Prediction Model for Low Bone Mineral Density in Cancer Survivors and Age-Matched Controls Using a Causal Bayesian Network: A Nationwide Population-Based Study in Korea.

Background: With advances in cancer treatment, the number of cancer survivors has increased, bringing attention to long-term complications such as alterations in bone mineral density (BMD). Although survivors are at elevated risk for low BMD, prior studies have focused on specific cancer types and relied on traditional regression models, which are limited in capturing complex inter-variable relationships. This study aimed to examine the causal relationships among factors affecting BMD in cancer survivors and age-matched controls using causal Bayesian network (CBN) modeling. Methods: Data from the 2010-2011 Korea National Health and Nutrition Examination Survey (KNHANES) V were analyzed. We included 227 cancer survivors and 681 age- and sex-matched controls. Associations between BMD and variables such as age, sex, body composition, smoking, fracture history, and vitamin D were assessed using linear regression. A CBN model was then applied to evaluate probabilistic dependencies and potential causal relationships between variables and femoral neck BMD. Results: Among all participants, age, sex, smoking, fracture history, body fat percentage, muscle mass, and cancer history were significantly associated with femoral neck BMD. In cancer survivors, age (β = -0.032, Conclusion: Causal Bayesian network modeling identified muscle mass as a key modifiable factor influencing BMD among cancer survivors. These findings highlight the importance of muscle-preserving lifestyle interventions, including resistance exercise and adequate protein intake, in survivorship care. The CBN approach provides a framework for identifying individualized risk pathways and can support personalized bone-health management strategies in clinical practice.