Prediktiva biomarkörer för parodontit
- Julia Davies
- Malmö universitet
- Ansvarig vid Mau:
- Julia Davies
- Torbjörn Bengtsson – Örebro universitet
- Hazem Khalaf – Örebro universitet
- Rolf Lood – Lunds universitet
- Johanna Lönn – Örebro universitet
- Eleonor Palm - Örebro universitet
- Bijar Ghafouri – Linköpings universitet
- 01 september 2018 - 31 augusti 2023
This project focuses on the discovery of predictive biomarkers and preventive biotherapeutics for periodontitis (gum disease). Previous attempts to exploit the presence of specific microorganisms in oral biofilms or individual components of the inflammatory response as predictive biomarkers for this disease have been largely unsuccessful. Therefore, the novel idea behind this project is that microbial phenotypes in biofilms or specific molecules associated with them, in combination with specific molecules involved in the host response to the same biofilms, can be used as biomarkers to predict the onset of severe disease.
Foresight follows a well-established ‘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 will be identified from clinical samples as well as models of microbe-host interactions. Verification will be undertaken on five-six of the most promising biomarkers. The expected output will be a small number (1-2) of highly credible biomarker candidates suitable for further investigation, including sensor development. Later in the project, sensors supplied by the “Sensor group” will be used to analyse the biomarkers in patients in real-time in longitudinal clinical studies.
The Foresight group working on this project includes a range of competencies: periodontology and study design, clinical sampling and analysis, microbiology, cell biology, inflammation and oral ecology.
The project addresses three research questions:
- How can virulence expression in, and host responses to, dysbiotic biofilms be utilized as a platform for targeted strategies to predict periodontitis?
- How can predictive biomarkers be targeted to prevent periodontitis?
- What is the efficacy of developed biomarkers?
1. Identifying predictive biomarkers for periodontitis
Developing microbial biofilms in the gingival pocket induces inflammation in the adjacent host tissues, which drives a change in biofilm phenotype. Dysbiotic and proteolytic biofilms are important factors in disease progression. We, therefore, propose that proteolytic activity per se and an array of host- and microbiome-derived proteolytic enzymes in the gingival pocket could be utilized as biomarkers for the prediction of bone loss in periodontitis.
2. 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 (e.g. protease inhibitors) themselves to intervene in disease progression.
3. 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 periodontitis will include a total of 50 patients in each study (25 with high and 25 with low risk). The candidate biomarkers will be analysed using sensor prototypes based on the novel analytical methods we have developed.
4. 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 Skåne will be recruited. Samples from multiple sites will be collected 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.