Effect of Omega-3 Polyunsaturated Fatty Acids Intake on Eosinophil Airway Inflammation in University Athletes
Bibliographic record
Abstract
Background: Though athletes have a high risk of respiratory disorders, effective prevention has not yet to be identified. Omega-3 (n-3) polyunsaturated fatty acids (PUFA) have some practical anti-inflammatory effects in allergy, and therefore may reduce airway inflammation in athletes. This study aimed to assess whether n-3 PUFA intake affects airway inflammation in university athletes. Methods: Twenty-three males were divided into three groups: 1) the eosinophilic airway inflammation group (I_PUFA group; fractional exhaled nitric oxide (FeNO ≥ 25 ppb, n = 10); 2) the non-eosinophilic airway inflammation group (N_PUFA group; FeNO < 25 ppb, exhaled carbon dioxide (eCO) ≥ 3.6 ppm, n = 5); and 3) the control group (FeNO < 25 ppb, eCO < 3.6 ppm, n = 8). Participants took supplements containing 260 mg of docosapentaenoic acid and 600 mg of eicosapentaenoic acid (EPA) daily for 3 weeks. Baseline measurements of FeNO, respiratory impedance, respiratory function, dietary intake (food frequency questionnaires), and blood tests were performed. FeNO and respiratory impedance were measured weekly, and the rest were measured after 3 weeks. Results: There was a significant decrease in FeNO levels from baseline at 2 and 3 weeks in the I_PUFA group (54.7 ± 8.5 ppb vs. 45.1 ± 9.1 and 45.4 ± 7.7 ppb; mean ± standard error (SE), P < 0.05). After 3 weeks, FeNO levels remained unchanged in the N_PUFA and control groups, and respiratory impedance and function remained unchanged in all groups. Blood EPA levels significantly increased in the I_PUFA and N_PUFA groups (I_PUFA, 27.7 ± 16.9 vs. 52.1 ± 12.3 µg/mL; N_PUFA, 20.8 ± 8.7 vs. 70.4 ± 36.1 µg/mL; mean ± standard deviation (SD), P < 0.05). No changes were observed in dietary intake over the 3 weeks. Conclusions: n-3 PUFA supplementation for 3 weeks reduced airway inflammation in athletes with FeNO levels ≥ 25 ppb.
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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".