Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans


  • Group authorship/contribution: A list of the members of the Osteoporotic Fractures in Men (MrOS) Research Group may be found in Appendix B.


Vertebral strength, as estimated by finite element analysis of computed tomography (CT) scans, has not yet been compared against areal bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA) for prospectively assessing the risk of new clinical vertebral fractures. To do so, we conducted a case-cohort analysis of 306 men aged 65 years and older, which included 63 men who developed new clinically-identified vertebral fractures and 243 men who did not, all observed over an average of 6.5 years. Nonlinear finite element analysis was performed on the baseline CT scans, blinded to fracture status, to estimate L1 vertebral compressive strength and a load-to-strength ratio. Volumetric BMD by quantitative CT and areal BMD by DXA were also evaluated. We found that, for the risk of new clinical vertebral fracture, the age-adjusted hazard ratio per standard deviation change for areal BMD (3.2; 95% confidence interval [CI], 2.0–5.2) was significantly lower (p < 0.005) than for strength (7.2; 95% CI, 3.6–14.1), numerically lower than for volumetric BMD (5.7; 95% CI, 3.1–10.3), and similar for the load-to-strength ratio (3.0; 95% CI, 2.1–4.3). After also adjusting for race, body mass index (BMI), clinical center, and areal BMD, all these hazard ratios remained highly statistically significant, particularly those for strength (8.5; 95% CI, 3.6–20.1) and volumetric BMD (9.4; 95% CI, 4.1–21.6). The area-under-the-curve for areal BMD (AUC = 0.76) was significantly lower than for strength (AUC = 0.83, p = 0.02), volumetric BMD (AUC = 0.82, p = 0.05), and the load-to-strength ratio (AUC = 0.82, p = 0.05). We conclude that, compared to areal BMD by DXA, vertebral compressive strength and volumetric BMD consistently improved vertebral fracture risk assessment in this cohort of elderly men. © 2012 American Society for Bone and Mineral Research.


Vertebral fractures, the most common clinical manifestation of osteoporosis, can cause significant pain and portend future vertebral and hip fractures.1 The most widely used clinical predictor of vertebral fracture is areal bone mineral density (BMD), as measured at the hip or spine by dual-energy X-ray absorptiometry (DXA).2 However, areal BMD cannot distinguish between the cortical and trabecular bone or variable depth in the anterior-posterior direction, and, for the spine, is further confounded by osteophytes, facet arthritic changes, and aortic calcification. As a result of these limitations and because most of those who suffer osteoporotic fractures are not in fact osteoporotic as classified by areal BMD,3, 4 there remains a need to develop clinically-feasible approaches that can improve fracture risk assessment for the spine.

One possible approach for improving fracture risk assessment for the spine combines quantitative computed tomography (CT) with finite element analysis and bone biomechanics to provide estimates of vertebral strength and a load-to-strength ratio. For the spine, this “biomechanical CT” (BCT) technique has been validated in cadaver experiments by multiple research groups,5, 6 has provided unique insight into therapeutic treatment effects,7, 8 and has surpassed DXA in differentiating individuals with versus without prevalent vertebral fracture.9, 10 However, BCT remains untested against DXA for prospectively assessing the risk of new vertebral fractures. Addressing this limitation, we compared BCT-derived vertebral strength and the load-to-strength ratio, as well as volumetric BMD from quantitative CT, versus DXA-derived areal BMD for assessing the risk of new clinical vertebral fractures in elderly men. This study is novel because it is the first to report on such a comparison for the prediction of new vertebral fractures.


Study population

All participants in this study were from the multicenter Osteoporotic Fractures in Men (MrOS) Research Group observational study, which enrolled 5994 volunteer community-dwelling participants from March 2000 through April 2002 at six sites throughout the United States and is described in detail elsewhere.11, 12 All participants gave written informed consent. Eligible participants were at least 65 years of age, could walk without assistance from another person, and had not had bilateral hip replacement surgery. Proportions of Black, Asian, and Hispanic men enrolled at each study site were generally representative of those reflected in the local population of older men by U.S. Census data.11, 12 Because of resource constraints, CT scans at baseline were obtained for only 3663 participants, with an effort to scan as many non-white men as possible. The demographics of the men in this “CT cohort” and the full cohort were comparable, except for a slightly greater proportion of non-white men in the CT cohort.13

Study design

We used a case-cohort design, our study cohort consisting of the approximately 3600 men from the overall MrOS “CT cohort” who also had a DXA exam at baseline. Our cases consisted of all men from this study cohort who had an incident (new) radiographically-confirmed “clinical” vertebral fracture; ie, a new vertebral fracture that presented clinically during the course of the observation period. These cases were compared to a randomly selected subset from our study cohort (any cases in that subset were removed). Age, weight, height, body mass index (BMI), and areal BMD properties for our study sample were similar to those for the full MrOS CT cohort (Table 1). BCT analysis was performed on the baseline CT scans in a blinded fashion to provide measures of vertebral compressive strength; we also measured a load-to-strength ratio,14, 15 as well as volumetric BMD by quantitative CT and areal BMD by DXA. The outcomes were then compared for their ability to predict the new clinical vertebral fractures.

