Bone Mineral Density and Breast Cancer Risk Factors among Premenopausal and Postmenopausal Women A Systematic Review
Abstract
BACKGROUND: Bone mineral density (BMD) is a lifetime marker of estrogen in a woman's body and has been associated with increased breast cancer risk. Nonetheless the actual association is still debatable. Furthermore, estrogen is very crucial in maintaining human bone density and gradually decreases over age. A systematic search was conducted to assess any association of BMD with breast cancer risk factors among premenopausal and postmenopausal women.
MATERIALS AND METHODS: Review identification was performed through databases searching on MEDLINE, CINAHL and SCOPUS and 19 qualified studies were elected. The keywords used were "bone mineral density", "breast cancer", and "breast density".
RESULTS: A total of 19 articles showed variation with the majority of the studies focused on postmenopausal and a few focused on premenopausal women. Overall there was no concensus on effects.
CONCLUSIONS: An enormous effort is being undertaken by researchers to prove that BMD might be one of the significant risk factors for breast cancer.
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Development of a crushable foam model for human trabecular bone
- Data-driven motion compensation in HR-pQCT.
- Modeling of structure, quality, and function in the orthodontic patient
- Exploring the mechanism of Heidihuang Pill in the treatment of osteoporosis based on network pharmacology, molecular docking, and experimental validation
- Effect of fabric on the accuracy of computed tomography-based finite element analyses of the vertebra
- Use of interventions involving virtual reality tasks during the climacteric: a systematic review
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.