Leib E, Winzenrieth R, Lamy O, Hans D
Department of Medicine, University of Vermont College of Medicine, Burlington, VT, USA.
Calcif Tissue Int. 2014 Sep;95(3):201-8. doi: 10.1007/s00223-014-9882-3. Epub 2014 Jun 20.
Several cross-sectional studies have shown the ability of the TBS to discriminate between those with and without fractures in European populations. The aim of this study was to assess the ability of TBS to discriminate between those with and without fractures in a large female Caucasian population in the USA. This was a case-control study of 2,165 Caucasian American women aged 40 and older. Patients with illness or taking medications known to affect bone metabolism were excluded. Those in the fracture group (n = 289) had at least one low-energy fracture. BMD was measured at L1-L4, TBS calculated directly from the same DXA image. Descriptive statistics and inferential tests for difference were used. Univariate and multivariate logistic regression models were created to investigate possible association between independent variables and the status of fracture. Odds ratios per standard deviation decrease (OR) and areas under the ROC curve were calculated for discriminating parameters. Weak correlations were observed between TBS and BMD and between TBS and BMI (r = 0.33 and -0.17, respectively, p < 0.01). Mean age, weight, BMD and TBS were significantly different between control and fracture groups (all p ≤ 0.05), whereas no difference was noted for BMI or height. After adjusting for age, weight, BMD, smoking, and maternal and family history of fracture, TBS (but not BMD) remained a significant predictor of fracture: OR 1.28[1.13-1.46] even after adjustment. In a US female population, TBS again was able to discriminate between those with and those without fractures, even after adjusting for other clinical risk factors.
多项横断面研究表明,在欧洲人群中,骨小梁评分(TBS)能够区分有无骨折的人群。本研究的目的是评估在美国一大群白种女性中,TBS区分有无骨折人群的能力。这是一项针对2165名40岁及以上美国白种女性的病例对照研究。排除患有疾病或正在服用已知会影响骨代谢药物的患者。骨折组(n = 289)的患者至少有一处低能量骨折。在L1-L4测量骨密度(BMD),TBS直接从同一双能X线吸收测定(DXA)图像计算得出。使用描述性统计和差异推断检验。创建单变量和多变量逻辑回归模型以研究自变量与骨折状态之间可能的关联。计算用于区分参数的每标准差降低的比值比(OR)和ROC曲线下面积。观察到TBS与BMD之间以及TBS与体重指数(BMI)之间存在弱相关性(r分别为0.33和-0.17,p < 0.01)。对照组和骨折组之间的平均年龄、体重、BMD和TBS存在显著差异(所有p≤0.05),而BMI或身高无差异。在调整年龄、体重、BMD、吸烟以及母亲和家族骨折史后,TBS(而非BMD)仍然是骨折的显著预测指标:即使在调整后,OR为1.28[1.13 - 1.46]。在美国女性人群中,即使在调整其他临床风险因素后,TBS再次能够区分有无骨折的人群。