Effect of diacylglycerol (Enova oil™) consumption on serum lipid profiles in overweight hypertriglyceridemic women
Bibliographic record
Abstract
Diacylglycerol (DAG) oil, a potential weight‐control agent, may also lower serum lipids and reduce the risk of cardiovascular disease. Our objective was to investigate the effects of DAG (Enova oil™) on serum lipid levels in overweight hypertriglyceridemic women compared with control oil, composed of sunflower, safflower and rapeseed oils. Using a randomized crossover design, 26 subjects consumed two treatment diets, each lasting 28 days and separated by 4‐week washout periods. Twenty grams of either DAG or control oil were consumed in the morning under supervision with an additional 20 grams of either oil provided for lunch and/or supper. No significant changes were observed in the serum lipid levels between the two treatments. Total cholesterol was not affected (p=0.4619) by DAG treatment (5.11±0.90mmol/L) compared to control group (5.04±0.92mmol/L). TG showed a non‐significant (p=0.4928) change between DAG group (1.664±0.69mmol/L) and control (1.59±0.66mmol/L). HDL was not altered (p=0.6252) by DAG consumption (1.32±0.34mmol/L, DAG vs 1.31±0.30mmol/L, control), nor were LDL levels affected (p=0.6388) by DAG consumption (3.45±0.78mmol/L) compared with control (3.41±0.84mmol/L). Results suggest that consumption of DAG oil has no significant effect on serum lipids when compared with conventional oils. Supported by Heart and Stroke Foundation of Canada.
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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 | 0.000 | 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 | 0.000 |
| 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".