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全基因组遗传评分添加到标准推荐的分层工具中并不能提高极低骨密度患者的识别率。

A genome-wide genomic score added to standard recommended stratification tools does not improve the identification of patients with very low bone mineral density.

机构信息

Department of Rheumatology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200, Aarhus, Denmark.

Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Boulevard 82, 8200, Aarhus, Denmark.

出版信息

Osteoporos Int. 2023 Nov;34(11):1893-1906. doi: 10.1007/s00198-023-06857-w. Epub 2023 Jul 26.

Abstract

UNLABELLED

The role of integrating genomic scores (GSs) needs to be assessed. Adding a GS to recommended stratification tools does not improve the prediction of very low bone mineral density. However, we noticed that the GS performed equally or above individual risk factors in discrimination.

PURPOSE

We aimed to investigate whether adding a genomic score (GS) to recommended stratification tools improves the discrimination of participants with very low bone mineral density (BMD).

METHODS

BMD was measured in three thoracic vertebrae using CT. All participants provided information on standard osteoporosis risk factors. GSs and FRAX scores were calculated. Participants were grouped according to mean BMD into very low (<80 mg/cm), low (80-120 mg/cm), and normal (>120 mg/cm) and according to the Bone Health and Osteoporosis Foundation recommendations for BMD testing into an "indication for BMD testing" and "no indication for BMD testing" group. Different models were assessed using the area under the receiver operating characteristics curves (AUC) and reclassification analyses.

RESULTS

In the total cohort (n=1421), the AUC for the GS was 0.57 (95% CI 0.52-0.61) corresponding to AUCs for osteoporosis risk factors. In participants without indication for BMD testing, the AUC was 0.60 (95% CI 0.52-0.69) above or equal to AUCs for osteoporosis risk factors. Adding the GS to a clinical risk factor (CRF) model resulted in AUCs not statistically significant from the CRF model. Using probability cutoff values of 6, 12, and 24%, we found no improved reclassification or risk discrimination using the CRF-GS model compared to the CRF model.

CONCLUSION

Our results suggest adding a GS to a CRF model does not improve prediction. However, we noticed that the GS performed equally or above individual risk factors in discrimination. Clinical risk factors combined showed superior discrimination to individual risk factors and the GS, underlining the value of combined CRFs in routine clinics as a stratification tool.

摘要

目的

我们旨在研究在推荐的分层工具中加入基因组评分(GS)是否可以提高极低骨密度(BMD)参与者的区分能力。

方法

使用 CT 测量三个胸椎的 BMD。所有参与者均提供了有关标准骨质疏松症风险因素的信息。计算了 GS 和 FRAX 评分。根据平均 BMD 将参与者分为极低(<80mg/cm)、低(80-120mg/cm)和正常(>120mg/cm)组,并根据 Bone Health and Osteoporosis Foundation 的 BMD 检测建议分为“BMD 检测指征”和“无 BMD 检测指征”组。使用受试者工作特征曲线下面积(AUC)和重新分类分析评估不同模型。

结果

在总队列(n=1421)中,GS 的 AUC 为 0.57(95%CI 0.52-0.61),与骨质疏松症风险因素的 AUC 相对应。在没有 BMD 检测指征的参与者中,AUC 为 0.60(95%CI 0.52-0.69),高于或等于骨质疏松症风险因素的 AUC。将 GS 添加到临床风险因素(CRF)模型中,AUC 与 CRF 模型相比没有统计学意义。使用概率截断值为 6%、12%和 24%,我们发现使用 CRF-GS 模型与使用 CRF 模型相比,重新分类或风险区分没有得到改善。

结论

我们的结果表明,将 GS 添加到 CRF 模型中并不能提高预测能力。然而,我们注意到 GS 在区分方面的表现与个别风险因素相当或优于个别风险因素。综合临床风险因素的表现优于个别风险因素和 GS,这突显了综合 CRF 在常规临床实践中作为分层工具的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b15a/10579117/9c67713f4756/198_2023_6857_Fig1_HTML.jpg

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