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Validation and verification of predictive salivary biomarkers for oral health
N. Bostanci, K. Mitsakakis, B. Afacan, K. Bao, B. Johannsen, D. Baumgartner, L. Müller, H. Kotolová, G. Emingil, M. Karpíšek
Language English Country Great Britain
Document type Journal Article, Research Support, Non-U.S. Gov't
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- MeSH
- Biomarkers metabolism MeSH
- Adult MeSH
- Enzyme-Linked Immunosorbent Assay methods MeSH
- Gingivitis diagnosis metabolism MeSH
- Cohort Studies MeSH
- Middle Aged MeSH
- Humans MeSH
- Matrix Metalloproteinase 8 metabolism MeSH
- Matrix Metalloproteinase 9 metabolism MeSH
- Oral Health * MeSH
- Periodontitis diagnosis metabolism MeSH
- Area Under Curve MeSH
- Cross-Sectional Studies MeSH
- ROC Curve MeSH
- Saliva metabolism MeSH
- Case-Control Studies MeSH
- Tissue Inhibitor of Metalloproteinase-1 metabolism MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Oral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.
Department of Periodontology School of Dentistry Ege University 35100 Bornova İzmir Turkey
Faculty of Pharmacy Masaryk University Palackeho trida 1946 1 61242 Brno Czech Republic
Hahn Schickard Georges Koehler Allee 103 79110 Freiburg Germany
References provided by Crossref.org
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- $a Oral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.
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