Probing the diabetes and colorectal cancer relationship using gene – environment interaction analyses
Bibliographic record
Abstract
Abstract Diabetes is an established risk factor for colorectal cancer; however, the mechanisms underlying this relationship are not fully understood and the role of genetic variation is unclear. We used data from 3 genetic consortia (CCFR, CORECT, GECCO; 31,318 colorectal cancer cases/41,499 controls) and undertook genome-wide gene-environment interaction analyses with colorectal cancer risk, including interaction tests of genetics(G)xdiabetes and joint testing of Gxdiabetes, G-colorectal cancer association and/or G-diabetes correlation (2,3-degrees of freedom joint tests; d.f.). Based on the joint tests, variant rs3802177 in SLC30A8 (p-value 3-d.f .:5.46×10 −11 ; regulates phosphorylation of the insulin receptor and phosphatidylinositol-3 kinase activity) and rs9526201 in LRCH1 (p-value 2-d.f .:7.84×10 −09 ; regulates T cell migration and Natural Killer Cell cytotoxicity) were associated with colorectal cancer. These results suggest that variation in genes related to insulin signalling and immune function may modify the association of diabetes with colorectal cancer and provide novel insights into the biology underlying the diabetes and colorectal cancer relationship.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".