Table 1. Baseline for the Full MrOS Cohort Who Had CT at Baseline and For the Subcohort Included in This BCT Study
Measurement (units)Full MrOS CT cohortBCT subcohort
Pooled (n = 3489)No-FX (n = 3411)FX (n = 78)Pooled (n = 306)No-FX (n = 243)FX (n = 63)Difference (%)a
  • Data are shown for the pooled dataset and also by fracture status. Values are mean (SD) except where noted.

  • aBMD = areal BMD from DXA; BCT = biomechanical CT; BMI = body mass index; DXA = dual energy X-ray absorptiometry; FN = femoral neck; FX = fracture group; LS = total lumbar spine; MrOS = Osteoporotic Fractures in Men; n.m. = data not measured in this analysis; n.s. = not significant; No-FX = no-fracture group; vBMD = volumetric BMD from quantitative CT.

  • a

    Percent differences (FX versus No-FX) for the BCT subcohort are also shown when significant (p < 0.05 at least; absolute difference is shown for age).

  • b

    Load-to-strength ratio is the ratio of estimated in vivo loading (N) to vertebral strength (N).

  • c

    Strength-to-density ratio is the ratio of vertebral strength to integral vBMD (the volumetric BMD of all bone in the finite element model).

  • *

    p < 0.005.

  • **

    p < 0.0001.

Age (years)73.5 (5.8)73.5 (5.8)75.6 (5.9)74.3 (6.2)73.7 (6.1)76.3 (6.3)+2.6*
Weight (kg)83.0 (13.3)83.0 (13.4)80.9 (11.6)83.8 (13.5)84.4 (13.9)81.4 (11.9)n.s.
Height (cm)174 (7)174 (7)174 (7)174 (7)175 (7)174 (6)n.s.
BMI (kg/m2)27.4 (3.8)27.4 (3.8)26.8 (3.4)27.5 (3.8)27.6 (3.9)27.0 (3.5)n.s.
LS aBMD (g/cm2)1.07 (0.19)1.07 (0.19)0.91 (0.15)1.04 (0.19)1.08 (0.19)0.91 (0.14)−15.7**
FN aBMD (g/cm2)0.78 0.13)0.79 (0.13)0.72 (0.13)0.76 (0.12)0.78 (0.12)0.71 (0.14)−9.0**
Integral vBMD (mg/cm3)n.m.n.m.n.m.189 (47)199 (44)151 (36)−24.1**
Vertebral strength (N)n.m.n.m.n.m.6360 (2410)6880 (2300)4320 (1620)−37.2**
Load-to-strength ratio (N)bn.m.n.m.n.m.0.41 (0.18)0.36 (0.12)0.58 (0.26)+61.1**
Strength-to-density ratio (Ncm3/mg)cn.m.n.m.n.m.32.7 (6.1)34.0 (5.7)27.9 (5.5)−17.9**

To assess new clinical vertebral fractures, every 4 months after baseline, MrOS participants completed a mailed questionnaire regarding recent falls and fractures. All thoracic and lumbar spine fractures that were reported by participants were then adjudicated centrally. To adjudicate, the radiological image (X-ray or MRI) used to diagnose the fracture by the participants' physician in the community was obtained and sent to the Coordinating Center. The study radiologist confirmed the presence of a new vertebral fracture using these community-acquired images, comparing them to lateral thoracic and lumbar X-rays that were obtained for all MrOS participants at baseline. A new clinical vertebral fracture was then defined as a change in semiquantitative (SQ) grade16 of at least 1 between the baseline film and follow-up image. The average (± SD) follow-up time for new fractures was 6.5 ± 2.2 years.

