Abstract

Skeletal malocclusion can present early in childhood and, in some cases, progress into adulthood. If the malocclusion or dentofacial deformity is severe enough, surgical correction through orthognathic surgery is inevitable. The ability to accurately predict the direction and magnitude of craniofacial growth is very limited and based on empirical knowledge. Growth is a key consideration when clinically managing skeletal malocclusion, as it has a direct impact on the timing and method of treatment. Without tools to predict or assess the risk of developing a severe malocclusion, the current practice is to “wait and watch” until growth is complete, and the malocclusion has completely manifested itself. This is frustrating to the patient, orthodontist, and oral surgeon. However, the growth of the cranial base is completed first, followed by the face and mandible, and there are indications that there are morphological and developmental correlations between these structures. This is based on 2-dimensional data with conflicting literature. Moreover, the role of genetics in many malocclusion types has been validated1 and the prevalence of inherited dentofacial deformities in families is well-known. Thus, it may be possible to predict craniofacial growth based on overall 3-dimensional craniofacial shape, particularly in families with skeletal malocclusions. The aims of this study are to establish the distinct 3D geometric morphometric variations between the 3 types of skeletal malocclusion (Angle's classification), demonstrate that this variation is largely attributed to the shape of the cranial base, and confirm feasibility in identifying at-risk craniofacial shape variation in a family trio.In total, 300 CBCTs from a curated dataset of Class I, II, and III healthy (non-syndromic) controls were used for 3D geometric morphometric analysis (Procrustes Superimposition, Principal Components and Partial-Least Square [PLS] regression analyses) to compare the shape of cranial base, face, and mandible between the 3 Classes; mean age 24.8 (14-58) years, 56% females. Validated 3D landmarks2 were applied to the CBCTs, P < .05 statistically significant. The CBCTs were obtained on Planmeca Promax with consistent parameters. Pre- and postoperative CBCTs from a 17-year-old female with skeletal Class III and those of her unaffected parents (53 and 56 years old) were analyzed against the controls. All participants were consented onto and CBCTs gathered through IRB-approved protocols.The 3 skeletal Classes segregated distinctly with the first 3 PCs, explaining 38.7% of variation. In the PLS analysis, integration and correlation were present between cranial base shape, face, and mandible with differences between Angle's classification (P = .001). In the family trio, the pre- and postoperative craniofacial shape of the proband remained within the Class III cluster, despite the normal postoperative jaw relationships. Moreover, the father appeared on the same plot as a Class III outlier, despite his normal clinical phenotype. The mother clustered within the Class II group consistent with her clinical phenotype (Image 1).The authors demonstrate that overall craniofacial shape segregates based on the 3 skeletal malocclusion types and that the cranial base shape correlates with the position of the face and mandible. Thus, the cranial base may be a significant driver in the development of a skeletal malocclusion. In addition, the position of the postoperative proband shape within the Class III cluster confirms that the cranial base contributes to the Class III phenotype even after surgical correction of jaw relationship. Finally, the shape association between the proband and her father is promising and indicates that craniofacial shape of parents will contribute to the development of more accurate growth prediction models.

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