Omega-3 polyunsaturated fatty acids-enriched hen eggs consumption enhances microvascular reactivity in young healthy individuals
Bibliographic record
Abstract
The beneficial effect of omega-3 polyunsaturated fatty acids (PUFA) supplementation on the cardiovascular (CV) system is well supported in CV patients; however, the effect of the consumption of omega-3 PUFA-enriched functional food in healthy individuals is still not fully elucidated. This study aimed to determine the effect of the consumption of omega-3 PUFA-enriched hen eggs on the microvascular reactivity (primary outcome), blood pressure (BP), and serum lipid profile in young healthy individuals. The control group (N = 16) ate 3 ordinary hen eggs (277 mg of omega-3 PUFAs/day), and the OMEGA-3 group (N = 20) ate 3 omega-3 PUFA-enriched eggs containing 259 mg of omega-3 PUFAs/egg daily (α-linolenic acid (ALA), 167 mg/egg; eicosapentaenoic acid (EPA), 7 mg/egg; docosahexaenoic acid (DHA), 84 mg/egg) for 3 weeks (777 mg of omega-3 PUFA/day). Postocclusive reactive hyperemia (PORH) in skin microcirculation assessed by laser Doppler flowmetry, serum lipid profile, fasting blood glucose, high-sensitivity C-reactive protein (hsCRP), and arterial BP were measured in all subjects before and after the protocol. PORH was significantly enhanced, and triglycerides, hsCRP, and BP were significantly decreased in the OMEGA-3 group compared with baseline measurements, whereas there was no significant difference in the control group after the protocol when compared with baseline. To the best of our knowledge, this is the first study to demonstrate that consumption of a mixture of omega-3 PUFA (ALA + EPA + DHA), provided via enriched hen eggs, elicits changes in the microvascular reactivity, BP, and triglyceride level in healthy subjects that are associated with CV benefits, thus suggesting that daily consumption of omega-3 PUFA-enriched eggs in healthy individuals may potentially contribute to CV risk factor attenuation and disease prevention.
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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".