Potential Biomarkers to Improve the Prediction of Osteoporotic Fractures

Abstract

Osteoporotic fracture (OF) is associated with high disability and morbidity rates. The burden of OF may be reduced by early identification of subjects who are vulnerable to fracture. Although the current fracture risk assessment model includes clinical risk factors (CRFs) and bone mineral density (BMD), its overall ability to identify individuals at high risk for fracture remains suboptimal. Efforts have therefore been made to identify potential biomarkers that can predict the risk of OF, independent of or combined with CRFs and BMD. This review highlights the emerging biomarkers of bone metabolism, including sphongosine-1-phosphate, leucine-rich repeat-containing 17, macrophage migration inhibitory factor, sclerostin, receptor activator of nuclear factor-κB ligand, and periostin, and the importance of biomarker risk score, generated by combining these markers, in enhancing the accuracy of fracture prediction.

Keywords: Biomarkers; Bone density; Fracture; Risk factors.

Fig. 1. Comparison of the receiver operating characteristics curves and the area under the curve (AUC) of clinical risk factor (CRF) and femur neck bone mineral density (FN BMD) with or without biomarker risk score (BRS) for detecting osteoporotic fracture in 160 postmenopausal women. ^aSignificant improvement of the AUC upon addition of BRS to CRF and FN BMD.