Mechanism and efficacy of diacylglycerol on energy expenditure and body composition in overweight women
Bibliographic record
Abstract
Diacylglycerol (DAG) has the potential to control body weight by inducing energy expenditure (EE) and modifying body fat accumulation. Our objective was to examine the efficacy of DAG oil (Enova oil™) in comparison with control oil composed of sunflower oil, safflower oil and rapeseed oil in overweight women. Twenty‐six subjects consumed two treatment diets for 28 days separated by a 4‐week washout period according to a randomized crossover design. 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. DAG oil failed to induce (p>0.05, n=24) total energy expenditure or (p>0.05, n=24) fat oxidation compared with the control. DAG did not alter the lean (p>0.05, n=19) mass, at trunk (p>0.05, n=19), android (p>0.05, n=26) and gynoid (p>0.05, n=26) areas, but did reduce the accumulation of total body fat (p<0.05, n=19), at trunk (p<0.05, n=19), android (p<0.05, n=26), and gynoid (p<0.05, n=26) areas. We conclude that DAG oil does not increase total EE or EE from fat and lean mass, but effectively increases post meal EE from CHO, and reduces fat accumulation in overweight female individuals. 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".