Diet and Nutritional Factors in the Prevention and Treatment of COVID-19: An Umbrella Review
Bibliographic record
Abstract
Background: There is growing interest in the use of natural therapies for the prevention and treatment of COVID-19 and related illnesses. The aim of this review was to identify and examine the systematic and narrative reviews reporting on the relationship between diet or nutritional status and COVID-19. Methods: This paper is part of an umbrella review of studies related to natural health products and therapies for the prevention or treatment of COVID-19, as a follow-up to a live review that was conducted by the World Naturopathic Federation. PubMed and Google Scholar were searched for systematic and narrative reviews. Results: Seven narrative reviews and four systematic reviews were included. The reviews included evidence suggesting that dietary patterns and nutritional status are important modifiable risk factors relevant to the prevention and treatment of COVID-19. Three systematic reviews reported an association between poor nutritional status and greater COVID-19 severity or death. Narrative reviews suggested a possible benefit of the Mediterranean diet, fibre-rich diets, and antioxidant-rich fruits and vegetables. Conclusion: The research suggests that nutrition status is a significant factor in the progression of COVID-19 infection. While more clinical and interventional evidence is needed to precisely understand the impact of diet, dietary constituents, and nutritional status on modifying COVID-19 risk, the findings of this review highlight the importance of following existing dietary guidelines to support healthy immune function.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".