Journal of periodontology · 2026
New periodontal phenotype classification in patients with different types of malocclusion and dental crowding.
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Al-Sosowa Abeer A, Abdulghani Ehab A, Meng Wang, Zhu Jiajun, Ding Pei-Hui
Аннотация
BACKGROUND: The periodontal phenotype plays a critical role in periodontal stability and orthodontic risk assessment. However, an integrated evaluation of soft- and hard-tissue periodontal characteristics across various sagittal and vertical skeletal patterns and dental crowding remains limited. This study applied an innovative cluster-based classification of periodontal phenotype to evaluate these variances.
METHODS: This cross-sectional study included 180 participants and 1080 maxillary anterior teeth. Using superimposed cone beam computed tomography and intraoral scan data, the thickness of the gingiva (GT) and the thickness of the alveolar bone (ABT) was evaluated. The keratinized tissue width (KTW) was obtained from intraoral images utilizing a correction factor. Subjects were classified by sagittal skeletal classification, vertical skeletal patterns, and crowding severity. Periodontal phenotype clusters were assigned using a previously developed classification framework. Statistical analyses included chi-square tests with Monte Carlo simulation for categorical comparisons and multinomial logistic regression to assess associations between cluster membership and potential covariates.
RESULTS: The distribution of periodontal phenotype clusters was significantly associated with the sagittal skeletal classification and vertical skeletal patterns, with Class III and hypodivergent groups being more commonly found in thin or majority-thin clusters. While the dental crowding severity showed no significant correlation with periodontal phenotype.
CONCLUSIONS: The anterior periodontal phenotype is not randomly distributed but rather reflects sagittal and vertical skeletal patterns and is independent of tooth alignment and crowding severity. Class III and hypodivergent patterns are more commonly associated not only with reduced GT but also with narrow KTW and reduced ABT, which highlights the importance of phenotype-guided risk assessment in orthodontic planning.
METHODS: This cross-sectional study included 180 participants and 1080 maxillary anterior teeth. Using superimposed cone beam computed tomography and intraoral scan data, the thickness of the gingiva (GT) and the thickness of the alveolar bone (ABT) was evaluated. The keratinized tissue width (KTW) was obtained from intraoral images utilizing a correction factor. Subjects were classified by sagittal skeletal classification, vertical skeletal patterns, and crowding severity. Periodontal phenotype clusters were assigned using a previously developed classification framework. Statistical analyses included chi-square tests with Monte Carlo simulation for categorical comparisons and multinomial logistic regression to assess associations between cluster membership and potential covariates.
RESULTS: The distribution of periodontal phenotype clusters was significantly associated with the sagittal skeletal classification and vertical skeletal patterns, with Class III and hypodivergent groups being more commonly found in thin or majority-thin clusters. While the dental crowding severity showed no significant correlation with periodontal phenotype.
CONCLUSIONS: The anterior periodontal phenotype is not randomly distributed but rather reflects sagittal and vertical skeletal patterns and is independent of tooth alignment and crowding severity. Class III and hypodivergent patterns are more commonly associated not only with reduced GT but also with narrow KTW and reduced ABT, which highlights the importance of phenotype-guided risk assessment in orthodontic planning.