lmpact of various dietary oils on expression levels of inflammatory genes: a randomized crossover controlled nutritional intervention (40.6)
Bibliographic record
Abstract
Consumption of a docosahexaenoic acid (DHA)‐enriched canola oil was shown to have anti‐inflammatory effects compared with a control diet rich in linoleic acid (LA) and a diet rich in flax oil and alpha‐linolenic acid (ALA). We further investigated the impact of these oils on the expression of key inflammatory genes in whole blood cells as part of the Canola Oil Multicenter Intervention Trial (COMIT). A total of 118 men and women with abdominal obesity and at least one other criterion for metabolic syndrome consumed 5 isoenergetic diets for 4 weeks each according to a randomized, crossover design. Each diet provided 60 g/3000 kcal of various experimental oils. Here we report the impact of 3 of these 5 oils: 1‐ CONTROL high LA corn/safflower oil (LA 41.6 g; ALA 0.2 g; n6/n3 ratio 208:1), 2‐ FLAX oil (LA 22.5 g; ALA 19.2 g; n6/n3 ratio 1.2:1), 3‐ High DHA canola (DHA‐CAN; LA 7.6 g; ALA 1.2 g; DHA 3.5 g; n6/n3 ratio 1.2:1). Inflammatory gene expression in whole blood cells at the end of each diet was assessed by real‐time PCR in a subset of 66 individuals. DHA‐CAN lowered mRNA expression of interleukin(IL)‐1β compared with CONTROL (fold change 0.90, P =0.03) and FLAX (fold change 0.89, P =0.03). There was no between‐diet difference in the expression of other inflammatory genes and transcription factors. These data suggest that anti‐inflammatory effects of DHA compared with n3 and n6 polyunsaturated fatty acid rich oils from plant sources occur at least in part through a reduction in the expression of IL‐1β. Grant Funding Source : Supported by the Canola Council of Canada, Flax 2015 and Agriculture and Agri‐Food 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".