High-oleic rapeseed (canola) and flaxseed oils modulate serum lipids and inflammatory biomarkers in hypercholesterolaemic subjects
Bibliographic record
Abstract
Recently, novel dietary oils with modified fatty acid profiles have been manufactured to improve fatty acid intakes and reduce CVD risk. Our objective was to evaluate the efficacy of novel high-oleic rapeseed (canola) oil (HOCO), alone or blended with flaxseed oil (FXCO), on circulating lipids and inflammatory biomarkers v. a typical Western diet (WD). Using a randomised, controlled, crossover trial, thirty-six hypercholesterolaemic subjects consumed three isoenergetic diets for 28 d each containing approximately 36% energy from fat, of which 70% was provided by HOCO, FXCO or WD. Dietary fat content of SFA, MUFA, PUFA n-6 and n-3 was 6, 23, 5, 1% energy for HOCO; 6, 16, 5, 7·5% energy for FXCO; 11·5, 16, 6, 0·5% energy for WD. After 28 d, compared with WD, LDL-cholesterol was reduced 15·1% (P < 0·001) with FXCO and 7·4% (P < 0·001) with HOCO. Total cholesterol (TC) was reduced 11% (P < 0·001) with FXCO and 3·5% (P = 0·002) with HOCO compared with WD. Endpoint TC differed between FXCO and HOCO (P < 0·05). FXCO consumption reduced HDL-cholesterol by 8·5% (P < 0·001) and LDL:HDL ratio by 7·5% (P = 0·008) v. WD. FXCO significantly decreased E-selectin concentration compared with WD (P = 0·02). No differences were observed in inflammatory markers after the consumption of HOCO compared with WD. In conclusion, consumption of novel HOCO alone or when blended with flaxseed oil is cardioprotective through lipid-lowering effects. The incorporation of flaxseed oil may also target inflammation by reducing plasma E-selectin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".