Clinical oral investigations · 2023
Three-dimensional phenotype characteristics of skeletal class III malocclusion in adult Chinese: a principal component analysis-based cluster analysis.
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Alshoaibi Lina Hassan, Alareqi Mohammed Muneer, Al-Somairi Majedh Abdo Ali, Al-Tayar Barakat, Almashraqi Abeer A, An Xiaoli, Alhammadi Maged Sultan
Аннотация
BACKGROUND: Skeletal class III malocclusion has a diverse and complicated aetiology involving environmental and genetic factors. It is critical to correctly classify and define this malocclusion to be diagnosed and treated on a clinically sound basis. Thus, this study aimed to provide reliable and detailed measurements in a large ethnically homogeneous sample of Chinese adults to generate an adequate phenotypic clustering model to identify and describe the skeletal variation present in skeletal class III malocclusion.
MATERIALS AND METHODS: This is a retrospective cross-sectional study in which 500 pre-treatments cone-beam computed tomography (CBCT) scans of patients with skeletal class III malocclusion (250 males and 250 females) were selected following specific selection criteria. Seventy-six linear, angular, and ratios measurements were three-dimensionally analysed using InVivo 6.0.3 software. These measurements were categorised into 47 skeletal, 18 dentoalveolar, and 11 soft tissue variables. Multivariate reduction methods: principal component analyses and cluster analyses were used to present the most common phenotypic groupings of skeletal class III malocclusion in Han ethnic group of Chinese adults.
RESULTS: The principal component analysis revealed eight principal components accounted for 72.9% of the overall variation of the data produced from the seventy-six variables. The first four principal components accounted for 53.37% of the total variations. They explained the most variation in data and consisted mainly of anteroposterior and vertical skeletal relationships. The cluster analysis identified four phenotypes of skeletal class III malocclusion: C1, 34%; C2, 11.4%; C3, 26.4%; and C4, 28.2%.
CONCLUSION: Based on three-dimensional analyses, four skeletal class III malocclusion distinct phenotypic variations were defined in a large sample of the adult Chinese population, showing the occurrence of phenotypic variation between identified clusters in the same ethnic group. These findings might serve as a foundation for accurate diagnosis and treatment planning of each cluster and future genetic studies to determine the causative gene(s) of each cluster.
MATERIALS AND METHODS: This is a retrospective cross-sectional study in which 500 pre-treatments cone-beam computed tomography (CBCT) scans of patients with skeletal class III malocclusion (250 males and 250 females) were selected following specific selection criteria. Seventy-six linear, angular, and ratios measurements were three-dimensionally analysed using InVivo 6.0.3 software. These measurements were categorised into 47 skeletal, 18 dentoalveolar, and 11 soft tissue variables. Multivariate reduction methods: principal component analyses and cluster analyses were used to present the most common phenotypic groupings of skeletal class III malocclusion in Han ethnic group of Chinese adults.
RESULTS: The principal component analysis revealed eight principal components accounted for 72.9% of the overall variation of the data produced from the seventy-six variables. The first four principal components accounted for 53.37% of the total variations. They explained the most variation in data and consisted mainly of anteroposterior and vertical skeletal relationships. The cluster analysis identified four phenotypes of skeletal class III malocclusion: C1, 34%; C2, 11.4%; C3, 26.4%; and C4, 28.2%.
CONCLUSION: Based on three-dimensional analyses, four skeletal class III malocclusion distinct phenotypic variations were defined in a large sample of the adult Chinese population, showing the occurrence of phenotypic variation between identified clusters in the same ethnic group. These findings might serve as a foundation for accurate diagnosis and treatment planning of each cluster and future genetic studies to determine the causative gene(s) of each cluster.