Levels of C-Peptide and Mammographic Breast Density
Bibliographic record
Abstract
Members of the insulin-like growth factor family have been associated with breast cancer risk and mammographic breast density, one of the strongest known breast cancer risk indicators. The aim of this cross-sectional study was to examine the association of levels of C-peptide (a marker of insulin secretion) with mammographic breast density among 1,499 healthy women recruited during screening mammography examinations. At time of mammography, blood samples and time since last meal were collected. Plasma C-peptide levels were measured by ELISA method, and mammographic breast density by a computer-assisted method. Spearman's partial correlation coefficients, adjusting for age and time since last meal (when necessary), were used to evaluate the associations. High body mass index and waist-to-hip ratio measurements were independently correlated with high levels of C-peptide (r(s) = 0.173 and r(s) = 0.252, respectively; P < 0.0001) or low breast density (r(s) = -0.389 and r(s) = -0.142, respectively; P < 0.0001). High levels of C-peptide were correlated with low breast density (r(s) = -0.210, P < 0.0001). However, the strength of the negative correlation was substantially reduced and was no longer significant after further adjustment for body mass index and waist-to-hip ratio (r(s) = -0.022, P = 0.41). These results suggest that C-peptide levels are not associated with breast density after complete adjustment for adiposity. Thus, the insulin/C-peptide-breast density relation does not seem to mirror the insulin/C-peptide-breast cancer association.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".