Premenopausal Plasma Osteoprotegerin and Breast Cancer Risk: A Case–Control Analysis Nested within the Nurses' Health Study II
Bibliographic record
Abstract
Abstract Background: Emerging evidence supports a role of the receptor activator of NF-κB (RANK) pathway in normal mammary gland development and breast carcinogenesis. Osteoprotegerin (OPG) is the endogenous decoy receptor for RANK-ligand (RANKL), which inhibits RANK-signaling. Whether OPG may be a biomarker of breast cancer risk remains unclear. Methods: We evaluated the association between plasma OPG and breast cancer risk in a case (n = 297)–control (n = 297) study nested within the Nurses' Health Study II. Cases were women who were cancer-free and premenopausal at blood collection who developed invasive breast cancer. OPG was quantified using an ELISA. Conditional logistic regression was used to estimate multivariable odds ratios (OR) and 95% confidence intervals (CI) for the association between OPG levels and breast cancer risk, adjusting for potential confounders. Unconditional logistic regression, additionally adjusting for matching factors, was used for stratified analyses. Results: Overall, there was no substantial evidence for an association between plasma OPG levels and breast cancer risk, although the point estimate for the highest (vs. lowest) quartile was below 1 (OR = 0.78; 95% CI, 0.46–1.33; Ptrend = 0.30). There was no evidence of heterogeneity by various reproductive, hormonal, or tumor characteristics, including hormone receptor status and grade (all Pheterogeneity ≥ 0.17). Conclusions: Findings from this prospective study do not provide substantial evidence for an association between circulating OPG and breast cancer risk among premenopausal women; however, we were underpowered in stratified analyses. Impact: Results do not provide strong evidence for OPG as a potential biomarker of breast cancer risk among 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".