Human Periodontal Phenotypes: Identification Based on Gingival and Dental Characteristics

Human periodontal phenotypes: Identification based on gingival and dental characteristics

Objectives: It is well documented that periodontal phenotypes are associated with different susceptibility to recession and clinical treatment outcomes. The aim of this clinical investigation was to identify periodontal phenotypes in young adults, using gingival and dental characteristics.

Materials and methods: Systemically and periodontally healthy young adults (18-36 years old) were recruited for the study. Clinical parameters were recorded from teeth #7 and #8, including the width of keratinized gingiva (WKG) and attached gingiva (WAG), and the thickness of gingiva (GTH), measured at midpoint of the labial attached gingiva and at the base of distal interdental papilla. Polyvinylsiloxane impressions of the teeth were used to fabricate stone casts, from which the crown width (CW) and crown length (CL) ratio (CW/CL) was recorded. A clustering analysis was first employed based on all standardized measurements from both teeth. K-means clustering algorithm was applied to all 12 standardized variables. Based on group labels reported from the clustering analysis, Fisher's Linear Discriminates Analysis (LDA) was used to access the importance of variables in classifying subjects.

Results: Ninety-six (96) subjects (F:73, M:23) with an average age of 24 ± 5 years were included in the study. The use of the clustering algorithm resulted in the identification of three groups: Group A (n=29), Group B (n=54) and Group C (n=13). Group A had widest KG and AG, least GTH, and intermediate CW/CL; Group B had narrowest KG and AG, intermediate GTH, and lowest CW/CL; Group C had intermediate WKG and WAG, uppermost GTH, and highest CW/CL. LDA indicated that without GTH related variables, the rest provide 88.5% accurate classification.

Conclusions: Three periodontal phenotypes were identified in young adults by cluster analysis. The elimination of an invasive measure (GTH) does not greatly alter subject classification.