Magda Feres1, Yoram Louzoun2,3, Simi Haber2, Marcelo Faveri1, Luciene C Figueiredo1, Liran Levin4. 1. Department of Periodontology, Guarulhos University, Guarulhos, SP, Brazil. 2. Department of Mathematics, Bar-Ilan University, Ramat-Gan, Israel. 3. The Leslie and Susan Gonda (Goldschmied) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel. 4. Division of Periodontology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Abstract
BACKGROUND: The existence of specific microbial profiles for different periodontal conditions is still a matter of debate. The aim of this study was to test the hypothesis that 40 bacterial species could be used to classify patients, utilising machine learning, into generalised chronic periodontitis (ChP), generalised aggressive periodontitis (AgP) and periodontal health (PH). METHOD: Subgingival biofilm samples were collected from patients with AgP, ChP and PH and analysed for their content of 40 bacterial species using checkerboard DNA-DNA hybridisation. Two stages of machine learning were then performed. First of all, we tested whether there was a difference between the composition of bacterial communities in PH and in disease, and then we tested whether a difference existed in the composition of bacterial communities between ChP and AgP. The data were split in each analysis to 70% train and 30% test. A support vector machine (SVM) classifier was used with a linear kernel and a Box constraint of 1. The analysis was divided into two parts. RESULTS: Overall, 435 patients (3,915 samples) were included in the analysis (PH = 53; ChP = 308; AgP = 74). The variance of the healthy samples in all principal component analysis (PCA) directions was smaller than that of the periodontally diseased samples, suggesting that PH is characterised by a uniform bacterial composition and that the bacterial composition of periodontally diseased samples is much more diverse. The relative bacterial load could distinguish between AgP and ChP. CONCLUSION: An SVC classifier using a panel of 40 bacterial species was able to distinguish between PH, AgP in young individuals and ChP.
BACKGROUND: The existence of specific microbial profiles for different periodontal conditions is still a matter of debate. The aim of this study was to test the hypothesis that 40 bacterial species could be used to classify patients, utilising machine learning, into generalised chronic periodontitis (ChP), generalised aggressive periodontitis (AgP) and periodontal health (PH). METHOD: Subgingival biofilm samples were collected from patients with AgP, ChP and PH and analysed for their content of 40 bacterial species using checkerboard DNA-DNA hybridisation. Two stages of machine learning were then performed. First of all, we tested whether there was a difference between the composition of bacterial communities in PH and in disease, and then we tested whether a difference existed in the composition of bacterial communities between ChP and AgP. The data were split in each analysis to 70% train and 30% test. A support vector machine (SVM) classifier was used with a linear kernel and a Box constraint of 1. The analysis was divided into two parts. RESULTS: Overall, 435 patients (3,915 samples) were included in the analysis (PH = 53; ChP = 308; AgP = 74). The variance of the healthy samples in all principal component analysis (PCA) directions was smaller than that of the periodontally diseased samples, suggesting that PH is characterised by a uniform bacterial composition and that the bacterial composition of periodontally diseased samples is much more diverse. The relative bacterial load could distinguish between AgP and ChP. CONCLUSION: An SVC classifier using a panel of 40 bacterial species was able to distinguish between PH, AgP in young individuals and ChP.
Authors: Bruno Rescala; Wilson Rosalem; Ricardo P Teles; Ricardo G Fischer; Anne D Haffajee; Sigmund S Socransky; Anders Gustafsson; Carlos Marcelo Figueredo Journal: J Periodontol Date: 2010-09 Impact factor: 6.993
Authors: Paul E Kolenbrander; Robert J Palmer; Alexander H Rickard; Nicholas S Jakubovics; Natalia I Chalmers; Patricia I Diaz Journal: Periodontol 2000 Date: 2006 Impact factor: 7.589
Authors: Loreto Abusleme; Amanda K Dupuy; Nicolás Dutzan; Nora Silva; Joseph A Burleson; Linda D Strausbaugh; Jorge Gamonal; Patricia I Diaz Journal: ISME J Date: 2013-01-10 Impact factor: 10.302
Authors: Sigmund S Socransky; Anne D Haffajee; Ricardo Teles; Jan L Wennstrom; Jan Lindhe; Anna Bogren; Hatice Hasturk; Thomas van Dyke; Xiaoshan Wang; Jo Max Goodson Journal: J Clin Periodontol Date: 2013-05-27 Impact factor: 8.728
Authors: Maria Muchova; Dario L Balacco; Melissa M Grant; Iain L C Chapple; Sarah A Kuehne; Josefine Hirschfeld Journal: Front Oral Health Date: 2022-03-15