Salivary biomarkers for dental caries detection and personalized monitoring

Pune N. Paqué*, Christopher Herz, Daniel B. Wiedemeier, Konstantinos Mitsakakis, Thomas Attin, Kai Bao, Georgios N. Belibasakis, John P. Hays, Joël S. Jenzer, Wendy E. Kaman, Michal Karpíšek, Philipp Körner, Johannes R. Peham, Patrick R. Schmidlin, Thomas Thurnheer, Florian J. Wegehaupt, Nagihan Bostanci

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

This study investigated the potential of salivary bacterial and protein markers for evaluating the disease status in healthy individuals or patients with gingivitis or caries. Saliva samples from caries-and gingivitis-free individuals (n = 18), patients with gingivitis (n = 17), or patients with deep caries lesions (n = 38) were collected and analyzed for 44 candidate biomarkers (cytokines, chemokines, growth factors, matrix metalloproteinases, a metallopeptidase inhibitor, proteolytic enzymes, and selected oral bacteria). The resulting data were subjected to principal component analysis and used as a training set for random forest (RF) modeling. This computational analysis revealed four biomarkers (IL-4, IL-13, IL-2-RA, and eotaxin/CCL11) to be of high importance for the correct depiction of caries in 37 of 38 patients. The RF model was then used to classify 10 subjects (five caries-/gingivitis-free and five with caries), who were followed over a period of six months. The results were compared to the clinical assessments of dental specialists, revealing a high correlation between the RF prediction and the clinical classification. Due to the superior sensitivity of the RF model, there was a divergence in the prediction of two caries and four caries-/gingivitis-free subjects. These findings suggest IL-4, IL-13, IL-2-RA, and eotaxin/CCL11 as potential salivary biomarkers for identifying noninvasive caries. Furthermore, we suggest a potential association between JAK/STAT signaling and dental caries onset and progression.

Original languageEnglish
Article number235
JournalJournal of Personalized Medicine
Volume11
Issue number3
DOIs
Publication statusPublished - 23 Mar 2021

Bibliographical note

Funding: This research was funded by the European Union’s Horizon 2020 research and innovation program, grant number 633780 (“DIAGORAS” project).

Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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