Healthy Ageing Theme, Garvan Institute of Medical Research, Sydney, Australia.
Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia.
Osteoporos Int. 2021 Feb;32(2):271-280. doi: 10.1007/s00198-020-05403-2. Epub 2020 Aug 12.
Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD.
Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction.
The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds.
In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models.
The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.
使用决策曲线分析（DCA），对 2188 名女性和 1324 名男性进行分析，发现由 62 个遗传变异构建的成骨基因组谱，可提高骨折风险预测的临床净获益，优于临床风险因素和骨密度（BMD）。
遗传分析是评估骨折风险的一种有前途的工具。本研究旨在使用决策曲线分析（DCA），这是一种确定遗传分析对骨折风险预测影响的新方法。
研究共纳入 2188 名女性和 1324 名年龄在 60 岁及以上的男性，随访时间最长达 23 年。在基线时获取骨密度（BMD）和临床风险因素。记录骨折和死亡率的发生情况。从 62 个与 BMD 相关的遗传变异中构建加权个体遗传风险评分（GRS）。考虑了 4 种模型：CRF（临床风险因素）；CRF+GRS；包括 CRF 和股骨颈 BMD 的 Garvan 模型（GFRC）；以及 GFRC+GRS。使用 DCA 在一系列合理的临床风险阈值范围内评估预测模型的临床净获益。
在女性和男性中，全模型 GFRC+GRS 均实现了最高的净获益。对于女性 10 年风险阈值>18%、男性 15%以上的风险阈值，GRS 提供的净获益高于 CRF 模型。在 20%的风险阈值下，与 Garvan 模型相比，添加 GRS 可以帮助每 81 名女性或每 24 名男性避免 1 次额外治疗。在较低的风险阈值下，4 种模型之间没有显著差异。
将遗传分析添加到临床风险因素中，可以提高骨折较高风险阈值的净临床获益。尽管在存在 BMD+CRF 的情况下，遗传分析的贡献较小，但它似乎能够替代 BMD 进行骨折预测。
