An observational and Mendelian randomisation study on vitamin D and COVID-19 risk in UK Biobank
Bibliographic record
Abstract
A growing body of evidence suggests that vitamin D deficiency has been associated with an increased susceptibility to viral and bacterial respiratory infections. In this study, we aimed to examine the association between vitamin D and COVID-19 risk and outcomes. We used logistic regression to identify associations between vitamin D variables and COVID-19 (risk of infection, hospitalisation and death) in 417,342 participants from UK Biobank. We subsequently performed a Mendelian Randomisation (MR) study to look for evidence of a causal effect. In total, 1746 COVID-19 cases (399 deaths) were registered between March and June 2020. We found no significant associations between COVID-19 infection risk and measured 25-OHD levels after adjusted for covariates, but this finding is limited by the fact that the vitamin D levels were measured on average 11 years before the pandemic. Ambient UVB was strongly and inversely associated with COVID-19 hospitalization and death overall and consistently after stratification by BMI and ethnicity. We also observed an interaction that suggested greater protective effect of genetically-predicted vitamin D levels when ambient UVB radiation is stronger. The main MR analysis did not show that genetically-predicted vitamin D levels are causally associated with COVID-19 risk (OR = 0.77, 95% CI 0.55-1.11, P = 0.160), but MR sensitivity analyses indicated a potential causal effect (weighted mode MR: OR = 0.72, 95% CI 0.55-0.95, P = 0.021; weighted median MR: OR = 0.61, 95% CI 0.42-0.92, P = 0.016). Analysis of MR-PRESSO did not find outliers for any instrumental variables and suggested a potential causal effect (OR = 0.80, 95% CI 0.66-0.98, p-val = 0.030). In conclusion, the effect of vitamin D levels on the risk or severity of COVID-19 remains controversial, further studies are needed to validate vitamin D supplementation as a means of protecting against worsened 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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".