A Prospective Study of C-Peptide, Insulin-like Growth Factor-I, Insulin-like Growth Factor Binding Protein-1, and the Risk of Colorectal Cancer in Women
Bibliographic record
Abstract
Hyperinsulinemia, hyperglycemia, and elevated insulin-like growth factor (IGF)-1 levels have been implicated in the etiology of colorectal cancer. However, the joint effects of insulin and IGF-I have not been considered, and whether hyperinsulinemia or hyperglycemia is more etiologically relevant is unclear. IGF binding protein-1 (IGFBP-1) has been hypothesized to mediate the effects of insulin, but epidemiologic data on IGFBP-1 are sparse. We conducted a nested case-control study among the 32,826 women of the Nurses' Health Study who provided a blood sample in 1989 to 1990. After excluding diabetics, we confirmed 182 incident colorectal cancer cases over 10 years of follow-up and 350 controls. Cases were matched to two controls on year of birth, date of blood draw, and fasting status. C-peptide levels were weakly associated with risk of colon cancer [top quartile (Q4) versus bottom quartile (Q1): multivariable relative risk (MVRR), 1.76; 95% confidence interval (95% CI), 0.85-3.63]. Fasting IGFBP-1 was inversely associated with risk of colon cancer (MVRR, 0.28; 95% CI, 0.11-0.75). We observed no clear association between glycosylated hemoglobin and risk for colorectal cancer. The IGF-I to IGFBP-3 molar ratio was associated with colon cancer risk (MVRR, 2.82; 95% CI, 1.35-5.88), and women with low levels of both IGF-I/IGFBP-3 and C-peptide (or high IGFBP-1) were at low risk, and elevation of either was sufficient to increase risk. Although altering IGF-I levels may not be practical, the growing burden of obesity and consequently hyperinsulinemia, which seems increasingly important for colon cancer, may be a target for effective prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".