Our goal was to develop data analytical tools that enable the identification and definition of distinct periodontal profile and tooth profile classes (PPC/TPC) of individuals using detailed clinical measures at the tooth-level, including both periodontal measurements and tooth loss.

Full-mouth clinical periodontal measurements (7 indices) from 6,793 subjects from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom Latent Class Analysis (LCA) procedure was developed to identify seven distinct PPC/TPC. Each PPC/TPC was associated with different clinical phenotypes. The NHANES (2009-2010/2011-2012) and the Piedmont study populations were used for validation with total of 7,785 subjects.

LCA method identified members of seven distinct periodontal profile classes (PPC A-G) and seven distinct tooth profile classes (TPC A-G) ranging from health to severe periodontal disease status. The method enabled the identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. PPC algorithm was applied and confirmed in three distinct cohorts.

These findings suggest that periodontal and tooth profile classes using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at a subject and tooth level. These classifications potentially can be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients.