Associations of blood lipids and LDL cholesterol lowering drug-targets with colorectal cancer risk: a Mendelian randomisation study
Bibliographic record
Abstract
BACKGROUND: Whether blood lipids are causally associated with colorectal cancer (CRC) risk remains unclear. METHODS: Using two-sample Mendelian randomisation (MR), our study examined the associations of genetically-predicted blood concentrations of lipids and lipoproteins (primary: LDL-C, HDL-C, triglycerides, and total cholesterol), and genetically-proxied inhibition of HMGCR, NPC1L1, and PCSK9 (which mimic therapeutic effects of LDL-lowering drugs), with risks of CRC and its subsites. Genetic associations with lipids were obtained from the Global Lipids Genetics Consortium (n = 1,320,016), while genetic associations with CRC were obtained from the largest existing CRC consortium (n = 58,221 cases and 67,694 controls). Our main analysis was a multivariable MR (MVMR) with mutual adjustments for LDL-C, HDL-C, and triglycerides. Secondary analyses, including MVMR additionally-adjusting for BMI or diabetes, were also performed. RESULTS: Genetically-predicted LDL-C was positively associated with CRC risk in the MVMR adjusted for HDL-C and triglycerides (OR = 1.09; 95%CI 1.02-1.16 per SD increase) and additionally-adjusted for BMI (OR = 1.12; 95%CI 1.05-1.21) or diabetes (OR = 1.09; 95%CI 1.02-1.17). Associations were generally consistent across anatomical subsites. No clear evidence of association was found for other lipids, lipoproteins, or LDL-lowering drug-targets. CONCLUSIONS: We found evidence of a weak positive association between LDL-C and CRC that did not appear to be explained by potential pleiotropic pathways such as via HDL-C, triglycerides, BMI, or diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".