Developing a periodontal disease antibody array for the prediction of severe periodontal disease using machine learning classifiers

BACKGROUND: The aim of this study was to simultaneously and quantitatively assess the expression levels of 20 periodontal disease-related proteins in gingival crevicular fluid (GCF) from normal controls (NOR) and severe periodontitis (SP) patients with an antibody array. METHODS: Antibodies against 20 periodontal disease-related proteins were spotted onto a glass slide to create a periodontal disease antibody array (PDD). The array was then incubated with GCF samples collected from 25 NOR and 25 SP patients. Differentially expressed proteins between NOR and SP patients were then used to build receiver operator characteristic (ROC) curves and compare five classification models, including support vector machine, random forest, k nearest neighbor, linear discriminant analysis, and Classification and Regression Trees. RESULTS: Seven proteins (C-reactive protein, interleukin IL-1α, interleukin-1β, interleukin-8, matrix metalloproteinase-13, osteoprotegerin, and osteoactivin) were significantly upregulated in SP patients compared with NOR, while receptor activator of nuclear factor-kappa was significantly downregulated. The highest diagnostic accuracy using a ROC curve was observed for IL-1β with an area under the curve of 0.984. Five of the proteins (IL-1β, IL-8, MMP-13, osteoprotegerin, and osteoactivin) were identified as important features for classification. Linear discriminant analysis had the highest classification accuracy across the five classification models that were tested. CONCLUSION: This study highlights the potential of antibody arrays to diagnose periodontal disease.