Genetically-predicted vitamin D status, ambient UVB during the pandemic and COVID-19 risk in UK Biobank: Mendelian Randomisation study
Bibliographic record
Abstract
Abstract A growing body of evidence shows that poor vitamin D status 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, and to explore potential causal effects. We used logistic regression to identify associations between different vitamin D variables (25-hydroxyvitamin D concentration (25-OHD), ambient UVB and genetically-predicted 25-OHD concentrations) and COVID-19 (risk of infection, hospitalisation and death) in 495,780 participants from UK Biobank. We subsequently performed a Mendelian Randomisation (MR) study to test if there was any causal effect. In total, 1,746 COVID-19 cases and 399 COVID-19 deaths occurred between March and June 2020. We found significant inverse associations between COVID-19 infection and 25-OHD in univariable models, but these associations were non-significant after adjustment for confounders. Ambient UVB was strongly and inversely associated with hospitalization and death. Although the main MR analysis showed that genetically-predicted vitamin D levels were not causally associated with COVID-19 risk, MR sensitivity analysis using weighted mode method indicated a potential causal effect (OR=0.72, 95% CI:0.53-0.98; P=0.041). In conclusion, our study found suggestive evidence of association between vitamin D and the risk or severity of COVID-19 but further studies are needed.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.000 |
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".