Dietary high oleic canola oil supplemented with DHA oil attenuates plasma PCSK9 levels in humans
Bibliographic record
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a novel circulating protein, which plays an important role in regulation of cholesterol metabolism by promoting hepatic LDL receptor degradation. However, the action of dietary fat composition on PCSK9 levels remains unknown. A double‐blinded RCT, consisting of five 30‐d intervention periods, was conducted to investigate varying dietary oils on circulating PCSK9 levels in healthy humans. Treatments included 60 g/d of conventional canola oil (Canola), high oleic canola oil (CanoalOleic), high oleic canola/DHA oil blend (CanolaDHA), corn/safflower oil blend (CornSaff) and flax/safflower oil blend (FlaxSaff). PCSK9 levels using ELISA were assessed at the end of each phase. Lipid profiles (n=54) showed that CanolaDHA feeding resulted in the highest (p<0.05) serum total cholesterol (TC) and LDL‐C levels among all five treatments. CanolaDHA feeding also produced the lowest (p<0.05) plasma PCSK9 concentrations (204.5±9.6 ng/ml) compared to Canola (231.6±9.6 ng/ml) and CanolaOleic (230.2±9.6 ng/ml) diets. In CanolaDHA diet, PCSK9 levels correlated (r=0.35, p<0.01) with TC levels but not with LDL‐C levels. Results indicate that post‐diet response in PCSK9 may be altered with CanolaDHA diet. In conclusion, the elevated LDL‐C levels from a DHA oil treatment may not be deleterious as they are accompanied by a decline in PCSK9 levels. (Supported by the Canola Council of Canada and Growing Forward II program)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".