Low dietary sodium potentially mediates COVID-19 prevention associated with whole-food plant-based diets
Bibliographic record
Abstract
Compared with an omnivorous Western diet, plant-based diets containing mostly fruits, vegetables, grains, legumes, nuts and seeds, with restricted amounts of foods of animal origin, are associated with reduced risk and severity of COVID-19. Additionally, inflammatory immune responses and severe acute respiratory symptoms of COVID-19, including pulmonary oedema, shortness of breath, fever and nasopharyngeal infections, are associated with Na toxicity from excessive dietary Na. High dietary Na is also associated with increased risks of diseases and conditions that are co-morbid with COVID-19, including chronic kidney disease, hypertension, stroke, diabetes and obesity. This article presents evidence that low dietary Na potentially mediates the association of plant-based diets with COVID-19 prevention. Processed meats and poultry injected with sodium chloride contribute considerable amounts of dietary Na in the Western diet, and the avoidance or reduction of these and other processed foods in whole-food plant-based (WFPB) diets could help lower overall dietary Na intake. Moreover, high amounts of K in plant-based diets increase urinary Na excretion, and preagricultural diets high in plant-based foods were estimated to contain much lower ratios of dietary Na to K compared with modern diets. Further research should investigate low Na in WFPB diets for protection against COVID-19 and co-morbid conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".