Circulating Insulin-Like Growth Factor Binding Protein-1 and the Risk of Pancreatic Cancer
Bibliographic record
Abstract
Insulin-like growth factor (IGF)-I has growth-promoting effects on pancreatic cancer cells, and elevated fasting serum insulin has been linked to pancreatic cancer risk. IGF binding protein-1 (IGFBP-1) is a downstream target of insulin and inhibits IGF-I activity. To investigate whether prediagnostic plasma levels of IGFBP-1 are associated with pancreatic cancer risk, we did a prospective, case-control study nested within the Health Professionals Follow-up Study, the Nurses' Health Study, the Physicians' Health Study, and the Women's Health Initiative. We assayed circulating IGFBP-1 among 144 pancreatic cancer cases that occurred >or=4 years after plasma collection and in 429 controls, matched for date of birth, prospective cohort, smoking status, and fasting status. When compared with participants in the three highest quartiles of plasma IGFBP-1, those in the lowest quartile experienced a relative risk (RR) for pancreatic cancer of 2.07 [95% confidence intervals (95% CI), 1.26-3.39], after adjusting for other risk factors, including circulating IGF-I, IGF binding protein-3, and C-peptide. Only participants in the lowest quartile of plasma IGFBP-1 showed an elevated risk of pancreatic cancer. The influence of low plasma IGFBP-1 became progressively stronger with time; among cases diagnosed >or=8 years after blood collection, the adjusted RR was 3.47 (95% CI, 1.48-8.14), comparing the bottom versus the top three quartiles. The influence of plasma IGFBP-1 was most marked among participants who never smoked cigarettes (RR, 3.30; 95% CI, 1.48-7.35). Among participants in four U.S. prospective cohort studies, low plasma IGFBP-1 levels significantly predicted an increased risk of pancreatic cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".