Exploring Metabolomic Drivers of Colorectal and Gastric Cancer: A Mendelian Randomisation Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the causal relationship between 1,400 metabolites and colorectal and gastric cancer. STUDY DESIGN: Mendelian randomisation study. Place and Duration of the Study: The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China, from July to August 2024. METHODOLOGY: Metabolite genome-wide association study (GWAS) data and genetic data from the Canadian Longitudinal Study on Aging (CLSA) as well as the expensive FinnGen project, respectively, were sourced. Suitable instrumental variables were chosen based on their association with metabolites at a genome-wide significance level, thus ensuring a high degree of reliability in the causal inferences drawn. Inverse variance weighting (IVW) was used for initial analysis. Sensitivity analyses were conducted using MR Egger regression and weighted median methods to validate findings and assess potential pleiotropy or bias. RESULTS: Metabolites were included in the study of 8,299 individuals. Gastric cancer included 1,307 cases and 287,137 controls; while colorectal cancer included 6,509 cases and 287,137 controls. The research identified sixty-nine metabolites associated with varying degrees of risk enhancement or mitigation. For gastric cancer, a more focused discovery highlighted two metabolites with significant causal links-associated with increased risk as well as a protective effect. Sensitivity analyses confirmed the validity of these findings. CONCLUSION: By elucidating specific metabolites that exert direct causal effects on colorectal and gastric cancer risk, the study marked a significant advancement in the understanding of the metabolic pathways involved in cancer development. KEY WORDS: Mendelian randomisation, Colorectal cancer, Gastric cancer, Metabolites, Genetic variants, Genome-wide association studies, Causal inference.
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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 | 0.001 | 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".