3 polyunsaturated fatty acids (PUFAs) intake reduces the production of apoB-48-containing lipoproteins as well as of VLDL apoB-100 and increases their conversion into smaller lipoproteins. Medium-chain triglycerides appear to have no significant effect on lipoprotein kinetics. Finally, Mediterranean diet in the absence of weight loss reduces LDL cholesterol, primarily by enhancing its clearance from the circulation. SUMMARY: Kinetic studies with tracers allow a better appreciation of the impact of specific dietary factors on plasma lipid risk factors. However, additional studies are required to better document the effect of monounsaturated fatty acids, n-6 PUFAs, and of whole diets on lipoprotein metabolism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".