Plasma C-Peptide, Mammographic Breast Density, and Risk of Invasive Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Insulin may promote breast cancer directly by stimulating the insulin receptor or indirectly by increasing the plasma concentration of active sex hormones. The association between insulin and breast density, a strong breast cancer risk factor, has not been thoroughly studied. We measured associations between c-peptide (a molar marker of insulin secretion), breast cancer risk, and breast density measurements in case-control studies nested within the Nurses' Health Study and Nurses' Health Study II cohorts. METHODS: Breast cancer associations were estimated with multivariate logistic regression models and then pooled across cohorts (total n = 1,084 cases and 1,785 controls). Mammographic density associations (percent dense area, dense area, and nondense area) were estimated as the difference in least-square means of the density parameters between quartiles of c-peptide concentration in all breast cancer controls with available screening mammography films (n = 1,469). RESULTS: After adjustment for adiposity, c-peptide was not associated with any measure of breast density. However, c-peptide was associated with an approximately 50% increased risk of invasive breast cancer [top vs. bottom quartile, adjusted OR = 1.5, 95% confidence interval (CI), 1.1-2.0] that was robust to adjustment for plasma-free estradiol and sex hormone-binding globulin. The association was stronger for ER-negative disease (adjusted OR = 2.0; 95% CI, 1.2-3.6). CONCLUSIONS: Our data suggest a positive association between hyperinsulinemia and breast cancer risk that occurs through nonestrogenic mechanisms, and that is not mediated by breast density. IMPACT: Primary prevention of breast cancer in women with hyperinsulinemia may be possible by targeting insulin signaling pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".