Abstract 035: Effects of Canola Oil and High Oleic Canola Oil-rich Diets on Abdominal Fat Mass in Individuals at Risk for Metabolic Syndrome
Bibliographic record
Abstract
Introduction: Dietary monounsaturated fatty acids (MUFA) decrease metabolic syndrome (MetS) risk factors. Therefore, novel vegetable oils high in MUFA may improve CVD risk in individuals with MetS. Objective: To evaluate the efficacy of five vegetable oil treatments including corn/safflower oil (69.3% LA, 17.6% MUFA), canola oil (62.8% MUFA, 29.3% PUFA: 19.5% LA, 10% ALA), high oleic canola oil (72% MUFA, 17% PUFA: 15% LA, 2% ALA), high oleic canola oil with DHA (63.8% MUFA, 13% LA, 6% DHA), and flax/safflower oil (69.4% PUFA: 37.5% LA, 32% ALA, 17.9% MUFA) on abdominal fat mass in subjects with central obesity plus at least one other risk factor for MetS. Methods: A multi-center, double blind, randomized, 5-period crossover, controlled feeding study was conducted to evaluate the effects of vegetable oils with different fatty acid profiles on abdominal fat mass in subjects with risk factors for MetS. Subjects (n=121: 62 women, 59 men) were fed an isocaloric heart healthy diet (50% CHO, 15% PRO, 35% FAT, Results: A mixed linear model demonstrated that canola and high oleic canola oils reduced abdominal fat mass by 1.6% (51.4 g) and 1.6% (50.1 g), respectively. These changes across treatments were significantly different from flax/safflower oil (p Conclusion: Canola oil and high oleic canola oil lowered abdominal fat mass compared to flax/safflower oil after 4 weeks in men and women with metabolic syndrome risk factors consuming an isocaloric diet. Further studies are needed to determine the mechanisms that account for the visceral fat loss in response to a high MUFA diet.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".