J 2021

Validation and verification of predictive salivary biomarkers for oral health

BOSTANCI, N., K. MITSAKAKIS, B. AFACAN, K. BAO, B. JOHANNSEN et. al.

Basic information

Original name

Validation and verification of predictive salivary biomarkers for oral health

Authors

BOSTANCI, N. (guarantor), K. MITSAKAKIS, B. AFACAN, K. BAO, B. JOHANNSEN, D. BAUMGARTNER, L. MULLER, Hana KOTOLOVÁ (203 Czech Republic, belonging to the institution), G. EMINGIL and Michal KARPÍŠEK (203 Czech Republic, belonging to the institution)

Edition

Nature Scientific Reports, London, NATURE RESEARCH, 2021, 2045-2322

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30104 Pharmacology and pharmacy

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 4.996

RIV identification code

RIV/00216224:14160/21:00121412

Organization unit

Faculty of Pharmacy

UT WoS

000632046500002

Keywords in English

Validation; verification; predictive salivary; biomarker; oral health

Tags

Tags

International impact, Reviewed
Změněno: 16/4/2021 18:32, Mgr. Hana Hurtová

Abstract

V originále

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.