Alveolar bone changes and root resorption in adult patients undergoing mandibular molar distalization with clear aligners: a retrospective CBCT study

This retrospective study aims to determine the changes in alveolar bone height, thickness, and root resorption after mandibular molar distalization (MMD) using clear aligner therapy (CAT). A total of 92 distalized mandibular molars, corresponding to twenty-three adult patients (15 females and 8 males), were included. Conebeam computed tomography (CBCT) images were evaluated for bone height, bone thickness, and root length. Paired-sample t-test and the Wilcoxon signed-rank test were used to compare the changes before and after molar distalization. Bivariate Pearson correlation analysis was conducted to assess the relationship between age and treatment duration. Multiple linear regression (MLR) was applied to predict the alveolar bone loss and root resorption based on age and duration of treatment. Significant alveolar bone height reduction was observed on the lingual side of the left second molar (L-2nd M) and the buccal side of the right first molar (R-1st M) (L-2nd M = 1.29 ± 1.14, 1.93 ± 1.22, P < 0.001; R-1stM = 1.80 ± 0.74 2.69 ± 1.36;, P < 0.001). Furthermore, root length reduction was significantly different in the L-2nd M (3.61 ± 1.34, 4.49 ± 1.54, P < 0.001) and the R-2nd M (3.06 ± 0.97, 4.10 ± 1.61, P < 0.001). However, bone thickness showed site-specific changes. Pearson correlation analysis revealed no significant correlation between the age and treatment duration across all the distalized molars. MLR demonstrated that age and treatment duration are weak predictors of post-treatment changes. significant alveolar bone height reduction and thickness changes were observed in mandibular molars after distalization with clear aligners, alongside weak associations with age and treatment duration. These findings enhance the understanding the changes that occur in periodontal tissues after MMD using CAT. Furthermore, these findings highlight the need for further research for better understand of these changes and improve of predictive factors.