Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries
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 empl...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | International Journal of Dentistry |
| Online Access: | http://dx.doi.org/10.1155/2011/196721 |
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| author | Thomas C. Hart Patricia M. Corby Milos Hauskrecht Ok Hee Ryu Richard Pelikan Michal Valko Maria B. Oliveira Gerald T. Hoehn Walter A. Bretz |
| author_facet | Thomas C. Hart Patricia M. Corby Milos Hauskrecht Ok Hee Ryu Richard Pelikan Michal Valko Maria B. Oliveira Gerald T. Hoehn Walter A. Bretz |
| author_sort | Thomas C. Hart |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e83069ec63724e4f9dcc2a53a5eaddb9 |
| institution | Kabale University |
| issn | 1687-8728 1687-8736 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Dentistry |
| spelling | doaj-art-e83069ec63724e4f9dcc2a53a5eaddb92025-02-03T01:00:49ZengWileyInternational Journal of Dentistry1687-87281687-87362011-01-01201110.1155/2011/196721196721Identification of Microbial and Proteomic Biomarkers in Early Childhood CariesThomas C. Hart0Patricia M. Corby1Milos Hauskrecht2Ok Hee Ryu3Richard Pelikan4Michal Valko5Maria B. Oliveira6Gerald T. Hoehn7Walter A. Bretz8Department of Periodontics, College of Dentistry, University of Illinois at Chicago, 801 S. Paulina Street, Chicago, IL 60612, USADepartment 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, USAComputer Science Department, Intelligent Systems Program, Department of Biomedical Informatics, University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, PA 15232, USAHuman and Craniofacial Genetics Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD 20892, USAComputer Science Department, Intelligent Systems Program, Department of Biomedical Informatics, University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, PA 15232, USAComputer Science Department, Intelligent Systems Program, Department of Biomedical Informatics, University of Pittsburgh Cancer Institute, University of Pittsburgh, Pittsburgh, PA 15232, USADepartment of General Dentistry, UNIMONTES, Montes Claros, MG 39401, BrazilCritical Care Medicine Department, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USADepartment 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, USAThe 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.http://dx.doi.org/10.1155/2011/196721 |
| spellingShingle | Thomas C. Hart Patricia M. Corby Milos Hauskrecht Ok Hee Ryu Richard Pelikan Michal Valko Maria B. Oliveira Gerald T. Hoehn Walter A. Bretz Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries International Journal of Dentistry |
| title | Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries |
| title_full | Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries |
| title_fullStr | Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries |
| title_full_unstemmed | Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries |
| title_short | Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries |
| title_sort | identification of microbial and proteomic biomarkers in early childhood caries |
| url | http://dx.doi.org/10.1155/2011/196721 |
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