Association of Condylar Bone Quality with TMJ Osteoarthritis

Abstract

The etiology and treatment of temporomandibular joint (TMJ) osteoarthritis (TMJOA) remain complex and unclear. Based on clinical observations, we hypothesized that low condylar bone quality is significantly correlated with TMJOA and explored this association in a cross-sectional study with human patients. A total of 254 postmenopausal female participants were included in this study. Radiographic findings from cone beam computed tomography (CBCT) and clinical symptoms were used to classify each TMJ data sample as healthy control ( n = 124) or TMJOA ( n = 130). Condylar bone mineral density (BMD) (computed tomography Hounsfield unit [CT HU]) and bone volume fraction (BV/TV) were measured and modeled as predictors of healthy control versus TMJOA status in multilevel logistic regression analyses. Both CT HU (adjusted odds ratio [AOR] = 0.9989, interquartile odds ratio [IOR] = 0.4206) and BV/TV (AOR= 0.8096, IOR = 0.1769) were negatively associated with TMJOA ( P = 0.049, 0.011, respectively). To assess the diagnostic performance of CT HU and BV/TV for identification of TMJOA, receiver operating characteristic (ROC) curves were plotted. The estimated areas under the curve (AUC) were 0.6622 for BV/TV alone, 0.6074 for CT HU alone, and 0.7136 for CT HU and BV/TV together. The model incorporating CT HU and BV/TV together had a significantly higher AUC than the models using BV/TV alone ( P = 0.038) or HU alone ( P = 0.021). In conclusion, we found that low condylar bone quality was significantly correlated with TMJOA development and that condylar CT HU and BV/TV can be used together as a potential diagnostic tool for TMJOA. Careful clinical evaluation of the condyle coupled with appropriate radiographic interpretation would thus be critical for the early detection of TMJOA.

Keywords: bone volume; cone beam computed tomography; cross-sectional study; mandibular condyle; micro-computed tomography; multivariate analysis.

Figure 1.

Flowchart for the selection of the study population. CBCT, cone beam computed tomography; TMD, temporomandibular disorder; TMJ, temporomandibular joint; TMJOA, temporomandibular joint osteoarthritis.

Figure 2.

Representative images and bone quantification of healthy control and TMJOA. (A–D) The representative CBCT and 3D reconstruction images of healthy control (A and B) and TMJOA (C and D). Compared to the healthy control, the TMJOA images (C and D) show condylar head deformity, lower trabecular bone density, and thinner cortical bone. (E–F) The CT HU (E) and BV/TV (F) of TMJOA condyles were significantly lower than that of healthy condyles. (G–H) Predicted probabilities of TMJOA along the ranges of CT HU (G) and BV/TV (H) observed in the data, averaged over all other covariates and with the subject random effect fixed at zero. The green regions indicate the 95% confidence interval. The solid lines (blue) delineate the interval [mean – SD, mean + SD] for healthy control. The dashed lines (yellow) delineate the interval [mean – SD, mean + SD] for TMJOA. These intervals overlap for CT HU but not for BV/TV. TMJOA, temporomandibular joint osteoarthritis; CBCT, cone beam computed tomography; CT HU, computed tomography Hounsfield unit; BV/TV, bone volume fraction; SD, standard deviation. *P < 0.05. **P < 0.01. This figure is available in color online.

Figure 3.

ROC curves for different variables. ROC curves where the classifiers are the fixed-effect predictions (i.e., excluding random effects) from multilevel logistic regression models with the following set of predictors: (1) CT HU alone: gray line, (2) BV/TV alone: green line, (3) CT HU and BV/TV together: red line, and (4) CT HU, BV/TV, and all other confounders: blue line. Based on the sensitivity and specificity of a diagnostic test using ROC curves, the combined model of using CT HU and BV/TV together (AUC = 0.7136) provides significantly better discrimination of case/controls than that of using CT HU (AUC = 0.6074) or BV/TV (AUC = 0.6622) alone. However, adding the additional confounders (AUC = 0.7279) does not significantly improve discrimination over using just CT HU and BV/TV together. AUC, area under the curve; BV/TV, bone volume fraction; CT HU, computed tomography Hounsfield unit; ROC, receiver operating characteristic.