Correlates of Circulating Osteoprotegerin in Women with a Pathogenic or Likely Pathogenic Variant in the <i>BRCA1</i> Gene
Bibliographic record
Abstract
BACKGROUND: Lower levels of osteoprotegerin (OPG), the decoy receptor for receptor activator of NFκB (RANK)-ligand, have been reported among women with a BRCA1 mutation, suggesting OPG may be marker of cancer risk. Whether various reproductive, hormonal, or lifestyle factors impact OPG levels in these women is unknown. METHODS: BRCA1 mutation carriers enrolled in a longitudinal study, no history of cancer, and a serum sample for OPG quantification, were included. Exposure information was collected through self-reported questionnaire at study enrollment and every 2 years thereafter. Serum OPG levels (pg/mL) were measured using an ELISA, and generalized linear models were used to assess the associations between reproductive, hormonal, and lifestyle exposures at the time of blood collection with serum OPG. Adjusted means were estimated using the fully adjusted model. RESULTS: A total of 701 women with a median age at blood collection of 39.0 years (18.0-82.0) were included. Older age (Spearman r = 0.24; P < 0.001) and current versus never smoking (98.82 vs. 86.24 pg/mL; Pcat < 0.001) were associated with significantly higher OPG, whereas ever versus never coffee consumption was associated with significantly lower OPG (85.92 vs. 94.05 pg/mL; Pcat = 0.03). There were no other significant associations for other exposures (P ≥ 0.06). The evaluated factors accounted for 7.5% of the variability in OPG. CONCLUSIONS: OPG is minimally influenced by hormonal and lifestyle factors among BRCA1 mutation carriers. IMPACT: These findings suggest that circulating OPG levels are not impacted by non-genetic factors in high-risk women.
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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".