Osteoporosis and Bone Biology Program, Garvan Institute of Medical Research, Sydney, NSW, Australia.
J Bone Miner Res. 2011 Feb;26(2):414-9. doi: 10.1002/jbmr.219.
Fragility fracture is a serious public health problem in the world. The risk of fracture is determined by genetic and nongenetic clinical risk factors. This study sought to quantify the contribution of genetic profiling to fracture prognosis. The study was built on the ongoing Dubbo Osteoporosis Epidemiology Study, in which fracture and risk factors of 858 men and 1358 women had been monitored continuously from 1989 and 2008. Fragility fracture was ascertained by radiologic reports. Bone mineral density at the femoral neck was measured by dual-energy X-ray absorptiometry (DXA). Fifty independent genes with allele frequencies ranging from 0.01 to 0.60 and relative risks (RRs) ranging from 1.01 to 3.0 were simulated. Three predictive models were fitted to the data in which fracture was a function of (1) clinical risk factors only, (2) genes only, and (3) clinical risk factors and 50 genes. The area under the curve (AUC) for model 1 was 0.77, which was lower than that of model II (AUC = 0.82). Adding genes into the clinical risk factors model (model 3) increased the AUC to 0.88 and improved the accuracy of fracture classification by 45%, with most (41%) improvement in specificity. In the presence of clinical risk factors, the number of genes required to achieve an AUC of 0.85 was around 25. These results suggest that genetic profiling could enhance the predictive accuracy of fracture prognosis and help to identify high-risk individuals for appropriate management of osteoporosis or intervention.
脆性骨折是全球严重的公共健康问题。骨折风险由遗传和非遗传临床危险因素决定。本研究旨在定量评估基因谱分析对骨折预后的贡献。该研究基于正在进行的 Dubbo 骨质疏松症流行病学研究,该研究自 1989 年至 2008 年连续监测了 858 名男性和 1358 名女性的骨折和危险因素。脆性骨折通过放射报告确定。通过双能 X 射线吸收法(DXA)测量股骨颈的骨矿物质密度。模拟了 50 个独立的基因,这些基因的等位基因频率范围为 0.01 至 0.60,相对风险(RR)范围为 1.01 至 3.0。将三个预测模型拟合到数据中,其中骨折是临床危险因素的函数(1),仅(2)基因,(3)临床危险因素和 50 个基因。模型 1 的曲线下面积(AUC)为 0.77,低于模型 II(AUC = 0.82)。将基因添加到临床危险因素模型(模型 3)中,将 AUC 提高到 0.88,并将骨折分类的准确性提高了 45%,特异性提高了 41%。在存在临床危险因素的情况下,需要大约 25 个基因才能达到 AUC 为 0.85。这些结果表明,基因谱分析可以提高骨折预后的预测准确性,并有助于识别高危人群,以进行适当的骨质疏松症管理或干预。