Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles

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.

The combined and individual impact of diabetes and smoking on key subgingival periodontal pathogens in patients with chronic periodontitis

“…A limitation of the current study is that, despite the high sensitivity of real‐time PCR, its cost and time demand restricted the number of target pathogens and samples to be evaluated. Therefore, investigations using broader microbiological evaluations or, even mathematical methods such as the support vector machine, could be useful to find possible differences in the subgingival microbiota among patients with risk factors beyond the target pathogens.…”

Using big data to promote precision oral health in the context of a learning healthcare system

“…Deep phenotyping refers to “the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described” in order to facilitate a more comprehensive understanding of the pathologic basis of disease . A study on oral microbial profiles used principal components analysis to determine the microbial profile of healthy patients when compared to chronic and aggressive periodontitis patients . Deep phenotyping of periodontitis patients can provide new insights into pathologic phenotypic characteristics that are predictive of tooth loss .…”

“…93 A study on oral microbial profiles used principal components analysis to determine the microbial profile of healthy patients when compared to chronic and aggressive periodontitis patients. 94 Deep phenotyping of periodontitis patients can provide new insights into pathologic phenotypic characteristics that are predictive of tooth loss. 95 Similar principles can also be applied to implant patients in order to develop more comprehensive risk profiles of patients likely to develop implant failure or peri-implantitis.…”

Salivary Microbiome Shifts in Response to Periodontal Treatment Outcome

“…[ 10 ] One should also consider that sampling of the salivary microbiome may be more homogenous and repetitive among studies. [ 7 ] Variations between studies on subgingival microbiomes may occur due to procedural differences in biofilm sampling (curette versus paper‐point), [ 29,30 ] or may indeed reflect highly specific biological differences between affected sites.…”