Scanning procedures

Baseline CT and DXA scans were used for all analyses. The quantitative CT scans were obtained using a standardized protocol in which the lumbar region was scanned from 5 mm above the L1 superior endplate to 5 mm below the L2 inferior endplate. The scans were acquired using the settings: 120 kVp, 150 mA, 1-mm slice thickness (pitch of 1), 48 cm (or equivalent) field of view, 512 × 512 matrix size in spiral reconstruction mode using a standard kernel. Although different types of CT machines were used at each site, the same type of calibration phantom (Image Analysis, Inc., Columbia, KY, USA) with known hydroxyapatite concentrations (0, 75, and 150 mg/cm3) was included in every scan. Areal BMD (in g/cm2) at the hip and spine was measured using the same model of fan-beam DXA scanner (QDR 4500W; Hologic, Inc., Waltham, MA, USA). Centralized quality control procedures, certification of DXA operators, and standardized procedures for scanning were used to ensure reproducibility of DXA measurements.

BCT analysis

BCT analysis of the anonymized CT scans was performed at the University of California, Berkeley (UC Berkeley), blinded to the fracture status of participants. Using custom software developed at UC Berkeley, each vertebral image was first calibrated using the external phantom (Image Analysis, Inc.) visible in each image, segmented from the surrounding tissue using a constant threshold value, resampled to 1 × 1 × 1 mm3 voxels, rotated into a standard coordinate system, cropped to remove the posterior elements, then converted into a 1 × 1 × 1 mm3 voxel-type mesh of eight-node cube-shaped finite elements.

Following methods for finite element analysis similar to those described elsewhere,17 the volumetric BMD of each element was used to assign element-specific material properties to each finite element and the resulting “voxel-type” of finite element models were then virtually loaded to failure. All models were virtually loaded in uniform compression via a thin layer of polymethylmethacrylate (E = 2500 MPa) placed over the endplates, and all bone material was modeled as elastic-perfectly-plastic using a Mises failure criterion, based on cadaver data for human vertebral trabecular bone.18 Anisotropic elastic and isotropic strength properties that were implemented are described elsewhere17 and were further adjusted to account for side-artifact errors19 using the inverse-linear pooled density relations from Bevill and colleagues.20 Nonlinear finite element analysis (ABAQUS v6.6; Simulia, Providence, RI, USA) was used to compute a value of compressive strength, defined as the total reaction force generated at an imposed overall deformation equivalent to 1.9% strain. Results from cadaver biomechanical testing of 52 elderly vertebral bodies (Appendix A) confirmed that this overall implementation provided highly correlated (R2 = 0.85) estimates of vertebral compressive strength compared to the experimentally measured values, with statistical Y = X accuracy (Fig. 1).

Figure 1.

Validation of the BCT-derived compressive strength estimates against experimental measurements from biomechanical testing. Data are for 52 isolated individual vertebral body specimens (T7–L4) taken from 52 cadavers. Least squares regression analysis indicated that the slope was not significantly different than zero (95% CIs, 0.97–1.23) and the intercept was not different than zero (p = 0.78).

In addition to the vertebral compressive strength, we measured a number of secondary outcomes. To account for an estimate of the in vivo loading on a patient-specific basis, a load-to-strength ratio was calculated using patient-specific weight and height data. For this calculation, we used a static equilibrium model15 to simulate 90-degree forward flexion while lifting a 10-kg mass in outstretched arms.14 “Integral” volumetric BMD was (in mg/cm3) of all the bone in the finite element model, ie, the entire vertebral body not including the posterior elements, was measured from the quantitative CT scans and was taken as the mean value of the volumetric BMD of each resampled voxel. To help assess any predictive effects of compressive strength over and above those associated with volumetric BMD, a strength-to-density ratio was computed from these two variables.

All BCT analyses were performed for the L1 vertebral level when acceptable CT scans were available for that level, or L2 otherwise. Originally, 374 CT scans were selected from the MrOS CT cohort for BCT analysis. Of these scans, 11 were not transferred to Berkeley, 39 were eliminated from further analysis because of missing slices in the CT scan, 15 scans were eliminated because of poor image quality, and three were eliminated because of an unusual vertebral body shape that prevented analysis. Hence, we obtained BCT data for 306 men. For 20 of these men, L2 was analyzed instead of L1 because L1 showed imaging artifacts.

Statistical analysis

All statistical analyses were performed at the San Francisco Coordinating Center, after completion of all BCT analyses. Pearson correlation coefficients were calculated for the biomechanical parameters among themselves and versus lumbar spine areal BMD, age, weight, and BMI to determine associations with established predictors of fracture. Modeling of the time to incident spine fracture was performed using Cox proportional hazards regression with the Prentice weighting method and robust variance estimate necessitated by the case-cohort design.21 Hazard ratios for fracture were expressed per 1 SD change in the parameter, and also by quartiles with respect to the lowest risk quartile as the referent group. Covariates were then added to the model sequentially (age, race, BMI, study geographic site, then total lumbar spine areal BMD from the spine DXA) to assess their effect on the hazard ratio. To test the equivalence of hazard ratios (versus that for areal BMD), two similar Cox models were fit to the same data simultaneously using a repeated measures approach. To assess prediction of fracture, receiver operator characteristic (ROC) curve analysis was used on the unadjusted variables and values of area under the ROC curve (AUC) for the various predictors were compared to the AUC for areal BMD using logistic regression. Because the most clinically relevant portion of the ROC curve for fracture risk prediction is the high-specificity region, we calculated values of sensitivity at 90% and 95% specificity. All statistical analyses were performed using SAS (version 9.1; SAS Institute, Inc., Cary, NC, USA) except for the ROC analysis, which used STATA (version 11, SJ9-1 st0154; StataCorp, College Station, TX, USA).


