ParoPredict - Profiling of Oral Microbiome Dynamics to Predict the Long-Term Course of Periodontal Treatment

• Applicant: Dr. Daniel Hagenfeld
• Subject Area: Dentistry, Oral Surgery
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 468857272

Periodontitis is a chronic infectious disease that affects tooth-supporting tissues. It has a high prevalence that is even higher in individuals who smoke and is the main reason for tooth loss in the elderly. Mechanical periodontal therapy is often accompanied by systemic antibiotics that improve clinical inflammatory signs and reduce periodontal pathogenic bacteria. However, the influence of long-term effects of periodontal therapy on the entire microbiota is still unclear. Thus, dentists rely on the patient’s age and clinical symptoms, and not on the present microbiota to decide for or against the use of systemic antibiotics. Our own and many other studies revealed that there is a succession of periodontal pathogens within microbiomes of periodontally diseased dental pockets. Furthermore, we have shown that periodontal pathogens significantly decrease in microbiomes of smoking and non-smoking periodontitis patients when treated with antibiotics in a short-term evaluation. A systematic review conducted by our group showed that there are no long-term microbiome studies available that determine if this difference in microbial profiles persists after periodontal treatment with antibiotics. Therefore, the aim of this study is to describe long-term microbiome changes and find bacterial species that predict an above median treatment response while considering smoking status, parameters of systemic inflammation and antibiotic use. We will use frozen and stored samples of 167 smoking and non- smoking periodontitis patients from the already finished randomized controlled ABPARO study that evaluated the course over two years after periodontal therapy, either with amoxicillin and metronidazole or placebo for 7 days. First, we will expand our short-term evaluations with the sampling timepoints 6, 12 and 24 months using short-read paired-end 16S rDNA amplicon sequencing. Here, we will describe if our previously observed microbial shifts persist after a longer follow-up. Secondly, we will establish and train a predictive random forest model to find discriminatory genus-level taxa predicting above and below median clinical changes after 2 years. Furthermore, the influence of antibiotics, parameters of systemic inflammation and smoking will be evaluated on those predictions. Last, we will conduct a confirmatory full-length 16S rDNA sequence analysis with species-level resolution. Here, samples will be sequenced for a deeper taxonomic resolution to further optimize the model predictions. Consequently, this will enable us to identify microbiome profiles for favorable and unfavorable long-term treatment outcomes and clarify the influence of antibiotics, systemic inflammation and smoking status on those outcomes. We expect a greater community impact of the results because it could enable a reduced and more focused use of antibiotics in periodontal treatment that can decrease health expenditures and prevents unnecessary tooth loss.

• DFG Programme: Research Grants