Aggressive periodontitis defined by recursive partitioning analysis of immunologic factors

Personalized Medicine Based on the Pathogenesis and Risk Assessment of Endodontic-Periodontal Lesions

Endodontic-periodontal lesions (EPLs) are chronic inflammatory lesions in the mouth caused by multiple factors. Both periapical and marginal periodontitis are characterized by infection and inflammation around the affected teeth, suggesting that the theory of complex systems might describe the progression of EPL. The diagnosis and treatment of EPLs are complicated by variations of this condition and difficulties distinguishing EPLs from other diseases. Technological advances in diagnostic and treatment methods, including cone beam computed tomography, microscopy, mineral trioxide aggregates, and periodontal regenerative treatment, have improved outcomes, even in untreatable teeth. However, treating EPLs with iatrogenic problems and/or severe periodontitis remains challenging. Assessing the risk of each EPL based on the possible pathogenesis of each EPL is essential for determining individualized treatment and optimizing personalized medicine for individual patients.

Diagnosis of Papillon-Lefèvre syndrome: review of the literature and a case report

Papillon-Lefëvre syndrome (PLS), classified as ectodermal dysplasia, is an autosomal recessive condition related to the cathepsin C (CTSC) gene mutation. The first clinical symptoms, occurring most commonly between the ages of 1 and 4, are palmoplantar hyperkeratosis and also periodontitis resulting in the loss of most or all teeth in the same sequence in which they erupted. Most often the redness of palms and soles precede the occurrence of keratoderma. Moreover, excessive sweating, moderate mental retardation, the tendency to purulent skin and internal organs infection may occur. Lack of cathepsin seems to have a crucial role in the intensity of symptoms. In most of the patients, there can be observed impairment of phagocytosis and chemotaxis of neutrophils, granulocytes, leukocytes and cytotoxic lesion of fibroblasts and macrophages. Also, functional impairment of lymphocytes, neutrophils, and monocytes is observed. The study, using flow cytometry, showed a decreased percentage of T cells CD8+ and increased CD4:CD8 ratio.

The role of inflammation and genetics in periodontal disease

Periodontitis is a complex disease: (a) various causative factors play a role simultaneously and interact with each other; and (b) the disease is episodic in nature, and bursts of disease activity can be recognized, ie, the disease develops and cycles in a nonlinear fashion. We recognize that various causative factors determine the immune blueprint and, consequently, the immune fitness of a subject. Normally, the host lives in a state of homeostasis or symbiosis with the oral microbiome; however, disturbances in homeostatic balance can occur, because of an aberrant host response (inherited and/or acquired during life). This imbalance results from hyper- or hyporesponsiveness and/or lack of sufficient resolution of inflammation, which in turn is responsible for much of the disease destruction seen in periodontitis. The control of this destruction by anti-inflammatory processes and proresolution processes limits the destruction to the tissues surrounding the teeth. The local inflammatory processes can also become systemic, which in turn affect organs such as the heart. Gingival inflammation also elicits changes in the ecology of the subgingival environment providing optimal conditions for the outgrowth of gram-negative, anaerobic species, which become pathobionts and can propagate periodontal inflammation and can further negatively impact immune fitness. The factors that determine immune fitness are often the same factors that determine the response to the resident biofilm, and are clustered as follows: (a) genetic and epigenetic factors; (b) lifestyle factors, such as smoking, diet, and psychosocial conditions; (c) comorbidities, such as diabetes; and (d) local and dental factors, as well as randomly determined factors (stochasticity). Of critical importance are the pathobionts in a dysbiotic biofilm that drive the viscious cycle. Focusing on genetic factors, currently variants in at least 65 genes have been suggested as being associated with periodontitis based on genome-wide association studies and candidate gene case control studies. These studies have found pleiotropy between periodontitis and cardiovascular diseases. Most of these studies point to potential pathways in the pathogenesis of periodontal disease. Also, most contribute to a small portion of the total risk profile of periodontitis, often limited to specific racial and ethnic groups. To date, 4 genetic loci are shared between atherosclerotic cardiovascular diseases and periodontitis, ie, CDKN2B-AS1(ANRIL), a conserved noncoding element within CAMTA1 upstream of VAMP3, PLG, and a haplotype block at the VAMP8 locus. The shared genes suggest that periodontitis is not causally related to atherosclerotic diseases, but rather both conditions are sequelae of similar (the same?) aberrant inflammatory pathways. In addition to variations in genomic sequences, epigenetic modifications of DNA can affect the genetic blueprint of the host responses. This emerging field will yield new valuable information about susceptibility to periodontitis and subsequent persisting inflammatory reactions in periodontitis. Further studies are required to verify and expand our knowledge base before final cause and effect conclusions about the role of inflammation and genetic factors in periodontitis can be made.

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".

Mathematical modeling suggests that periodontitis behaves as a non-linear chaotic dynamical process

BACKGROUND

This study aims to expand on a previously presented cellular automata model and further explore the non-linear dynamics of periodontitis. Additionally the authors investigated whether their mathematical model could predict the two known types of periodontitis, aggressive (AgP) and chronic periodontitis (CP).

METHODS

The time evolution of periodontitis was modeled by an iterative function, based on the hypothesis that the host immune response level determines the rate of periodontitis progression. The chaotic properties of this function were investigated by direct iteration, and the model was validated by immunologic and clinical parameters derived from two clinical study populations.

RESULTS

Periodontitis can be described as chaos with the level of the host immune response determining its progression rate; the dynamics of the proposed model suggest that by increasing the host immune response level, periodontitis progression rate decreases. Renormalization transformations show the presence of two overlapping zones of disease activity corresponding to AgP and CP. By k-means cluster analysis, immunologic parameters corroborated the findings of the renormalization transformations. Periodontitis progression rates are modeled to scale with a power law of 1.3, and the mean exponential speed of the system is found to be 1.85 (metric entropy); clinical datasets confirmed the mathematical estimates.

CONCLUSIONS

This study introduces a mathematical model that identifies periodontitis as a non-linear chaotic process. It offers a quantitative assessment of the disease progression rate and identifies two zones of disease activity that correspond to the existing classification of periodontitis in the AgP and CP types.