Phenotypic Characterization of Skeletal Class III Malocclusion Using Principal Component Analysis and Cluster Analysis
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DOI:
https://doi.org/10.3126/ojn.v14i1.62788Keywords:
Class III malocclusion, Cluster analysis, Multivariate reduction analyses, Phenotypes, Principal component analysisAbstract
Background: Skeletal Class III malocclusion is characterized by several dentofacial deformities linked to environmental and genetic causes. It constitutes a clinical obstacle due to an insufficient understanding of its origins. Thus, proper classification and definition are key to diagnosing and treating this malocclusion correctly.
Objective: This study aimed to identify sub-clusters of skeletal Class III malocclusion in a group of Yemeni adults using multivariate reduction analyses.
Design: This cross-sectional prospective study was conducted at the Orthodontic Graduate Clinics, Faculty of Dentistry, Sana`a University, Sana`a, Yemen.
Material and Methods: This study included lateral cephalometric radiographs of 144 Yemeni adults (67 males and 77 females, mean age 29 years) with true Class III skeletal malocclusion ranging from mild to severe. A total of 62 measurements were used to perform principal component analyses and subsequent cluster analyses.
Results: Eight principal components were identified and represented 79.4% of the variance. The fist three main components, which described vertical and sagittal variables as significant descriptors, explained 50.8% of the variance. Cluster analysis identified 5 phenotypic subclusters. Cluster 1 denoted a mild Class III phenotype. Cluster 2 displayed a vertical phenotype with a steep mandibular plane. Cluster 3 displayed a phenotype characterized by a purely severe mandibular prognathism. Cluster 4 showed a severe maxillary retrusion phenotype. Cluster 5 displayed a severe maxillary deficiency with a severe mandibular protrusion.
Conclusion: There was a significant variance demonstrated among sub-phenotypes of a selected group of adult Yemeni Class III populations. Based on these results, further genetic investigations will enable us to uncover the etiological genes associated with each sub-cluster.
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