Three periodontitis phenotypes: Bone loss patterns, antibiotic-surgical treatment and the new classification.

AIM: To compare three periodontitis clusters (A, B and C) for alveolar bone loss (ABL) patterns, antibiotic prescriptions and surgeries and to relate them to the new classification of periodontitis. MATERIALS AND METHODS: ABL patterns, prescription of systemic antibiotics and the number of surgeries were retrieved for all patients (n = 353) in the clusters. Comparisons and possible predictors for antibiotics were assessed, and results also evaluated in relation to the new classification. RESULTS: Cluster A is characterized by angular defects often affecting the first molars and localized stage III/IV grade C periodontitis. Cluster B contains mainly localized or generalized stage III/IV, grade C patients. Cluster C contains mainly patients with generalized stage III/IV grade C periodontitis. Patients in cluster A received significantly more antibiotics compared to B and C (78% vs. 23% and 17%); the predictors for antibiotic prescription were young age and localized ABL. No differences in numbers of periodontal surgeries were observed between clusters (A = 1.0 ± 1.4, B = 1.3 ± 1.4 and C = 1.3 ± 1.5). CONCLUSIONS: Within stage III/IV grade C periodontitis, we could detect three clusters of patients. The distinct localized ABL pattern and younger age in cluster A presumably prompted clinicians to prescribe antibiotics.

At least three phenotypes exist among periodontitis patients.

AIM: To identify phenotypes of periodontitis patients by the use of an unsupervised modelling technique (clustering), based on pre-treatment radiographic and microbiological characteristics. MATERIALS AND METHODS: This retrospective study included data from 392 untreated periodontitis patients. Co-regularized spectral clustering algorithm was used to cluster the patients. The resulting clusters were subsequently characterized based on their demographics, radiographic bone loss patterns and microbial data. RESULTS: The vast majority of patients fitted into one of the three main clusters (accuracy 90%). Cluster A (n = 18) was characterized by high prevalence and high proportions of Aggregatibacter actinomycetemcomitans, a trend for a more localized pattern of alveolar bone loss and young individuals. Clusters B (n = 200) and C (n = 135) differed clearly in disease severity patterns and smoking habits, but not in microbiological characteristics. CONCLUSION: On the basis of alveolar bone loss patterns and microbiological data, untreated periodontitis patients can be clustered into at least three phenotypes. These results should be validated in other cohorts, and the clinical utility needs to be explored on the basis of periodontal treatment outcomes and/or disease progression.

Hidden noise in immunologic parameters might explain rapid progression in early-onset periodontitis.

To investigate in datasets of immunologic parameters from early-onset and late-onset periodontitis patients (EOP and LOP), the existence of hidden random fluctuations (anomalies or noise), which may be the source for increased frequencies and longer periods of exacerbation, resulting in rapid progression in EOP. Principal component analysis (PCA) was applied on a dataset of 28 immunologic parameters and serum IgG titers against periodontal pathogens derived from 68 EOP and 43 LOP patients. After excluding the PCA parameters that explain the majority of variance in the datasets, i.e. the overall aberrant immune function, the remaining parameters of the residual subspace were analyzed by computing their sample entropy to detect possible anomalies. The performance of entropy anomaly detection was tested by using unsupervised clustering based on a log-likelihood distance yielding parameters with anomalies. An aggregate local outlier factor score (LOF) was used for a supervised classification of EOP and LOP. Entropy values on data for neutrophil chemotaxis, CD4, CD8, CD20 counts and serum IgG titer against Aggregatibacter actinomycetemcomitans indicated the existence of possible anomalies. Unsupervised clustering confirmed that the above parameters are possible sources of anomalies. LOF presented 94% sensitivity and 83% specificity in identifying EOP (87% sensitivity and 83% specificity in 10-fold cross-validation). Any generalization of the result should be performed with caution due to a relatively high false positive rate (17%). Random fluctuations in immunologic parameters from a sample of EOP and LOP patients were detected, suggesting that their existence may cause more frequently periods of disease activity, where the aberrant immune response in EOP patients result in the phenotype "rapid progression".