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多基因风险评分在提高骨折风险筛查中的应用:一项遗传风险预测研究。

Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study.

机构信息

Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada.

Department of Human Genetics, McGill University, Montréal, Québec, Canada.

出版信息

PLoS Med. 2020 Jul 2;17(7):e1003152. doi: 10.1371/journal.pmed.1003152. eCollection 2020 Jul.

Abstract

BACKGROUND

Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program.

METHODS AND FINDINGS

A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk.

CONCLUSIONS

Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.

摘要

背景

由于筛查项目仅能识别一小部分适合干预的人群,因此遗传性风险因素的基因组预测可以通过去除低遗传风险的个体来减少需要筛查的人数。因此,我们测试了定量超声足跟声速(SOS)多基因风险评分是否可以识别低危个体,从而安全地将其排除在骨折风险筛查计划之外。

方法和发现

在英国生物银行的 2 个独立子集中训练和选择了 SOS 的多基因风险评分(包含 341,449 人和 5,335 人)。表现最佳的预测模型被称为“gSOS”,并使用国家骨质疏松症指南小组临床指南(N=10,522 名符合条件的参与者)在 5 个验证队列中测试其在骨折风险筛查中的效用。所有个体均进行全基因组基因分型,并测量了骨折风险因素。在 5 个队列中,平均年龄范围为 57 至 75 岁,54%的研究对象为女性。主要结局是使用和不使用遗传预筛查正确识别需要治疗的个体的灵敏度和特异性。参考标准是基于骨密度(BMD)的骨折风险评估工具(FRAX)评分。次要结局是需要临床风险因素为基础的 FRAX(CRF-FRAX)筛查和基于 BMD 的 FRAX(BMD-FRAX)筛查的筛查人群比例。gSOS 与测量的 SOS 高度相关(r2=23.2%,95%CI 22.7%至 23.7%)。没有遗传预筛查的情况下,指南建议在验证队列中的正确治疗分配的灵敏度和特异性分别为 99.6%和 97.1%。然而,81%的人群需要进行 CRF-FRAX 测试,37%需要进行 BMD-FRAX 测试才能达到这种准确性。在预筛查中使用 gSOS 并将进一步评估限制在 gSOS 较低的人群中,对灵敏度和特异性的影响很小(分别为 93.4%和 98.5%),但需要进行 CRF-FRAX 测试和 BMD-FRAX 测试的人群比例分别降低了 37%和 41%。研究的局限性包括依赖于主要为欧洲种族的队列以及使用骨折风险的替代指标。

结论

我们的研究结果表明,在骨折风险筛查中使用多基因风险评分可以减少需要筛查测试的人数,包括 BMD 测量,同时保持识别应建议干预的个体的高灵敏度和特异性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/19cc/7331983/55743302c70b/pmed.1003152.g001.jpg

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