Bidirectional Mendelian randomisation analysis of the relationship between circulating vitamin D concentration and colorectal cancer risk
Bibliographic record
Abstract
Epidemiological evidence is consistent with a protective effect of vitamin D against colorectal cancer (CRC), but the observed strong associations are open to confounders and potential reverse causation. Previous Mendelian randomisation (MR) studies were limited by poor genetic instruments and inadequate statistical power. Moreover, whether genetically higher CRC risk can influence vitamin D level, namely the reverse causation, still remains unknown. Herein, we report the first bidirectional MR study. We employed 110 newly identified genetic variants as proxies for vitamin D to obtain unconfounded effect estimates on CRC risk in 26 397 CRC cases and 41 481 controls of European ancestry. To test for reserve causation, we estimated effects of 115 CRC-risk variants on vitamin D level among 417 580 participants from the UK Biobank. The causal association was estimated using the random-effect inverse-variance weighted (IVW) method. We found no significant causal effect of vitamin D on CRC risk [IVW estimate odds ratio: 0.97, 95% confidence interval (CI) = 0.88-1.07, P = .565]. Similarly, no significant reverse causal association was identified between genetically increased CRC risk and vitamin D levels (IVW estimate β: -0.002, 95% CI = -0.008 to 0.004, P = .543). Stratified analysis by tumour sites did not identify significant causal associations in either direction between vitamin D and colon or rectal cancer. Despite the improved statistical power of this study, we found no evidence of causal association of either direction between circulating vitamin D and CRC risk. Significant associations reported by observational studies may be primarily driven by unidentified confounders.
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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 | 0.070 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".