Influence of diet in COVID-19 infection and severity risk: a systematic review
Bibliographic record
Abstract
Introduction: Introduction: the risk and/or prognosis of COVID-19, caused by the SARS-CoV-2 virus, have been related to chronic diseases such as obesity, diabetes mellitus, and cardiovascular diseases, with poor-quality diet being a predisposing factor for these diseases. Objective: to synthesize the scientific evidence on the effect of diet on the risk of SARS-CoV-2 infection and severe COVID-19. Methods: a systematic review was carried out following the PRISMA guidelines. The bibliographic search was made in the databases Web of Science, Scopus and Medline (through the PubMed search engine). Risk of bias analysis was performed using the Newcastle-Ottawa and Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies scales. Results: 14 studies were included. Good adherence to the Mediterranean diet was associated with a decreased risk of SARS-CoV-2 infection (OR = 0.44; 95 % CI, 0.22-0.88, for high versus low adherence, and significant ORs of 0.88 and 0.95 in studies that analyzed adherence quantitatively) but not with the severity of COVID-19. A plant-based diet also had a protective association against both COVID-19 infection and severity. Specifically, a high consumption of vegetables, legumes and cereals, and a low intake of dairy products and red meat showed a protective effect against infection and/or COVID-19 severity, depending on the study. Vitamin and probiotic supplements also lowered the risk of infection. Conclusion: the available evidence suggests that a healthy diet, based on a Mediterranean or plant-based diet, with moderate consumption of dairy and red meat, exerts a protective effect against COVID-19.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".