A Prospective Evaluation of Insulin and Insulin-like Growth Factor-I as Risk Factors for Endometrial Cancer
Bibliographic record
Abstract
Obesity is a major risk factor for endometrial cancer, a relationship thought to be largely explained by the prevalence of high estrogen levels in obese women. Obesity is also associated with high levels of insulin, a known mitogen. However, no prospective studies have directly assessed whether insulin and/or insulin-like growth factor-I (IGF-I), a related hormone, are associated with endometrial cancer while accounting for estrogen levels. We therefore conducted a case-cohort study of incident endometrial cancer in the Women's Health Initiative Observational Study, a prospective cohort of 93,676 postmenopausal women. The study involved all 250 incident cases and a random subcohort of 465 subjects for comparison. Insulin, total IGF-I, free IGF-I, IGF-binding protein-3, glucose, and estradiol levels were measured in fasting baseline serum specimens. Cox models were used to estimate associations with endometrial cancer, particularly endometrioid adenocarcinomas, the main histologic type (n = 205). Our data showed that insulin levels were positively associated with endometrioid adenocarcinoma [hazard ratio contrasting highest versus lowest quartile (HR(q4-q1)), 2.33; 95% confidence interval (95% CI), 1.13-4.82] among women not using hormone therapy after adjustment for age and estradiol. Free IGF-I was inversely associated with endometrioid adenocarcinoma (HR(q4-q1), 0.53; 95% CI, 0.31-0.90) after adjustment for age, hormone therapy use, and estradiol. Both of these associations were stronger among overweight/obese women, especially the association between insulin and endometrioid adenocarcinoma (HR(q4-q1), 4.30; 95% CI, 1.62-11.43). These data indicate that hyperinsulinemia may represent a risk factor for endometrioid adenocarcinoma that is independent of estradiol. Free IGF-I levels were inversely associated with endometrioid adenocarcinoma, consistent with prior cross-sectional data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".