The effect of omega3 fatty acid supplementation on PPARγ and UCP2 expressions, resting energy expenditure, and appetite in athletes
Bibliographic record
Abstract
BACKGROUND: Omega3 fatty acids as a ligand of energy-related genes, have a role in metabolism, and energy expenditure. These effects are due to changes in the expression of peroxisome proliferator-activated receptor-gamma (PPARγ) and uncoupling protein2 (UCP2). This study evaluated the effect of omega3 supplements on PPARγ mRNA expression and UCP2 mRNA expression and protein levels, as regulators of energy metabolism, resting energy expenditure (REE), and appetite in athletes. METHODS: In a 3-week double-blind RCT in Tabriz, Iran, in 2019, 36 male athletes, age 21.86 (±3.15) y with 16.17 (±5.96)% body fat were randomized to either an intervention (2000 mg/day omega3; EPA: 360, DHA: 240) or placebo (2000 mg/day edible paraffin) groups. Appetite and REE were assessed before and after the intervention. PPARγ and UCP2 mRNA expression and UCP2 protein levels in blood were evaluated by standard methods. RESULTS: Results showed PPARγ mRNA levels, and UCP2 mRNA and protein levels increased in omega3 group (p < 0.05), as did REE (p < 0.05). Also, differences in the sensation of hunger or satiety were significant (p < 0.05). CONCLUSIONS: Our findings showed that omega3 supplementation leads to the up-regulation of PPARγ and UCP2 expressions as the indicators of metabolism in healthy athletes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".