Circulating Insulin and C-Peptide Levels and Risk of Breast Cancer among Predominately Premenopausal Women
Bibliographic record
Abstract
Insulin and insulin resistance have been hypothesized to increase the risk of breast cancer as insulin increases breast cell proliferation and inhibits sex hormone binding globulin. Although insulin is directly related to body weight, adiposity is inversely associated with breast cancer risk in premenopausal women but directly related to risk in postmenopausal women. To explore the association between insulin and c-peptide levels and breast cancer risk, we conducted a nested case-control study of predominantly premenopausal women within the Nurses' Health Study II cohort. From 1996 to 1999, blood samples were collected from 29,611 participants. A total of 317 cases were diagnosed after blood collection and before June 2003 and matched to 634 controls; 75% of these women were premenopausal at blood collection. Logistic regression models, controlling for breast cancer risk factors, were used to calculate relative risks (RR) and 95% confidence intervals (95% CI). Among women with fasting blood samples (n = 211 cases), insulin was suggestively inversely associated with breast cancer risk (highest versus lowest quartile: RR, 0.5; 95% CI, 0.3-1.0; P(trend) = 0.06). Among all women, c-peptide was not associated with breast cancer risk (highest versus lowest quartile: RR, 1.1; 95% CI, 0.7-1.7; P(trend) = 0.79); results were similar among fasting samples. These associations did not differ by age, body mass index, or waist-to-hip ratio. Overall, higher levels of insulin and c-peptide were not associated with a higher risk of breast cancer among predominantly premenopausal women.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".