Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/DB8E0480-9D91-4168-9A82-FE62483FAE2E | ||
| Year | 2007 | ||
| Type of Item | Conference Full Paper | ||
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| Bibliographic Citation | V. Moustakis, M.L. Laine, L. Koumakis, G. Potamias, L. Zampetakis, B.G. Loos. (2007, Jul.). Modeling Genetic Susceptibility: a case study in periodontitis. Presented at 11th Conference on Artificial Intelligence in Medicine. [Online]. Available: http://www.researchgate.net/publication/254898521_Modelling_genetic_susceptibility_a_case_study_in_periodontitis | ||
| Appears in Collections |
Chronic inflammatory diseases like periodontitis have a complex pathogenesis and a multifactorial etiology, involving complex interactions between multiple genetic loci and infectious agents. We aimed to investigate the influence of genetic polymorphisms and bacteria on chronic periodontitis risk. We determined the prevalence of 12 singlenucleotide polymorphisms (SNPs) in immune response candidate genes and 7 bacterial species of potential relevance to periodontitis etiology, in chronic periodontitis patients and non-periodontitis control individuals (N = 385). Using decision tree analysis, we identified the presence of bacterial species Tannerella forsythia, Porphyromonas gingivalis, ggregatibacter actinomycetemcomitans, and SNPs TNF-857 and IL-1A-889 as discriminators between periodontitis and non-periodontitis. The model reached an accuracy of 80%, sensitivity of 85%, specificity of 73%, and AUC of 73%. This pilot study shows that, on the basis of 3 periodontal pathogens and SNPs, patterns may be ecognized to identify patients at risk for periodontitis. Modern bioinformatics tools are valuable in modeling the multifactorial and complex nature of periodontitis.
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