Michaëlsson K, Bergström R, Mallmin H, Holmberg L, Wolk A, Ljunghall S
Department of Orthopaedics, Central Hospital, Västerås, Sweden.
Osteoporos Int. 1996;6(2):120-6. doi: 10.1007/BF01623934.
There is a great need for simple means of identifying persons at low risk of developing osteoporosis, in order to exclude them from screening with bone mineral measurements, since this procedure is too expensive and time-consuming for general use in the unselected population. We have determined the relationships between body measure (weight, height, body mass index, lean tissue mass, fat mass, waist-to-hip ratio) and bone mineral density (BMD) in 175 women of ages 28-74 years in a cross-sectional study in a county in central Sweden. Dual-energy X-ray absorptiometry was performed at three sites: total body, L2-4 region of lumbar spine, and neck region of the proximal femur. Using multiple linear regression models, the relationship between the dependent variable, BMD, and each of the body measures was determined, with adjustment for confounding factors. Weight alone, in a multivariate model, explained 28%, 21% and 15% of the variance in BMD of total body, at the lumbar spine and at the femoral neck according to these models. The WHO definition of osteopenia was used to dichotomize BMD, which made it possible, in multivariate logistic regression models, to estimate the risk of osteopenia with different body measures categorized into tertiles. Weight of over 71 kg was associated with a very low risk of being osteopenic compared with women weighing less than 64 kg, with odds ratios (OR) of 0.01 (95% confidence interval (CI) 0.00-0.09), 0.06 (CI 0.02-0.22) and 0.13 (CI 0.04-0.42) for osteopenia of total body, lumbar spine and femoral neck, respectively. Furthermore a sensitivity/specificity analysis revealed that, in this population, a woman weighing over 70 kg is not likely to have osteoporosis. Test specifics of a weight under 70 kg for osteoporosis (BMD less than 2.5 SD compared with normal young women) of femoral neck among the postmenopausal women showed a sensitivity of 0.94, a specificity of 0.36, positive predictive value (PPV) of 0.21, and negative predictive value (NPV) of 0.97. Thus, exclusion of the 33% of women with the highest weight meant only that 3% of osteoporotic cases were missed. The corresponding figures for lumbar spine were sensitivity 0.89, specificity 0.38, PPV 0.33, and NPV 0.91. All women who were defined as being osteoporotic of total body weighed under 62 kg. When the intention was to identify those with osteopenia of total body among the postmenopausal women we attained a sensitivity of 0.92 and a NPV of 0.91 for a weight under 70 kg, whereas we found that weight could not be used as an exclusion criterion for osteopenia of femoral neck and lumbar spine. Our data thus indicate that weight could be used to exclude women from a screening program for postmenopausal osteoporosis.
迫切需要有简单的方法来识别患骨质疏松症风险较低的人群，以便将他们排除在骨矿物质测量筛查之外，因为对于未经过筛选的普通人群而言，这种检查方法过于昂贵且耗时。在瑞典中部一个县开展的一项横断面研究中，我们测定了175名年龄在28至74岁之间女性的身体测量指标（体重、身高、体重指数、瘦组织质量、脂肪质量、腰臀比）与骨矿物质密度（BMD）之间的关系。采用双能X线吸收法在三个部位进行测量：全身、腰椎L2 - 4区域以及股骨近端颈部区域。使用多元线性回归模型，在对混杂因素进行校正后，确定了因变量BMD与各项身体测量指标之间的关系。根据这些模型，在多变量模型中，仅体重一项就分别解释了全身、腰椎和股骨颈BMD变异的28%、21%和15%。采用世界卫生组织（WHO）骨质疏松症的定义将BMD进行二分法分类，这使得在多变量逻辑回归模型中，可以根据不同身体测量指标分为三分位数来估计骨质疏松症的风险。与体重小于64 kg的女性相比，体重超过71 kg的女性患骨质疏松症的风险非常低，全身、腰椎和股骨颈骨质疏松症的比值比（OR）分别为0.０1（95%置信区间（CI）0.00 - 0.09）、0.06（CI 0.02 - 0.22）和0.13（CI 0.04 - 0.42）。此外，一项敏感性/特异性分析显示，在该人群中，体重超过70 kg的女性不太可能患有骨质疏松症。绝经后女性中，股骨颈骨质疏松症（BMD比正常年轻女性低2.5个标准差以下）体重低于70 kg的检测指标显示，敏感性为0.94，特异性为0.36，阳性预测值（PPV）为0.21，阴性预测值（NPV）为0.9７。因此，排除体重最高的33%的女性仅意味着漏诊了3%的骨质疏松症病例。腰椎的相应数据为敏感性0.89，特异性0.38，PPV 0.33，NPV 0.91。所有被定义为全身骨质疏松症的女性体重均低于62 kg。当旨在识别绝经后女性中全身骨质疏松症患者时，对于体重低于70 kg的女性，我们获得了0.92的敏感性和0.91的NPV，然而我们发现体重不能用作股骨颈和腰椎骨质疏松症的排除标准。因此，我们的数据表明体重可用于将女性排除在绝经后骨质疏松症筛查项目之外。
