Contact person:
Claes Wickström
  • Malmö University
  • The Knowledge Foundation
Responsible at MaU:
Claes Wickström
Collaborators and other project members:
  • Henrik Jansson. Folktandvården Skåne
  • Stefan Rüdiger. Specialisttandvården Skåne
Time frame:
01 September 2018 - 31 August 2023
Research environment :
Research subject:

About the project

This project focuses on the discovery of predictive biomarkers and preventive biotherapeutics for caries. Currently, the best predictive factor for the development of caries is the presence of previous caries lesions. Previous attempts to exploit the presence of specific microorganisms e.g. Streptococcus mutans in oral biofilms as predictive biomarkers for caries have not been successful. Therefore, in this project, we hypothesize that phenotypes in oral biofilms and molecules associated with them can be used as biomarkers to predict the onset of disease. Foresight follows a well-established biomarker ‘pipeline principle’ for the clinical utility of new predictive biomarkers for risk assessment. The first part is a broad, semi-quantitative discovery phase where a panel of candidate biomarkers derived from clinically-derived biofilms (dental plaque) will be identified. Verification will be undertaken on five-six of the most promising biomarkers for caries.

The group working on predictive biomarkers for caries comprises competencies in cariology and study design, clinical sampling and analyses, sample analysis, microbiology and oral ecology, orthodontics, pedodontics and oral health.

The project addresses three research questions:

  • How can phenotypes in dysbiotic biofilms be utilized as a platform for targeted strategies to predict caries?
  • How can predictive biomarkers be targeted to prevent caries?
  • What is the efficacy of developed biomarkers?
  1. Identifying predictive biomarkers for caries

Mutans streptococci is a poor predictor of risk for caries development since these bacteria are found in people without the disease and caries can develop in individuals who lack these bacteria. Our approach is based on the ecological plaque hypothesis, which proposes that caries arise due to dysbiosis in biofilms on teeth, with enrichment of acid-tolerating phenotypes and a low-pH environment, due to frequent exposure to fermentable carbohydrates. We propose that levels of acid-tolerant bacteria and specific proteins associated with acid adaptation in the biofilm could be used as biomarkers to predict the risk for the development of caries.

  1. Identifying targeted biotherapeutics against biomarkers

Discovery of biomarkers to predict the onset of disease could be considered unethical without, in parallel, searching for ways to intervene in the pathogenic process. Our vision is that identification of biomarkers that are key players in oral disease processes will open the possibility for the development of new biotherapeutics that act directly on the biomarkers themselves to intervene in disease progression.

  1. Verification of candidate biomarkers

Verification will be performed on samples that closely represent individuals in which the clinical risk assessment will be deployed. Candidate biomarker specificity will be assessed in samples from groups of individuals with ‘high risk’ and ‘low risk’, respectively. Differentiation of these two groups is based on clinical assessments of established diseases. The verification of biomarkers for caries will include in a total of 50 patients in each study (25 with high and 25 with low risk).

  1. Clinical validation of predictive biomarkers

The clinical validation of predictive biomarkers will be undertaken in longitudinal prospective clinical trials conducted in accordance with “Standards for Reporting of Diagnostic Accuracy” (STARD-statement). Patients from the Public Dental Health in Halland, Småland and Skåne and from MaU will be recruited. Samples from multiple sites in everyone will be collected every six months over 3 years. The predictability of the biomarkers will be assessed by calculating their sensitivity, specificity, predictive values, and likelihood ratios for several threshold values.