Nguyen Tuan V
Bone Biology Division, Garvan Institute of Medical Research, Sydney, Australia; St Vincent's Clinical School, UNSW Medicine, UNSW Australia, Sydney, Australia; Centre for Health Technology, University of Technology, Sydney, Australia.
J Clin Densitom. 2017 Jul-Sep;20(3):353-359. doi: 10.1016/j.jocd.2017.06.021. Epub 2017 Jul 17.
Over the past decade, several genetic variants or genes for osteoporosis have been identified through genome-wide association studies and candidate gene association studies. These genetic variants are common in the general population but have modest effect sizes, with odds ratio ranging from 1.1 to 1.5. Thus, the utility of any single variant is limited. However, theoretical and empirical studies have suggested that a profiling of multiple variants that are associated with bone phenotypes (i.e., "osteogenomic profile") can improve the accuracy of fracture prediction and classification beyond that obtained by conventional clinical risk factors. These results support the view that an osteogenomic profile, when integrated into existing models, can help clinicians and patients alike to better assess the risk fracture for an individual, and raise the possibility of personalized osteoporosis care.
在过去十年中，通过全基因组关联研究和候选基因关联研究，已经鉴定出了几种与骨质疏松症相关的基因变异或基因。这些基因变异在普通人群中很常见，但效应大小适中，优势比在1.1至1.5之间。因此，任何单个变异的效用都是有限的。然而，理论和实证研究表明，对与骨表型相关的多个变异进行分析（即“骨基因组分析”）可以提高骨折预测和分类的准确性，超过传统临床风险因素所获得的准确性。这些结果支持这样一种观点，即骨基因组分析在整合到现有模型中时，可以帮助临床医生和患者更好地评估个体的骨折风险，并提高个性化骨质疏松症护理的可能性。
