Regulation of immune function in healthy adults: one-stop guide on the role of dietary fatty acids, gut microbiota-derived short chain fatty acids, and select micronutrients in combination with physical activity
Bibliographic record
Abstract
The immune system requires an adequate supply of nutrients, although current dietary recommendations may not account for optimal immune function in healthy adults. Nutrient inadequacies due to the growing influence of the western diet pose a risk for immune dysfunction. This review aims to determine the beneficial effects of supplementing dietary fats, nutrients that modulate gut microbiota, and specific micronutrients on systemic immune functions (concentrations of plasma cytokines, antibodies, and acute phase proteins) during health and acute inflammatory conditions, including COVID-19. We discussed micronutrients (selenium, zinc, and vitamin D) with compelling evidence supporting immunomodulatory properties. Additionally, the synergistic effects of physical activity and dietary interventions on systemic immune markers are explored. Briefly, evidence suggests that dietary consumption of monounsaturated (oleic and palmitoleic acids) and omega-3 polyunsaturated fatty acids (eicosapentaenoic and docosahexaenoic acids) promotes anti-inflammatory properties. Food sources (fiber, prebiotics, probiotics, omega-3) and patterns (Mediterranean diet) increase the production of short-chain fatty acids, beneficially altering gut microbiota composition, which subsequently enhances the immunomodulatory properties of circulating immune cells. A positive synergistic role of nutrient supplementation (omega-3 and fiber) and physical activity on circulating C-reactive protein and interleukin-6 levels has been observed. Lastly, omega-3 supplementation during COVID-19 infection may reduce circulating C-reactive protein and pro-inflammatory cytokines and improves pain and fatigue symptoms. This review highlights recent findings that support the beneficial role of specific nutrients in promoting systemic immune function in healthy adults. However, to establish specific dietary recommendations to support optimal immune function, more research is required. Key takeaway: Increasing dietary fats (fish and olive oils) and specific micronutrients may positively impact systemic immune function in healthy adults. Evidence suggests that these nutrients promote immunomodulatory properties useful in resolving acute infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".