Bioelectrical impedance analysis of bone mineral content based on dual-energy X-ray absorptiometry: evaluation of...

Bone mineral content (BMC) is a crucial indicator of skeletal health, influencing growth and development in younger individuals and fracture risk in older adults. This study aimed to evaluate the consistency of bioelectrical impedance analysis (BIA) compared with dual-energy X-ray absorptiometry (DXA) for assessing BMC in healthy populations. Additionally, it explored the potential to improve prediction accuracy by optimizing regression equations tailored to specific age groups, providing valuable insights for skeletal health monitoring and personalized healthcare. A total of 302 healthy Korean participants (148 men and 154 women; mean ages 24.87 ± 12.43 and 34.98 ± 22.24 years, respectively) underwent body composition measurements via BIA and DXA. Basic variables such as age, height, and weight, along with a range of BIA parameters, were utilized to refine predictive models for BMC. Age-specific regression models significantly enhanced prediction accuracy, with the adjusted R² reaching up to 0.90. The mean difference between the optimized BIA model and DXA was - 0.02 kg (p = 0.287), indicating negligible paired differences. In contrast, existing BIA equations exhibited substantial bias (mean difference up to 0.46 kg, p < 0.001). Age-related factors, particularly in older adults, likely contributed to the decline in predictive performance, with extracellular water (ECW) and total body water (TBW) identified as key variables in this subgroup. When using subject-specific equations, BIA demonstrated superior predictive capability for BMC compared to generalized equations. However, limitations include reliance on a single BIA device, lack of external validation, and an unbalanced age distribution, which may affect the generalizability of the findings. While DXA remains the gold standard, integrating BIA with optimized equations offers a portable and cost-effective alternative for skeletal health assessment and long-term monitoring in community settings. This study underscores the importance of developing BMC models tailored to specific populations to advance the precision and applicability of BIA methodologies across diverse age groups and demographic cohorts.