Consumption of a novel flaxseed and canola oil blend favourably improves serum lipid profiles in hyperlipidemic subjects
Bibliographic record
Abstract
The objective was to analyze the lipid‐lowering potential of diets rich in a flaxseed and canola oil blend (FCO) versus canola oil (CO) compared with an average Western dietary fat profile (WD). Hyperlipidemic subjects (n=36) consumed 3 treatment diets consecutively for 28 d separated by 4 wk washouts, using a randomized crossover design. Controlled diets fed under supervision contained 35% energy (En) as fat, 70% of which was provided by FCO, CO or WD. Dietary fat profile of SFA, MUFA, PUFA omega‐6, and PUFA omega‐3 was 6, 16, 4.5, 7.5% En for FCO; 6, 22, 5, 1% En for CO; and 11.5, 16, 6, 0.5% En for WD, respectively. After 28 d, compared with WD, LDL‐cholesterol was reduced by 14% with FCO (3.07 ± 0.12 mmol/L, P<0.001) and 11% with CO (3.17 ± 0.12 mmol/L; P<0.001). Total cholesterol (TC) was reduced by 10% with FCO (5.11 ± 0.13 mmol/L, P<0.001) and 6% with CO (5.34 ± 0.14 mmol/L; P<0.001) compared with WD. Endpoint TC also differed between FCO and CO (P<0.05). FCO consumption reduced both HDL‐cholesterol by 7% (1.28 ± 0.06 mmol/L; P<0.001) and LDL:HDL ratio by 5% (2.62 ± 0.17; P<0.05) compared with WD. Triglycerides were 10% lower after FCO (1.65 ± 0.14 mmol/L; P<0.05) and 11% lower after WD (1.63 ± 0.16 mmol/L; P<0.01) than after CO (1.84 mmol/L ± 0.18). It is concluded that consumption of a FCO blend provides effective lipid‐lowering beyond that of CO in hyperlipidemic subjects. Supported by Flax and Canola Councils of Canada and ARDI.
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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) | 0.001 | 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 | 0.000 | 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".