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 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 | 0.005 |
| 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) | 0.002 | 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".