Prediagnostic Plasma C-Peptide and Pancreatic Cancer Risk in Men and Women
Bibliographic record
Abstract
BACKGROUND: Hyperinsulinemia and insulin resistance have been proposed as underlying mechanisms for the increase in pancreatic cancer among long-standing diabetics and obese individuals. An association between serum insulin levels and pancreatic cancer risk was reported in a recent study, but the population was composed of heavy smokers and their findings may not be generalizable to nonsmokers. METHODS: Pancreatic cancer cases and matched controls were obtained from four large-scale prospective cohorts to examine the association between prediagnostic plasma levels of C-peptide and insulin and pancreatic cancer. One hundred ninety-seven pancreatic cancer cases were diagnosed during a maximum of 20 years of follow-up, after excluding cases diagnosed within 2 years of blood collection or with baseline diabetes. We estimated OR and confidence intervals (CI) using conditional logistic regression with adjustment for pancreatic cancer risk factors. RESULTS: Prediagnostic plasma C-peptide was positively associated with pancreatic cancer risk (OR, 1.52; 95% CI, 0.87-2.64, highest compared with the lowest quartile, P(trend) = 0.005). The association was not modified by body mass index or physical activity but seemed to be slightly stronger among never smokers than ever smokers. Fasting C-peptide and insulin were not related to pancreatic cancer; however, we observed a strong linear association for nonfasting C-peptide and pancreatic cancer (OR, 4.24; 95% CI, 1.30-13.8, highest versus lowest quartile, P(trend) < 0.001). CONCLUSIONS: Based on our finding of a strong positive association with nonfasting C-peptide levels, we propose that insulin levels in the postprandial state may be the relevant exposure for pancreatic carcinogenesis; however, other studies will need to examine this possibility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".