Serum 25-hydroxyvitamin D levels and risk of lung cancer and histologic types: a Mendelian randomisation analysis of the HUNT study
Bibliographic record
Abstract
We aimed to investigate potential causal associations between serum 25-hydroxyvitamin D (25(OH)D) levels and incidence of lung cancer overall and histologic types. We performed a Mendelian randomisation analysis using a prospective cohort study in Norway, including 54 580 individuals and 676 incident lung cancer cases. A 25(OH)D allele score was generated based on the vitamin D-increasing alleles rs2282679, rs12785878 and rs10741657. Hazard ratios with 95% confidence intervals for incidence of lung cancer and histologic types were estimated in relation to the allele score. The inverse-variance weighted method using summarised data of individual single nucleotide polymorphisms was applied to calculate the Mendelian randomisation estimates. The allele score accounted for 3.4% of the variation in serum 25(OH)D levels. There was no association between the allele score and lung cancer incidence overall, with HR 0.99 (95% CI 0.93–1.06) per allele score. A 25 nmol·L−1increase in genetically determined 25(OH)D level was not associated with the incidence of lung cancer overall (Mendelian randomisation estimate HR 0.96, 95% CI 0.54–1.69) or any histologic type. Mendelian randomisation analysis did not suggest a causal association between 25(OH)D levels and risk of lung cancer overall or histologic types in this population-based cohort study.
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".