On average, compared to the men who did not fracture (n = 243), the men who fractured (n = 63) had lower values of all BMD measures, even lower values of strength, and proportionally higher (worse) values of the load-to-strength ratio (Table 1). Total lumbar spine areal BMD (−15.7%), femoral neck areal BMD (−9.0%), volumetric BMD (−24.1%), vertebral strength (−37.2%), and strength-to-density ratio (−17.9%) were all lower for those who fractured and the load-to-strength ratio was higher (+61.1%), compared to men without a fracture. Correlation analysis with age for the pooled data indicated that all properties were lower as age increased except for total lumbar spine areal BMD (not significant) and the load-to-strength ratio (which was higher; Table 2). As expected, both total lumbar spine areal BMD (r = 0.79) and volumetric BMD (r = 0.92) were positively correlated with vertebral strength (Table 2). Scatter plots revealed a trend for fracture cases to occur at very low values of strength and volumetric BMD, and high values of the load-to-strength ratio (Fig. 2).

Table 2. Pearson Correlation Coefficients Between the Various Outcomes
 Age (years)Weight (kg)Height (cm)BMI (kg/m2)LS aBMD (g/cm2)FN aBMD (g/cm2)vBMD (mg/cm3)Strength (N)Load-to-strength ratio (N)aStrength-to-density ratio (Ncm3/mg)b
  • Data are results of univariate analysis with fracture and no-fracture groups pooled; p ≤ 0.05 at least, except for italicized cases, for which p > 0.05.

  • aBMD = areal BMD from DXA; BMI = body mass index; DXA = dual energy X-ray absorptiometry; FN = femoral neck; LS = total lumbar spine; vBMD = volumetric BMD from quantitative CT.

  • a

    Load-to-strength ratio is the ratio of estimated in vivo loading (N) to vertebral strength (N).

  • b

    Strength-to-density ratio is the ratio of vertebral strength to integral vBMD (the volumetric BMD of all bone in the finite element model).

LS aBMD0.−0.600.56
FN aBMD−0.160.390.170.370.631.000.580.56−0.430.41
Integral vBMD−−0.760.57
Vertebral strength−−0.790.83
Load-to-strength ratioa0.−0.60−0.43−0.76−0.791.00−0.74
Strength-to-density ratiob−0.260.310.240.220.560.410.570.83−0.741.00
Figure 2.

Vertebral strength, load-to-strength ratio, volumetric BMD, and the ratio of vertebral strength to volumetric BMD, all plotted against total lumbar spine areal BMD (by DXA). Fracture cases are shown in solid circles (n = 63), no-fracture cases are shown in open squares (n = 243). Total lumbar spine areal BMD values of 0.82 and 0.98 g/cm2 correspond to T-scores of −2.5 and −1.0, respectively, using young male reference values.

On average, hazard ratios were generally higher for strength and volumetric BMD than for areal BMD (Table 3). Specifically, the age-adjusted hazard ratio per SD decrease for lumbar spine areal BMD (3.2; 95% confidence interval [CI], 2.0–5.2) was significantly lower (p < 0.005) than for vertebral strength (7.2; 95% CI, 3.6–14.1), numerically lower than for volumetric BMD (5.7; 95% CI, 3.1–10.3) and similar for the load-to-strength ratio (3.0; 95% CI, 2.1–4.3). All hazard ratios were relatively insensitive to adjustment for age, race, and BMI, but the hazard ratios for strength (9.6; 95% CI, 4.6–19.8) and volumetric BMD (9.9; 95% CI, 5.0–19.7) both increased appreciably after adjusting for clinical site. All hazard ratios for BCT-derived variables remained statistically significant and were only moderately attenuated when lumbar spine areal BMD was included in the statistical model (Table 3). When the hazard ratios were broken down by quartiles, the gradient of risk for the highest-risk quartile was consistently higher for each biomechanical outcome and volumetric BMD than for areal BMD (Table 4).

