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International Journal of Dentistry
Volume 2011 (2011), Article ID 196721, 13 pages
Research Article

Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries

1Department of Periodontics, College of Dentistry, University of Illinois at Chicago, 801 S. Paulina Street, Chicago, IL 60612, USA
2Department of Cariology and Comprehensive Care and Department of Periodontics and Implants, College of Dentistry, New York University, 345 E. 24th Street, New York, NY 10010, USA
3Computer Science Department, Intelligent Systems Program, Department of Biomedical Informatics, University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, PA 15232, USA
4Human and Craniofacial Genetics Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USA
5Department of General Dentistry, UNIMONTES, Montes Claros, MG 39401, Brazil
6Critical Care Medicine Department, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA

Received 20 May 2011; Accepted 15 July 2011

Academic Editor: Alexandre Rezende Vieira

Copyright © 2011 Thomas C. Hart et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The purpose of this study was to provide a univariate and multivariate analysis of genomic microbial data and salivary mass-spectrometry proteomic profiles for dental caries outcomes. In order to determine potential useful biomarkers for dental caries, a multivariate classification analysis was employed to build predictive models capable of classifying microbial and salivary sample profiles with generalization performance. We used high-throughput methodologies including multiplexed microbial arrays and SELDI-TOF-MS profiling to characterize the oral flora and salivary proteome in 204 children aged 1–8 years (n=118 caries-free, n=86 caries-active). The population received little dental care and was deemed at high risk for childhood caries. Findings of the study indicate that models incorporating both microbial and proteomic data are superior to models of only microbial or salivary data alone. Comparison of results for the combined and independent data suggests that the combination of proteomic and microbial sources is beneficial for the classification accuracy and that combined data lead to improved predictive models for caries-active and caries-free patients. The best predictive model had a 6% test error, >92% sensitivity, and >95% specificity. These findings suggest that further characterization of the oral microflora and the salivary proteome associated with health and caries may provide clinically useful biomarkers to better predict future caries experience.