Using Genetic Proxies for Lifecourse Sun Exposure to Assess the Causal Relationship of Sun Exposure with Circulating Vitamin D and Prostate Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Ecological and epidemiological studies have identified an inverse association of intensity and duration of sunlight exposure with prostate cancer, which may be explained by a reduction in vitamin D synthesis. Pigmentation traits influence sun exposure and therefore may affect prostate cancer risk. Because observational studies are vulnerable to confounding and measurement error, we used Mendelian randomization to examine the relationship of sun exposure with both prostate cancer risk and the intermediate phenotype, plasma levels of vitamin D. METHODS: We created a tanning, a skin color, and a freckling score as combinations of single nucleotide polymorphisms that have been previously associated with these phenotypes. A higher score indicates propensity to burn, have a lighter skin color and freckles. The scores were tested for association with vitamin D levels (25-hydroxyvitamin-D and 1,25-dihydroxyvitamin-D) and prostate-specific antigen detected prostate cancer in 3,123 White British individuals enrolled in the Prostate Testing for cancer and Treatment (ProtecT) study. RESULTS: The freckling score was inversely associated with 25(OH)D levels [change in 25(OH)D per score unit -0.27; 95% CI, -0.52% to -0.01%], and the tanning score was positively associated with prostate cancer risk (OR = 1.05; 95% CI, 1.02-1.09), after adjustment for population stratification and potential confounders. CONCLUSIONS: Individuals who tend to burn are more likely to spend less time in the sun and consequently have lower plasma vitamin D levels and higher susceptibility to prostate cancer. IMPACT: The use of pigmentation-related genetic scores is valuable for the assessment of the potential benefits of sun exposure with respect to prostate cancer risk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".