Delineating the relationship between circulating osteoprotegerin and bone health in women with a pathogenic variant in BRCA1: A cross-sectional analysis
Bibliographic record
Abstract
Purpose Osteoprotegerin (OPG) plays an important role in the inhibition of osteoclast formation and bone resorption . Studies have reported lower OPG levels among women with a pathogenic variant (mutation) in the BRCA1 gene, and thus, may be at greater risk for skeletal bone loss. Thus, we investigated the association between circulating OPG and two validated markers of bone health: 1) bone fracture risk score (FRAX) and 2) bone mineral density (BMD), among BRCA mutation carriers. Methods Women with a blood sample and clinical data were included in this analysis. An enzyme-linked immunosorbent assay (ELISA) was used to quantify serum OPG (pg/mL) and the 10-year risk of major osteoporotic fracture (FRAXmajor) and hip fracture (FRAXhip) (%) was estimated using a web-based algorithm. For a subset of women, lumbar spine BMD was previously assessed by dual x-ray absorptiometry (DXA)(T-score). A Mann–Whitney U test was used to evaluate the association between OPG and FRAX score, while linear regression was used to assess the association of OPG and BMD. Results Among 701 women with a BRCA1 mutation, there was a significant (and unexpected) positive association between OPG levels and FRAX score (FRAXmajor: 2.12 (low OPG) vs. 2.53 (high OPG) P < 0.0001; FRAXhip: 0.27 (low OPG) vs. 0.44 (high OPG) P < 0.0001). In a subset with BMD measurement ( n = 50), low serum OPG was associated with a significantly lower BMD T-score (−1.069 vs. -0.318; P = 0.04). Conclusion Our findings suggest that women with inherently lower OPG may be at risk of lower BMD, the gold standard marker of bone disease . Due to the young age of our cohort, on-going studies are warranted to re-evaluate the association between OPG and FRAX in BRCA mutation carriers.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".