title: Aggressive periodontitis defined by recursive partitioning analysis of immunologic factors.
abstract: BACKGROUND
  The present study aims to extend recent findings of a non-linear model of the progression of periodontitis supporting the notion that aggressive periodontitis (AgP) and chronic periodontitis (CP) are distinct clinical entities. This approach is based on the implementation of recursive partitioning analysis (RPA) to evaluate a series of immunologic parameters acting as predictors of AgP and CP.
  
  
  METHODS
  RPA was applied to three population samples, that were retrieved from previous studies, using 17 immunologic parameters. The mean values of the parameters in control subjects were used as the cut-off points. Leave-one-out cross-validation (LOOCV) prediction errors were estimated in the proposed models, as well as the Kullback-Leibler divergence (DKL) of the distribution of positive results in AgP compared to CP and negative results in CP compared to AgP.
  
  
  RESULTS
  Seven classification trees were derived showing that the relationship of interleukin (IL)-4, IL-1, IL-2 has the highest potential to rule out or rule in AgP. On the other hand, immunoglobulin (Ig)A, IgM used to rule out AgP and cluster of differentiation 4 (CD4)/CD8, CD20 used to rule in AgP showed the least LOOCV cost. Penalizing DKL with LOOCV cost promotes the IL-4, IL-1, IL-2 model for ruling out AgP, whereas the single CD4/CD8 ratio with a lowered discrimination cut-off point was used to rule in AgP.
  
  
  CONCLUSIONS
  Although a test is unlikely to have both high sensitivity and high specificity, the use of immunologic parameters in the right model can efficiently complement a clinical examination for ruling out or ruling in AgP.
authors.affiliation: Department of Conservative Dentistry, School of Dentistry, Ohu University, Fukushima, Japan.
authors.forename: G
authors.initials: G
authors.lastname: Papantonopoulos
authors.forename: K
authors.initials: K
authors.lastname: Takahashi
authors.forename: T
authors.initials: T
authors.lastname: Bountis
authors.forename: B G
authors.initials: BG
authors.lastname: Loos
mesh_terms: D010520:Aggressive Periodontitis / Q000175:diagnosis / Q000276:immunology*; D000465:Algorithms; D018951:Antigens, CD20 / Q000032:analysis; D001491:Basophils / Q000473:pathology; D017252:CD3 Complex / Q000032:analysis; D015704:CD4 Antigens / Q000032:analysis; D016516:CD4-CD8 Ratio; D016827:CD8 Antigens / Q000032:analysis; D055113:Chronic Periodontitis / Q000175:diagnosis / Q000276:immunology*; D057225:Data Mining; D003663:Decision Trees; D004804:Eosinophils / Q000473:pathology; D005544:Forecasting; D006801:Humans; D007070:Immunoglobulin A / Q000032:analysis; D007074:Immunoglobulin G / Q000032:analysis; D007075:Immunoglobulin M / Q000032:analysis; D007155:Immunologic Factors / Q000032:analysis*; D007371:Interferon-gamma / Q000032:analysis; D007375:Interleukin-1 / Q000032:analysis; D007376:Interleukin-2 / Q000032:analysis; D015847:Interleukin-4 / Q000032:analysis; D015850:Interleukin-6 / Q000032:analysis; D007963:Leukocytes, Mononuclear / Q000473:pathology; D009000:Monocytes / Q000473:pathology; D009504:Neutrophils / Q000473:pathology; D014409:Tumor Necrosis Factor-alpha / Q000032:analysis
chemical_list: D018951:Antigens, CD20; D017252:CD3 Complex; D015704:CD4 Antigens; D016827:CD8 Antigens; C508598:IL4 protein, human; C508600:IL6 protein, human; D007070:Immunoglobulin A; D007074:Immunoglobulin G; D007075:Immunoglobulin M; D007155:Immunologic Factors; D007375:Interleukin-1; D007376:Interleukin-2; D015850:Interleukin-6; D014409:Tumor Necrosis Factor-alpha; D015847:Interleukin-4; D007371:Interferon-gamma