Association of markers of insulin and glucose control with subsequent colorectal cancer risk.
Bibliographic record
Abstract
We evaluated the association of plasma insulin and other markers of insulin and glucose control with subsequent colorectal cancer. Incident colon (n = 132) and rectal (n = 41) cancer cases and matched controls (n = 346) were identified between baseline in 1989 and 2000 among participants in a community-based cohort in Washington County, Maryland. Circulating markers of insulin and glucose control were measured in baseline blood samples. Body mass index (BMI) and use of medications to treat diabetes mellitus were self-reported at baseline. Conditional logistic regression was used to estimate matched odds ratios (ORs). Compared with the lowest fourth, participants with insulin concentrations in the highest fourth were not at an increased risk of colorectal cancer [OR, 0.78; 95% confidence interval (CI), 0.45-1.35; P(trend) = 0.24]. Similarly, no associations were observed for the ratio of total cholesterol:HDL-cholesterol, triglycerides, and insulin-like growth factor binding protein 1. However, those in the highest fourth of glycosylated hemoglobin (HbA(1c)) level had a slightly increased risk of colorectal cancer (OR, 1.57; 95% CI, 0.94-2.60; P(trend) = 0.02). The OR of colorectal cancer was 1.70 (95% CI, 1.01-2.86; P(trend) = 0.08) comparing BMI >/=30 kg/m(2) to <25 kg/m(2). The OR of colorectal cancer was 2.43 (95% CI, 1.10-5.38) for the use of medications to treat diabetes. The associations of higher HbA(1c), higher BMI, and the use of medications to treat diabetes, with colorectal cancer lend support to the hypothesis that perturbations in insulin and glucose control may influence colorectal carcinogenesis. It is possible that HbA(1c), BMI, and the use of medications to treat diabetes, as a surrogate for protracted or severe type 2 diabetes mellitus, may have been better time-averaged indicators of hyperinsulinemia and hyperglycemia than the plasma markers that were measured once prediagnostically in a nonfasting population.
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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".