Comparisons of Noninvasive Bone Mineral Measurements in Assessing Age-Related Loss, Fracture Discrimination, and Diagnostic Classification

<jats:title>Abstract</jats:title> <jats:p>The purpose of this study was to examine the commonly available methods of noninvasively assessing bone mineral status across three defined female populations to examine their interrelationships, compare their respective abilities to reflect age- and menopause-related bone loss, discriminate osteoporotic fractures, and classify patients diagnostically. A total of 47 healthy premenopausal (age 33 ± 7 years), 41 healthy postmenopausal (age 64 ± 9 years), and 36 osteoporotic postmenopausal (age 70 ± 6 years) women were examined with the following techniques: (1) quantitative computed tomography of the L1–L4 lumbar spine for trabecular (QCT TRAB BMD) and integral (QCT INTG BMD) bone mineral density (BMD); (2) dual X-ray absorptiometry of the L1–L4 posterior-anterior (DXA PA BMD) and L2–L4 lateral (DXA LAT BMD) lumbar spine, of the femoral neck (DXA NECK BMD) and trochanter (DXA TROC BMD), and of the ultradistal radius (DXA UD BMD) for integral BMD; (3) peripheral QCT of the distal radius for trabecular BMD (pQCT TRAB BMD) and cortical bone mineral content (BMC) (pQCT CORT BMC); (4) two radiographic absorptiometric techniques of the metacarpal (RA METC BMD) and phalanges (RA PHAL BMD) for integral BMD; and (5) two quantitative ultrasound devices (QUS) of the calcaneus for speed of sound (SOS CALC) and broadband ultrasound attenuation (BUA CALC). In general, correlations ranged from (r = 0.10−0.93) among different sites and techniques. We found that pQCT TRAB BMD correlated poorly (r ≤ 0.46) with all other measurements except DXA UD BMD (r = 0.62, p ≤ 0.0001) and RA PHAL BMD (r = 0.52, p ≤ 0.0001). The strongest correlation across techniques was between QCT INT BMD and DXA LAT BMD (r = 0.87, p ≤ 0.0001), and the weakest correlation within a technique was between pQCT TRAB BMD and pQCT CORT BMC (r = 0.25, p ≤ 0.05). Techniques showing the highest correlations with age in the healthy groups also showed the greatest differences among groups. They also showed the best discrimination (as measured by the odds ratios) for the distinction between healthy postmenopausal and osteoporotic postmenopausal groups based on age-adjusted logistic regression analysis. For each anatomic site, the techniques providing the best results were: (1) spine, QCT TRAB BMD (annual loss, −1.2% [healthy premenopausal and healthy postmenopausal]); Student's t-value [not the T score], 5.4 [healthy postmenopausal vs. osteoporotic postmenopausal]; odds ratio, 4.3 [age-adjusted logistic regression for healthy postmenopausal vs. osteoporotic postmenopausal]); (2) hip, DXA TROC BMD (−0.46; 3.5; 2.2); (3) radius, DXA UD BMD (−0.44; 3.3; 1.9) and pQCT, CORT BMC (−0.72; 2.9; 1.7); (4) hand, RA PHAL (−0.51; 3.6; 2.0); and (5) calcaneus, SOS (−0.09; 3.4; 2.1) and BUA (−0.52; 2.6; 1.7). Despite these performance trends, the differences among sites and techniques were statistically insignificant (p &gt; 0.05) using age-adjusted receiver operating characteristic (ROC) curve analysis. Nevertheless, kappa score analysis (using −2.0 T score as the cut-off value for osteopenia and −2.5 T score for osteoporosis) showed that in general the diagnostic agreement among these measurements in classifying women as osteopenic or osteoporotic was poor, with kappa scores averaging about 0.4 (exceptions were QCT TRAB/INTG BMD, DXA LAT BMD, and RA PHAL BMD, with kappa scores ranging from 0.63 to 0.89). Often different patients were estimated at risk by using different measurement sites or techniques.</jats:p>