Journal of Bone and Mineral Metabolism

, Volume 27, Issue 3, pp 372–378

Bioimpedance: can its addition to simple clinical criteria enhance the diagnosis of osteoporosis?

  • Heidi H. Y. Ngai
  • Ching-Lung Cheung
  • Tzy-Jyun Yao
  • Annie W. C. Kung
Original Article


There is a great need for a simple means to identify individuals at risk of osteoporosis. Because bioimpedance (BI) estimates body composition, which is highly related to bone mineral density (BMD), we aimed to define the usefulness of BI to assess BMD. The relationships between BI and BMD were quantified using partial correlations. Multiple linear regression with a forward selection method was used to examine the predictive abilities of various body measurements on BMD at lumbar spine, femoral neck, and total hip. The abilities of BI to discriminate low BMD or to discriminate osteoporosis were evaluated using receiver operating characteristic (ROC) curve analysis. The relationships between BI and BMD at the spine and hip were evaluated in 345 Southern Chinese postmenopausal women and 390 men. After adjusting for age and weight, BI was inversely associated with BMD in both sexes (r = −0.053 to −0.195). Multiple linear regression analysis revealed that BI is a significant independent predictor of BMD in men. This finding was not confirmed in women. The area under the ROC curves (AUC) for BI as a single predictor to diagnose osteoporosis was 0.658 and 0.655 in women and men, respectively. The AUC was improved slightly with the addition of BI in the model that consisted of age and weight alone. Although BI was significantly associated with BMD, addition of BI did not enhance the ability to diagnose osteoporosis significantly compared with simple clinical criteria such as age and weight.


Bioimpedance Bone mass density Osteoporosis Dual-energy X-ray absorptiometry 


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Copyright information

© The Japanese Society for Bone and Mineral Research and Springer 2009

Authors and Affiliations

  • Heidi H. Y. Ngai
    • 1
  • Ching-Lung Cheung
    • 1
  • Tzy-Jyun Yao
    • 2
  • Annie W. C. Kung
    • 1
  1. 1.Department of Medicine, Queen Mary HospitalThe University of Hong KongHong KongChina
  2. 2.Clinical Trials CentreThe University of Hong KongHong KongChina

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