Eriksson Joel, Evans Daniel S, Nielson Carrie M, Shen Jian, Srikanth Priya, Hochberg Marc, McWeeney Shannon, Cawthon Peggy M, Wilmot Beth, Zmuda Joseph, Tranah Greg, Mirel Daniel B, Challa Sashi, Mooney Michael, Crenshaw Andrew, Karlsson Magnus, Mellström Dan, Vandenput Liesbeth, Orwoll Eric, Ohlsson Claes
Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
J Bone Miner Res. 2015 Jan;30(1):184-94. doi: 10.1002/jbmr.2314.
It is important to identify the patients at highest risk of fractures. A recent large-scale meta-analysis identified 63 autosomal single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD), of which 16 were also associated with fracture risk. Based on these findings, two genetic risk scores (GRS63 and GRS16) were developed. Our aim was to determine the clinical usefulness of these GRSs for the prediction of BMD, BMD change, and fracture risk in elderly subjects. We studied two male (Osteoporotic Fractures in Men Study [MrOS] US, MrOS Sweden) and one female (Study of Osteoporotic Fractures [SOF]) large prospective cohorts of older subjects, looking at BMD, BMD change, and radiographically and/or medically confirmed incident fractures (8067 subjects, 2185 incident nonvertebral or vertebral fractures). GRS63 was associated with BMD (≅3% of the variation explained) but not with BMD change. Both GRS63 and GRS16 were associated with fractures. After BMD adjustment, the effect sizes for these associations were substantially reduced. Similar results were found using an unweighted GRS63 and an unweighted GRS16 compared with those found using the corresponding weighted risk scores. Only minor improvements in C-statistics (AUC) for fractures were found when the GRSs were added to a base model (age, weight, and height), and no significant improvements in C-statistics were found when they were added to a model further adjusted for BMD. Net reclassification improvements with the addition of the GRSs to a base model were modest and substantially attenuated in BMD-adjusted models. GRS63 is associated with BMD, but not BMD change, suggesting that the genetic determinants of BMD differ from those of BMD change. When BMD is known, the clinical utility of the two GRSs for fracture prediction is limited in elderly subjects.
识别骨折风险最高的患者很重要。最近一项大规模荟萃分析确定了63个与骨密度(BMD)相关的常染色体单核苷酸多态性(SNP),其中16个也与骨折风险相关。基于这些发现,开发了两个遗传风险评分(GRS63和GRS16)。我们的目的是确定这些遗传风险评分在预测老年受试者骨密度、骨密度变化和骨折风险方面的临床实用性。我们研究了两个男性(美国男性骨质疏松性骨折研究[MrOS]、瑞典MrOS)和一个女性(骨质疏松性骨折研究[SOF])的大型老年受试者前瞻性队列,观察骨密度、骨密度变化以及经影像学和/或医学确认的新发骨折情况(8067名受试者,2185例新发非椎体或椎体骨折)。GRS63与骨密度相关(约解释了3%的变异),但与骨密度变化无关。GRS63和GRS16均与骨折相关。在调整骨密度后,这些关联的效应大小大幅降低。与使用相应加权风险评分相比,使用未加权的GRS63和未加权的GRS16也得到了类似结果。当将遗传风险评分添加到基础模型(年龄、体重和身高)中时,骨折的C统计量(AUC)仅有轻微改善,而添加到进一步调整了骨密度的模型中时,C统计量没有显著改善。将遗传风险评分添加到基础模型中时,净重新分类改善幅度不大,在调整骨密度的模型中显著减弱。GRS63与骨密度相关,但与骨密度变化无关,这表明骨密度的遗传决定因素与骨密度变化的遗传决定因素不同。当已知骨密度时,这两个遗传风险评分在预测老年受试者骨折方面的临床实用性有限。