Modeling Susceptibility to Periodontitis

• Published in: Journal of dental research Vol. 92; no. 1; pp. 45 - 50
• Main Authors: Laine, M.L., Moustakis, V., Koumakis, L., Potamias, G., Loos, B.G.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-01-2013; SAGE PUBLICATIONS, INC
• Subjects: Accuracy; Adult; Aged; Aggregatibacter actinomycetemcomitans - physiology; Algorithms; Alveolar Bone Loss - genetics; Alveolar Bone Loss - microbiology; Area Under Curve; Bacteroides - physiology; Bioinformatics; Chronic Periodontitis - genetics; Chronic Periodontitis - microbiology; Computational Biology; Cytokines; Decision Support Techniques; Decision Trees; Dentistry; Disease; Etiology; Female; Genes; Genes, MHC Class II - genetics; Genetic Predisposition to Disease - genetics; Gram-Negative Bacteria - physiology; Gum disease; Humans; Immune response; Inflammatory diseases; Interleukin 1; Interleukin-1alpha - genetics; Male; Middle Aged; Models, Genetic; Periodontitis; Pilot Projects; Polymorphism, Single Nucleotide - genetics; Porphyromonas gingivalis - physiology; Risk Factors; Sensitivity and Specificity; Single-nucleotide polymorphism; Studies; Teeth; Tumor necrosis factor; Tumor Necrosis Factor-alpha - genetics; Young Adult; microbiology; disease susceptibility; single-nucleotide polymorphism (SNP); genetic polymorphism; periodontal disease; bioinformatics
• ISSN: 0022-0345; 1544-0591
• DOI: 10.1177/0022034512465435

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 single-nucleotide 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, Aggregatibacter 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 recognized to identify patients at risk for periodontitis. Modern bioinformatics tools are valuable in modeling the multifactorial and complex nature of periodontitis.