Table 3. Relative Hazard per Unit Change in SD for Each of the Main Explanatory Variables of New Clinical Vertebral Fractures, With and Without Additional Variables in the Model
Main variableAdditional variables in the model
NoneAgeAge Race BMIAge Race BMI SiteAge Race BMI Site LS aBMD
  • Values are SD (95% confidence interval).

  • aBMD = areal BMD from DXA; BMI = body mass index; DXA = dual energy X-ray absorptiometry; FN = femoral neck; LS = total lumbar spine; Site = imaging geographic region; vBMD = volumetric BMD from quantitative CT.

  • a

    Relative hazard denotes an increase in risk per decrease of 1 SD for all variables except for the load-to-strength ratio, for which it indicates an increase in risk per increase of 1 SD.

  • b

    Load-to-strength ratio is the ratio of estimated in vivo loading (N) to vertebral strength (N).

  • c

    Strength-to-density ratio is the ratio of vertebral strength to integral vBMD (the volumetric BMD of all bone in the finite element model.

  • *

    p ≤ 0.05 for comparison versus LS aBMD (same column) for the models in which LS aBMD was not included in the model (the last column).

  • **

    p ≤ 0.01 for comparison versus LS aBMD (same column) for the models in which LS aBMD was not included in the model (the last column).

  • ***

    p ≤ 0.001 for comparison versus LS aBMD (same column) for the models in which LS aBMD was not included in the model (the last column).

LS aBMD3.5 (2.2–5.4)3.2 (2.0–5.2)3.3 (2.1–5.2)3.4 (2.1–5.4)
FN aBMD2.1 (1.4–3.2)1.8 (1.2–2.9)2.1 (1.3–3.6)2.1 (1.2–3.5)0.9 (0.4–1.8)
Integral vBMD6.1 (3.4–11.1)*5.7 (3.1–10.3)5.8 (3.2–10.8)9.9 (5.0–19.7)***9.4 (4.1–21.6)
Strength7.6 (3.9–14.9)**7.2 (3.6–14.1)**7.3 (3.7–14.5)**9.6 (4.6–19.8)***8.5 (3.6–20.1)
Load-to-strength ratioa,b3.1 (2.2–4.4)3.0 (2.1–4.3)3.0 (2.1–4.5)3.6 (2.5–5.1)2.9 (1.9–4.4)
Strength-to-density ratioc3.4 (2.3–5.1)3.3 (2.2–4.9)3.3 (2.2–5.0)3.4 (2.2–5.0)2.2 (1.4–3.6)
Table 4. Relative Hazard for Each Quartile
PredictorRelative hazard for each quartilea
  • Values are normalized with respect to the lowest-risk quartile, all adjusted for age (95% CI).

  • aBMD = areal BMD from DXA; CI = confidence interval; DXA = dual energy X-ray absorptiometry; LS = total lumbar spine; vBMD = volumetric BMD from quantitative CT.

  • a

    Q1 is taken as the lowest quartile, having the lowest absolute values of the predictor variable; Q4 has the highest values. For the calculation of relative hazards, the quartile with the lowest risk is the referent, and has its relative hazard set to unity; this referent quartile is Q4 for all predictors except for the load-to-strength ratio, for which it is Q1.

  • b

    Load-to-strength ratio is the ratio of estimated in vivo loading (N) to vertebral strength (N).

  • c

    Strength-to-density ratio is the ratio of vertebral strength to integral vBMD (the volumetric BMD of all bone in the finite element model).

LS aBMD14.3 (4.1–49.7)7.5 (2.1–27.0)3.2 (0.8–12.4)1.0
Integral vBMD26.9 (6.2–116)10.9 (2.4–48.8)3.9 (0.8–19.5)1.0
Vertebral strength27.3 (6.2–120)8.9 (1.9–40.8)3.5 (0.7–17.5)1.0
Load-to-strength ratiob1.02.1 (0.5–8.6)6.4 (1.8–23.1)19.6 (5.7–67.7)
Strength-to-density ratioc42.3 (5.5–326)22.6 (2.9–177)9.6 (1.2–77.6)1.0

These trends for the hazard ratios were accompanied by improvements in fracture prediction, as characterized by the ROC analysis. Compared to the AUC value for total spine areal BMD (AUC = 0.76; Table 5), AUC values were higher for each of vertebral strength (AUC = 0.83, p = 0.02), volumetric BMD (0.82, p = 0.05), and the load-to-strength ratio (AUC = 0.820, p = 0.05). At clinically relevant levels of high specificity, the sensitivity for vertebral strength was higher than for total spine areal BMD (0.52 versus 0.43 at 90% specificity; and 0.37 versus 0.30 at 95% specificity) and similar trends were seen for the load-to-strength ratio. Sensitivity for volumetric BMD was less consistent. At 95% specificity, the sensitivity for volumetric BMD was higher than for areal BMD (0.38 versus 0.30), but at 90% specificity the sensitivity values were similar (0.44 versus 0.43).

