Type 2 diabetes mellitus, blood cholesterol, triglyceride and colorectal cancer risk in Lynch syndrome
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus and high total cholesterol and triglycerides are known to be associated with increased colorectal cancer risk for the general population. These associations are unknown for people with a germline DNA mismatch repair gene mutation (Lynch syndrome), who are at high risk of colorectal cancer. METHODS: This study included 2023 (56.4% female) carriers with a mismatch repair gene mutation (737 in MLH1, 928 in MSH2, 230 in MSH6, 106 in PMS2, 22 in EPCAM) recruited by the Colon Cancer Family Registry between 1998 and 2012. Weighted Cox regression was used to estimate the hazard ratios (HR) and 95% confidence intervals (CI) for the associations between self-reported type 2 diabetes, high cholesterol, triglyceride and colorectal cancer risk. RESULTS: Overall, 802 carriers were diagnosed with colorectal cancer at a median age of 42 years. A higher risk of colorectal cancer was observed in those with self-reported type-2 diabetes (HR 1.92; 95% CI, 1.03-3.58) and high cholesterol (HR 1.76; CI 1.23-2.52) compared with those without these conditions. There was no evidence of high triglyceride being associated with colorectal cancer risk. CONCLUSION: For people with Lynch syndrome, self-reported type-2 diabetes mellitus and high cholesterol were associated with increased colorectal 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".