This review addresses the multi-causal etiology of periodontitis, in which genetic factors play a role. The various proposed causes for periodontitis always play a role simultaneously, but the relative contribution of each of these, varies from case to case. In young individuals often with aggressive periodontitis (AgP) a stronger contribution from genetic factors is apparent, while in older individuals often with chronic periodontitis (CP), the relative contribution of the established subgingival biofilms (environmental factors) and life style factors (e.g. smoking, stress, diet), play a more dominant role in the phenotype of the disease.Nevertheless always some genetic susceptibility is present, for CP estimated at 25%.Periodontitis is therefore a complex disease, i.e. it behaves in a nonlinear fashion. Actually the disease progression rate fluctuates, where the disease sometimes moves into an aberrant state of host response and then swings back into a resolving state; in between, a settlement zone is present where essentially no differences are found for immunological parameters between cases with periodontitis and healthy controls. The genotype determines part of this fluctuation and the extent of it. The disease is polygenic, i.e. multiple genetic variants in multiple genes determine the phenotype, but there are individual and ethnic differences in the genes involved. We are still at the early stage to have identified the involved genes, in comparisons to other chronic diseases, we need to count on it that at least 100 causative genes across various global populations exist in AgP and CP. To date, the genetic variations firmly and repeatedly associated with periodontitis in some populations are found within the following genes: ANRIL, COX2, IL1, IL10, DEFB1, while many proposed periodontitis candidate genes have not been firmly proven or replicated. 3 KeypointsPeriodontitis is a multi-causal disease, with each of the causal factors playing a role but the relative contribution of these vary form case to case.The disease behaves in a nonlinear fashion, with periods of aberrant host response to periods within an active disease resolving state.To date only a few of the multitude of possible genetic factors for periodontitis are identified. Outline

The present study on a small group of patients treated for advanced periodontal disease and well maintained over 5 to 8 years showed no statistically significant differences between smokers and non-smokers in clinical probing depth and radiographic bone loss measurements.

There is neither a single clinical, microbiological, histopathological or genetic test, nor combinations of them, to discriminate aggressive periodontitis (AgP) from chronic periodontitis (CP) patients. We aimed to estimate probability density functions of clinical and immunologic datasets derived from periodontitis patients and construct artificial neural networks (ANNs) to correctly classify patients into AgP or CP class. The fit of probability distributions on the datasets was tested by the Akaike information criterion (AIC). ANNs were trained by cross entropy (CE) values estimated between probabilities of showing certain levels of immunologic parameters and a reference mode probability proposed by kernel density estimation (KDE). The weight decay regularization parameter of the ANNs was determined by 10-fold cross-validation. Possible evidence for 2 clusters of patients on cross-sectional and longitudinal bone loss measurements were revealed by KDE. Two to 7 clusters were shown on datasets of CD4/CD8 ratio, CD3, monocyte, eosinophil, neutrophil and lymphocyte counts, IL-1, IL-2, IL-4, INF-γ and TNF-α level from monocytes, antibody levels against A. actinomycetemcomitans (A.a.) and P.gingivalis (P.g.). ANNs gave 90%–98% accuracy in classifying patients into either AgP or CP. The best overall prediction was given by an ANN with CE of monocyte, eosinophil, neutrophil counts and CD4/CD8 ratio as inputs. ANNs can be powerful in classifying periodontitis patients into AgP or CP, when fed by CE values based on KDE. Therefore ANNs can be employed for accurate diagnosis of AgP or CP by using relatively simple and conveniently obtained parameters, like leukocyte counts in peripheral blood. This will allow clinicians to better adapt specific treatment protocols for their AgP and CP patients.

Cellular automata (CA) are time and space discrete dynamical systems that can model biological systems. The aim of this study is to simulate by CA experiments how the disease of periodontitis propagates along the dental root surface. Using a Moore neighborhood on a grid copy of the pattern of periodontal ligament fibers (PDLF) supporting and anchoring the teeth to bone, we investigate the fractal structure of the associated pattern using all possible outer-totalistic CA rules. On the basis of the propagation patterns, CA rules are classified in three groups, according to whether the disease was spreading, remaining constant or receding. These are subsequently introduced in a finite state Markov model as probabilistic "state-rules" and the model is validated using datasets retrieved from previous studies.Based on the maximum entropy production principle, we identified the "state-rule" that most appropriately describes the PDLF pattern, showing a power law distribution of periodontitis propagation rates with exponent 1.3. Entropy rates and mutual information of Markov chains were estimated by extensive data simulation. The scale factor of the PDLF used to estimate the conditional entropy of Markov chains was seen to be nearly equal 1.85. This possibly reflects the fact that a dataset representing tooth percentage with bone loss equal to 50% or more of their root length, is found to have a fractal dimension (FD) of about 1.84. Similarly, datasets of serum neutrophil, basophil, eosinophil, monocyte counts and IgG, IgA, IgM levels taken from periodontitis patients, showed a FD ranging from 1.82 to 1.87.Our study presents the first mathematical model to our knowledge that suggests periodontitis is a nonlinear dynamical process. Moreover, the model we propose implies that the entropy rate of the immune-inflammatory host response dictates the rate of periodontitis progression. This 1350056-1 Int. J. Bifurcation Chaos 2013.23. Downloaded from www.worldscientific.com by ROYAL INSTITUTE OF TECHNOLOGY on 02/02/15. For personal use only. G. Papantonopoulos et al.is validated by clinical data and suggests that our model can serve as a basis for detecting periodontitis susceptible individuals and shaping prognosis for treated periodontitis patients.

Two implant "phenotypes" were identified, one with susceptibility and another with resistance to peri-implantitis. Prediction of IIMBL could be achieved by using six variables.

It is concluded that smoking impairs healing after nonsurgical periodontal therapy. The decision analysis of this study questions the need for a thorough course of non-surgical treatment in smokers with advanced periodontal disease.

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.

Non-linearity of peri-implantitis was evidenced by finding different peri-implant bone levels between two main clusters of implant-treated patients and among six different jaw bone sites. The patient mean peri-implant bone levels were predicted from five variables and confirmed complexity for peri-implantitis.