Heterogeneity of Host Immunological Risk Factors in Patients With Aggressive Periodontitis

Takahashi, Keiso; Ohyama, Hideki; Kitanaka, Michitaka; Sawa, Takamasa; Mineshiba, Junji; Nishimura, Fusanori; Arai, Hideo; Takashiba, Shogo; Murayama, Yoji

doi:10.1902/jop.2001.72.4.425

Cited by 43 publications

(83 citation statements)

References 51 publications

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“…The level of host immune-inflammatory response X used in simulations of periodontitis progression was estimated using data files retrieved from three previous studies [Loos et al, 2000;Takahashi et al, 2001;Graswinckel et al, 2004]. For the purpose of the present study we wish to discriminate between chronic periodontitis (CP) and aggressive periodontitis (AgP).…”

Section: Probabilistic Analysismentioning

confidence: 99%

Using Cellular Automata Experiments to Model Periodontitis: A First Step Towards Understanding the Nonlinear Dynamics of the Disease

Papantonopoulos¹,

Takahashi

Bountis

et al. 2013

Int. J. Bifurcation Chaos

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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.

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Section: Probabilistic Analysismentioning

confidence: 99%

Using Cellular Automata Experiments to Model Periodontitis: A First Step Towards Understanding the Nonlinear Dynamics of the Disease

Papantonopoulos¹,

Takahashi

Bountis

et al. 2013

Int. J. Bifurcation Chaos

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“…This is definitely a group of patients with aggressive disease behavior. Takahashi's group [19] used the current classification scheme [5]; however, discrimination of AgP and CP was as much as possible precise, resulting in a group of “suspected” for AgP patients, which contributed to KDE but was not used in ANNs. The other two samples used in this study suffered of a crude method in designating patients into the AgP or CP group.…”

Section: Discussionmentioning

confidence: 99%

“…From Graswinckel et al [18] we used the datasets for IgA, IgM and IgG from 80 periodontitis patients (sample-3). From Takahashi et al [19] (sample-4) we derived data of serum antibody levels in 162 periodontitis patients against Aggregatibacter actinomycetemcomitans ( A.a. ) (Y4 antigen), A.a. (ATCC 29523), A.a. (SUNY67), Porphyromonas gingivalis ( P.g. ) (FDC381), P.g.…”

Section: Methodsmentioning

confidence: 99%

“…The undefined periodontitis patients, those with adult periodontitis (AP) or those with localized (L) or generalized (G) early onset periodontitis (EOP) from the studies from which data were retrieved [16]–[19] were reclassified as previously described [9]. Those with an age at the time of diagnosis >35 years or originally having AP, were reclassified as CP; patients ≤35 years were classified as AgP; those with L- or G-EOP were all classified as AgP.…”

Section: Methodsmentioning

confidence: 99%

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Artificial Neural Networks for the Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters

et al. 2014

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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.

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“…Newer imaging methods like CBCT give more insight into the bony defects which facilitates better treatment planning [28]. Further, serum IgG titer test against each pathogenic bacterium can be done with ELISA as the serum level of IgG directly correlates with the number of pathogenic bacteria in the periodontium [29]. Elimination of bacteria from the periodontal pockets can be assessed with this test which is useful for determining the optimal timing to start orthodontic treatment [30].…”

Section: Role Of Orthodontist In the Management Of Aggressive Perimentioning

confidence: 99%

Orthodontic Management in Aggressive Periodontitis

Gyawali

Bhattarai

2017

International Scholarly Research Notices

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Aggressive periodontitis is a type of periodontitis with early onset and rapid progression and mostly affecting young adults who occupy a large percentage of orthodontic patients. The role of the orthodontist is important in screening the disease, making a provisional diagnosis, and referring it to a periodontist for immediate treatment. The orthodontist should be aware of the disease not only before starting the appliance therapy, but also during and after the active mechanotherapy. The orthodontic treatment plan, biomechanics, and appliance system may need to be modified to deal with the teeth having reduced periodontal support. With proper force application and oral hygiene maintenance, orthodontic tooth movement is possible without any deleterious effect in the tooth with reduced bone support. With proper motivation and interdisciplinary approach, orthodontic treatment is possible in patients with controlled aggressive periodontitis.

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Heterogeneity of Host Immunological Risk Factors in Patients With Aggressive Periodontitis

Cited by 43 publications

References 51 publications

Using Cellular Automata Experiments to Model Periodontitis: A First Step Towards Understanding the Nonlinear Dynamics of the Disease

Using Cellular Automata Experiments to Model Periodontitis: A First Step Towards Understanding the Nonlinear Dynamics of the Disease

Artificial Neural Networks for the Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters

Orthodontic Management in Aggressive Periodontitis

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