Table 5. AUC Values From ROC Curve Analysis (Unadjusted Variables) and the Associated Sensitivity Values for Two Clinically Relevant Values of High Specificity (90% and 95%)
VariableAUCpaSensitivity (%)
90% Specificity95% Specificity
  • aBMD = areal BMD from DXA; AUC = area under the curve; DXA = dual energy X-ray absorptiometry; FN = femoral neck; LS = total lumbar spine; ROC = receiver operator characteristic; vBMD = volumetric BMD from quantitative CT.

  • a

    p < 0.005; p value is for comparing the AUC value against that for LS aBMD; not performed for FN aBMD.

  • b

    Load-to-strength ratio is the ratio of estimated in vivo loading (N) to vertebral strength (N).

  • c

    Strength-to-density ratio is the ratio of vertebral strength to integral vBMD (the volumetric BMD of all bone in the finite element model).

LS aBMD0.76 4330
FN aBMD0.65 3021
Integral vBMD0.830.054438
Vertebral strength0.830.025237
Load-to-strength ratiob0.820.055237
Strength-to-density ratioc0.770.743024


Our main goal in this observational multicenter study was to assess BCT-derived vertebral strength compared to areal BMD for assessing the risk of new clinical vertebral fractures in older men. By all metrics employed for this comparison, fracture risk assessment was significantly improved by vertebral strength. Although BCT-derived estimates of vertebral strength or the load-to-strength ratio in cross-sectional studies have been shown to better discriminate those with from those without prevalent spine fractures,9, 10 this study is the first to demonstrate improved vertebral fracture risk assessment in a prospective manner; ie, for prediction of new spine fractures. Although not our main focus in this analysis, our results also demonstrated improved assessment using volumetric BMD. While average values for the load-to-strength ratio were much higher (worse) for fracture than nonfracture men, it appears that of the biomechanical variables investigated in this analysis, strength was the most robust predictor.

These results provide new insight into the etiology of spine fractures. The high correlation between strength and volumetric BMD indicates that much of the association of strength with fracture risk in this study can be ascribed to interindividual variations in volumetric BMD. This is an insightful etiologic finding for the spine because it indicates that average volumetric density dominated the improvement of the strength-based fracture risk assessment over areal BMD, as opposed to, for example, more subtle factors such as 3D vertebral shape or intraspecimen variations in BMD. This is not to say that geometry and spatial variation in density is not important for vertebral strength. We focused on measurement of L1 vertebral strength in this study, but the fractures were recorded anywhere in the thoracic and lumbar spine. If geometric information implicitly embedded in the measure of L1 strength does not adequately reflect the geometry of the vertebral level at which actual fracture occurs, it is possible that that geometric information could actually confound the prediction. However, although we found that vertebral strength performed at least as well as volumetric BMD at prediction of vertebral fracture, the geometry information within the L1 vertebral strength metric did not confound the fracture prediction. Our finding that strength tended to perform more robustly than the load-to-strength ratio was somewhat surprising, particularly since we found comparable performance of strength and the load-to-strength ratio in our prior study of vertebral fractures in women.10, 14 This finding suggests that the role of loading on vertebral fracture risk may differ between men and women, and further studies are required to address this open issue.

Despite the new insight provided by these results, it remains to be seen how the improvements found here translate into clinical improvements in terms of identifying patients at highest risk of fracture. Whereas it was beyond the scope of this first BCT study of the spine on this cohort, recent types of reclassification analysis might be well suited for such purposes.22, 23 This is because the traditional AUC analysis used in our study is rather broad for applications such as osteoporosis for which clinical classifications relevant to treatment decisions are made only over a narrow range of (high) specificity. In the meanwhile, the data shown in Figure 1 suggest some possibilities. For example, for the 20 men who fractured and who also had a strength value of less than about 3500 N, 10 of them had a BMD T-score below −2.5 (“osteoporotic”) and 10 of them had a BMD T-score between −1.0 and −2.5 (“osteopenic”). Whereas we are not advocating here that this specific strength value be used as a clinical cut point, these data nevertheless suggest that men with the very lowest values of L1 vertebral strength may have a high risk of vertebral fracture even if classified as osteopenic based on traditional areal BMD criteria. Consistent with this trend for individuals, on average, the hazard ratio analysis indicated that strength and volumetric BMD were predictors independent of areal BMD, and when the hazard ratios were broken down by quartile (Table 4), relative hazards in the highest-risk quartiles were much higher for volumetric BMD and the biomechanical outcomes than for areal BMD.

