The Immunomodulatory Effects of a 6-Month Extra Virgin Olive Oil Intervention on Monocyte Cytokine Secretion and Plasma Cytokine Levels in Dyslipidemic and Post-Infarct Patients: A Clinical Pilot Study
Bibliographic record
Abstract
Atherosclerosis is an immuno-inflammatory process underlying cardiovascular diseases. One of the main actors of this inflammation is monocytes, with the switch in their phenotypes and irregularities in their cytokine production. OBJECTIVE: This study was aimed to investigate the nutraceutical potential of extra virgin olive oil (EVOO) on the inflammatory status of monocytes in participants showing different levels of cardiovascular risk. METHODS: 43 participants 65-85 years old were recruited including 14 healthy, 12 dyslipidemic patients with hypercholesterolemia recently diagnosed, and 17 post-infarct patients. Participants from all groups were supplemented with EVOO (25 mL/day) for 6 months. IL-1β, IL-6, IL-10, TNF-α cytokine production, and monocyte phenotypes were investigated both at quiescent and at stimulated state by flow cytometry. RESULTS: At the baseline (pre-intervention), dyslipidemic patients, compared to healthy and post-infarct participants, showed monocytes in an inflammatory state characterized by a significantly weaker IL-10 production. Our results do not show a significant modulation of the phenotype or IL-10, IL-6, and TNF-α production following a 6-month EVOO intake whether at quiescence or under stimulation. However, IL-1β is significantly increased by the intervention of EVOO in post-infarct patients. Paradoxically after the 6-month intervention, monocytes from dyslipidemic patients showed a significantly decreased secretion of IL-1β under LPS stimulation despite the increase observed at basal state. CONCLUSION: Our results show that 6-month EVOO intervention did not induce a monocyte phenotypic change or that this intervention significantly modifies cytokine production.
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 | 0.000 |
| 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 | 0.001 |
| 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".