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Record W4282976408 · doi:10.1158/1538-7445.am2022-5896

Abstract 5896: Reproductive, hormonal and lifestyle correlates of circulating osteoprotegerin levels in women with a <i>BRCA1</i> mutation

2022· article· en· W4282976408 on OpenAlexaff
Sarah Park, Tasnim Zaman, Shana J. Kim, Jennifer D. Brooks, Andy Kin On Wong, Jan Lubiński, Steven A. Narod, Leonardo Salmena, Joanne Kotsopoulos

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsOsteoprotegerinMedicineBreast cancerOncologyInternal medicineBiomarkerHormoneCancerFamily historyPhysiologyEndocrinologyReceptorActivator (genetics)BiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Purpose: BRCA1 mutation carriers face a very high lifetime risk of developing breast cancer. Dysregulation of the receptor activator of nuclear factor κB (RANK) pathway has been implicated in the pathogenesis of BRCA1-associated breast cancer. In addition, lower levels of osteoprotegerin (OPG), the decoy receptor for RANK-ligand, have been reported among women with a BRCA1 mutation and may be associated with an increased risk of breast cancer. Thus, inhibition of RANK signaling represents a potential target for prevention, and furthermore, OPG levels may be a biomarker of subsequent cancer risk. Whether non-genetic exposures also influence circulating levels of OPG in these high-risk women is not known. Therefore, the goal of this study was to evaluate reproductive, hormonal, and lifestyle correlates of OPG levels in BRCA1 mutation carriers. Methods: Eligible women included BRCA1 mutation carriers enrolled in a longitudinal study, aged 18 years or older, without a history of cancer, and with a serum sample available. All women completed a baseline questionnaire at the time of enrolment and a follow-up questionnaire every two years thereafter to collect detailed information on various exposures (i.e., reproductive, hormonal, and lifestyle) and outcomes. Serum OPG levels (pg/ml) were measured using an enzyme-linked immunosorbent assay (ELISA). Generalized linear models were used to evaluate the associations of various reproductive, hormonal, and lifestyle exposures at the time of blood collection with serum OPG and to estimate adjusted means. Results: A total of 828 women were included in the current analysis. Older age was associated with significantly higher OPG levels (<50 vs. >60 years, 79.04 vs. 82.01 pg/ml; Ptrend < 0.0001). Current vs. never smoking was also associated with significantly higher OPG levels (87.69 vs. 73.48 pg/ml; Pcat < 0.0001). There were no significant associations between other exposures and levels of OPG (P ≥ 0.17). Findings were similar in the analyses stratified by menopausal status. Conclusion: The results from this study suggest that non-genetic factors likely have a minimal impact upon serum OPG among women with a BRCA1 mutation. Further studies are needed to explore other potential correlates of OPG (i.e., diet, supplement use) and to elucidate whether integration of circulating OPG levels may improve existing risk prediction models. Citation Format: Sarah S. Park, Tasnim Zaman, Shana J. Kim, Jennifer D. Brooks, Andy K. Wong, Jan Lubiński, Steven A. Narod, Leonardo Salmena, Joanne Kotsopoulos. Reproductive, hormonal and lifestyle correlates of circulating osteoprotegerin levels in women with a BRCA1 mutation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5896.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.068
GPT teacher head0.383
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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