This study design had a number of novel features compared to other studies9, 10 that used BCT to assess vertebral fracture risk. Most uniquely, our study was prospective in nature, predicting new fractures in a fully blinded manner. We maximized statistical power for the given sample size by focusing on “clinical” spine fractures; ie, those that presented clinically and that were confirmed radiographically. However, it is not clear how our results would apply to radiographically-defined deformities, although it appears that inclusion of mild deformities would likely dilute the predictive ability, both of DXA and BCT.10 Our study design is somewhat limited in that the small sample size—due to both the relatively small number of fractures and the use of a randomly sampled subset of the cohort—produces wide CIs, diminishing our ability to detect significance differences; eg, between the age-adjusted hazard ratios for volumetric BMD versus areal BMD. These study-design issues can be addressed in the future for the MrOS cohort when more clinical and all deformity fractures become available for analysis.

In generalizing our results, we note that our cohort included only men, all of whom were older than 65 years and most of whom were white, and that our study is the only one currently available for BCT assessment of vertebral fracture in men. Additional studies are therefore required to confirm these results for other cohorts of men. For women, there are no prospective BCT spine studies available yet. Given the high statistical significance of the results observed here, particularly the high hazard ratio after including areal BMD in the hazards model, and given results from multiple BCT studies that consistently show an advantage of either BCT or volumetric BMD parameters over areal BMD for assessment of prevalent vertebral fractures in women,9, 10 it is reasonable to hypothesize that BCT would also predict new clinical vertebral fractures in women, although this remains to be demonstrated. Regarding race, although the MrOS population does include some nonwhite men, there were few fractures in nonwhite men in our sample and whether the current results are applicable in nonwhite populations also requires confirmation in other studies. However, compared to areal BMD, BCT-derived vertebral strength did improve assessment of prevalent fractures in Japanese women.24

From a technical perspective, the finite element models, although detailed, do provide only estimates of actual vertebral strength. Whereas our estimates of compressive strength were well-validated by biomechanical testing of cadaver vertebrae (Fig. 1), and whereas our technique is in good general agreement with finite element predictions made from much higher resolution micro-CT–based finite element models,25 the evolving clinical BCT technology does allow room for improvement. For example, model predictions might be improved by using alternative strategies for constitutive modeling of the bone,6 by including such additional biomechanical features as bending type loads6, 26 or patient-specific descriptions of kyphosis and musculature, by inclusion of a disc instead of polymethylmethacrylate,27, 28 or even by patient-specific modeling of the disc.29 Further, evaluation of such other measures as the volumetric BMD of the trabecular or cortical compartments10, 30 and the strengths associated with these compartments7 was beyond the scope of this initial study, but such measures may improve prediction,10 as might combining strength and other measures in a multivariate statistical-based risk model. Due to limited spatial resolution, any DXA- or (clinical) CT-based assessment provides no information on such microscale effects as microstructure,31, 32 mineral characteristics,33 microdamage,34 resorption cavities,35, 36 or collagen cross-linking.37, 38

Since the BCT technology is still evolving, we caution that absolute values reported here for vertebral strength, and even for volumetric BMD, may be different if different software is used due to different assumptions made in the finite element analysis and different region-of-interest volumes sampled in the volumetric BMD measurement. The software used in this analysis was developed at UC Berkeley for this specific research study and was not optimized for robust clinical use. As a result, we did not analyze about 15% of the original scans due to various types of imaging artifacts and technical problems. Whereas this loss rate partly represents the inevitable challenges of early adoption of CT scanning and cross-calibration in a large multicenter study such as MrOS, it also signals a challenge if using BCT in a heterogeneous clinical setting.

In summary, we found that vertebral strength, other BCT outcomes, and volumetric BMD were strongly associated with the risk of new clinical vertebral fractures in elderly men, alone and after accounting for age, BMI, and areal BMD. A large part of the predictive ability of vertebral strength was associated with variations in volumetric BMD. Overall, compared to areal BMD by DXA, vertebral compressive strength and volumetric BMD consistently improved vertebral fracture risk assessment in this multisite study of elderly men.


Dr. Keaveny has a financial interest in O.N. Diagnostics and both he and the company may benefit from the results of this work. All other authors state that they have no conflicts of interest.


