Cardiac proinflammatory pathways are altered with different dietary n-6 linoleic to n-3 α-linolenic acid ratios in normal, fat-fed pigs
Bibliographic record
Abstract
Although dietary fat has been associated with inflammation and cardiovascular diseases (CVD), most studies have focused on individuals with preexisting diseases. However, the role of dietary fatty acids on inflammatory pathways before the onset of any abnormality may be more relevant for identifying initiating factors and interventions for CVD prevention. We fed young male pigs one of three diets differing in n-6 and n-3 polyunsaturated fatty acids (PUFA) linoleic acid (LA, 18:2n-6) and alpha-linolenic acid (ALA, 18:3n-3) for 30 days. Cardiac membrane phospholipid fatty acids, phospholipase A(2) (PLA(2)) isoform activities, and cyclooxygenase (COX)-1 and -2 and 5-lipoxygenase (5-LO) expression were measured. The low PUFA diet (% energy, 1.2% LA+0.06% ALA) increased arachidonic acid (AA) and decreased eicosapentaenoic acid (EPA) in heart membranes and increased Ca(2+)-independent iPLA(2) activity, COX-2 expression, and activation of 5-LO. Increasing dietary ALA while keeping LA constant (1.4% LA+1.2% ALA) decreased the heart membrane AA, increased EPA, and prevented proinflammatory enzyme activation. However, regardless of high ALA, high dietary LA (11.6% LA and 1.2% ALA) decreased EPA and led to a high heart membrane AA, and Ca(2+)-dependent cPLA(2) with a marked increase in nitrosative stress. Our results suggest that the potential cardiovascular benefit of ALA is achieved only when dietary LA is reduced concomitantly rather than fed with high LA diet. The increased nitrosative stress in the unstressed heart with high dietary LA suggests that biomarkers of nitrosative stress may offer a useful early marker of the effects of dietary fat on oxidative tissue stress.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".