This study was supported by NIH AR49828 and AR43784 (NIAMS). The Osteoporotic Fractures in Men (MrOS) Study is supported by the following institutes at NIH: NIAMS, NIA, NCCR, and NIH Roadmap for Medical Research under the following grant numbers: U01 AR45580, U01 AR45614, U01 AR45632, U01 AR45647, U01 AR45654, U01 AR45583, U01 AR052234, U01 AG18197, U01-AG027810, and UL1 RR024140. The funding agencies had no role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript. We thank Sabrina Cheng, Jason Lee, Robyn Shaffer, and Simon Xu for their work on analyzing the CT scans and Lynn M. Marshall, PhD, for helpful discussions.

Authors' roles: Study design: EO, DB, and TMK. Study conduct: XW, AS, MJ, JC, LP, and TMK. Data collection: XW, AS, MJ, and JC. Data analysis: DB and LP. Data interpretation: PMC, KEE, SRC, EO, DB, and TMK. Drafting manuscript: XW and TMK. Revising manuscript content: XW, AS, PMC, LP, KEE, SRC, EO, DB, and TMK. Approving final version of manuscript: XW, AS, PMC, LP, MJ, JC, KEE, SRC, EO, DB, and TMK. TMK takes responsibility for the integrity of the data analysis.

Appendix A

A cadaver study was performed to validate the BCT-derived estimates of vertebral compressive strength against experimentally measured values from biomechanical testing. We analyzed 52 cadaver vertebral bodies (T7–L4), each bone from a separate cadaver (age 67.1 ± 11.8 years; range, 20–87 years; 25F, 27M). All vertebral bodies were separated from surrounding tissue, had their posterior elements removed, and were then scanned with quantitative CT while submerged in a bath of water (Philips Mx8000 IDT 16 CT scanner, 120 kV, 200 mA, “bone” kernel at 1-mm slice thickness; Philips Medical Systems NA, Bothell, WA, USA). After imaging, the vertebral bodies were molded to endplates using a 1–3-mm layer of polymethylmethacrylate (PMMA) and compressed at a displacement rate of 0.15 mm/s to beyond the ultimate point (Instron Model 5583; Instron Corporation, Canton, MA, USA) following a protocol similar to that described elsewhere.39 The ultimate strength was taken as the first obvious local maximum force observed from the force-deformation curve. Finite element models were created from each scan, according to the methods used in our clinical study. Results indicated a strong correlation between experiment and model, with statistical Y = X type of agreement (Fig. 1).

Appendix B

Investigators in the Osteoporotic Fractures in Men (MrOS) Research Group: Coordinating Center (California Pacific Medical Center Research Institute and University of California, San Francisco): SR Cummings (Principal Investigator), DC Bauer (co-Investigator), DM Black (co-Investigator), PM Cawthon (co-Investigator), MC Nevitt (co-Investigator), KL Stone (co-Investigator), R Fullman (Project Director), R Benard, T Blackwell, A Chau, L Christianson, L Concepcion, J Diehl, S Ewing, M Farrell, C Fox, S Hoffland, J Ireland, M Jaime-Chavez, E Kwan, SL Harrison, W Liu, LY Lui, A Mills, C Navy, L Nusgarten, L Palermo, N Parimi, L Perreault, K Peters, M Rahorst, CA Schafer, Schambach, J Schneider, R Scott, D Tanaka, C Yeung; Administrative Center (Oregon Health & Sciences University): E Orwoll (Principal Investigator), J Lapidus (co-Investigator), C Lee (co-Investigator), C Pedersen (Project Director), M Abrahamson, L Masterfield, C Nielson, Y Wang, S Shrestha; University of Alabama, Birmingham: J Shikany (Principal Investigator), CE Lewis (co-Investigator), P Johnson (Project Director), M Young, N Webb, S Felder, J King, T Johnsey, C Collier, K Hardy, J Smith, S Anderson; University of Minnesota: K Ensrud (Principal Investigator), H Fink (co-Investigator), N Nelson (Clinic Coordinator), P Van Coevering (Program Director), S Fillhouer (Project Director), R Andrews, M Forseth, RK Jacobson, S Luthi, K Moen, M Paudel; Stanford University: M Stefanick (Principal Investigator), A Hoffman (co-Investigator), N Ellsworth, K Kent, S Belding, A Krauss; University of Pittsburgh: J Cauley (Principal Investigator), J Zmuda (co-Investigator), M Danielson (Study Administrator), L Harper (Project Director), L Buck (Clinic Coordinator), D Cusick, M Gorecki, C Newman; University of California, San Diego: E Barrett-Connor (Principal Investigator), T Dam (co-Investigator), ML Carrion-Petersen (Project Director), P Miller, N Kamantigue, K Marksbury Jappe, M Stephens.