Microbiota around teeth and dental implants in periodontally healthy, partially edentulous patients: is pre-implant microbiological testing relevant?

Does subgingival bacterial colonization differ between implants and teeth? A systematic review

The aim was of this study was to determine the current weight of evidence for the existence of specific differences between the microbiota of healthy teeth and healthy implants, or of teeth with periodontitis and implants with peri-implantitis. A systematic review was conducted according to the PRISMA statement. The MEDLINE, EMBASE and Cochrane databases were searched up to February 2018 for studies comparing microbiological data of biofilm samples collected from healthy teeth and implants or from teeth with periodontitis and implants with peri-implantitis. The weight of evidence was defined in three categories (strong, moderate and mild/some), according to the difference in number of studies showing statistically significantly higher counts and/or proportions and/or abundance and/or prevalence of microorganisms in health or in disease. Of the 132 articles identified, 8 were included. A wide range of microorganisms were present in different conditions but no microorganisms showed strong, moderate or mild/some evidence for a specific association with either teeth or implants. The results of this systematic review indicated that there is insufficient evidence in the literature to support specific differences between microorganisms colonizing teeth and implants, either in health or in disease.

Effects of Bacterial Contamination on Dental Implants During Surgery: A Systematic Review

INTRODUCTION

Contamination during surgery negatively influences the prognosis of orthopaedic implants; however, it has not been proven whether contamination influences the success of dental implant treatment. The aim of the systematic review was to investigate if there exists evidence in the literature whether contamination of dental implants during surgery affects osseointegration and clinical success.

MATERIALS AND METHODS

Four data bases were used for the literature search. Primary studies and reviews regarding both clinical and preclinical research were eligible. Rating of the summarized quality of the evidence was performed.

RESULTS

Five preclinical studies were included. Because of the estimated high risk of bias in all included studies and extensive differences in study design between the included studies, meta-analysis was not performed and no reliable aggregated data could be extracted.

CONCLUSIONS

It is suggested that the scientific evidence with regard to the current topic is insufficient. Further controlled studies are warranted.

Salivary IL-1β and red complex bacteria as predictors of the inflammatory status in sub-peri-implant niches of subjects with peri-implant mucositis

BACKGROUND AND OBJECTIVES

Salivary biomarkers may enhance diagnostic sensitivity for peri-implant disease assessment. This study aimed to investigate the association of salivary periodontopathogen count and salivary interleukin-1beta (IL-1β) level with the peri-implant crevicular fluid IL-1β response at peri-implant mucositis (PM) sites among subjects with differing periodontal disease susceptibility.

MATERIALS AND METHODS

Eighty-seven partially edentulous subjects having at least one implant with peri-implant mucositis were included: 40 with history of chronic periodontitis (P) and 47 with no history of periodontitis (NP). Salivary IL-1β, peri-implant crevicular fluid (PICF) IL-1β, and salivary red complex pathogen counts were recorded. Subjects were scored according to a threshold salivary pathogen level of more than 5log (10) counts and assigned a "red complex score." Quartiles of salivary and PICF IL-1β levels were also scored. Area under receiver operating curve (AUC) was computed to predict the highest PICF IL-1β score using salivary biomarker as predictors and age-adjusted logistic regression performed for the significant predictors.

RESULTS

In the NP group, red complex score (AUC = 0.758 P = 0.010) (odds ratio = 1.377) and salivary IL-1β (AUC = 0.708 P = 0.038) (odds ratio = 2.506) were significant predictors of highest PICF IL-1β quartile score. In the P group, no significant associations were noted.

CONCLUSIONS

Salivary biomarkers could distinguish the "high" pro-inflammatory responders at PM sites only in subjects without inherent periodontal disease susceptibility. Periodontal susceptibility may impact the immuno-inflammatory response in sub-peri-implant niches of those with peri-implant mucositis.

The Relationship between Biofilm and Physical-Chemical Properties of Implant Abutment Materials for Successful Dental Implants

The aim of this review was to investigate the relationship between biofilm and peri-implant disease, with an emphasis on the types of implant abutment surfaces. Individuals with periodontal disease typically have a large amount of pathogenic microorganisms in the periodontal pocket. If the individuals lose their teeth, these microorganisms remain viable inside the mouth and can directly influence peri-implant microbiota. Metal implants offer a suitable solution, but similarly, these remaining bacteria can adhere on abutment implant surfaces, induce peri-implantitis causing potential destruction of the alveolar bone near to the implant threads and cause the subsequent loss of the implant. Studies have demonstrated differences in biofilm formation on dental materials and these variations can be associated with both physical and chemical characteristics of the surfaces. In the case of partially edentulous patients affected by periodontal disease, the ideal type of implant abutments utilized should be one that adheres the least or negligible amounts of periodontopathogenic bacteria. Therefore, it is of clinically relevance to know how the bacteria behave on different types of surfaces in order to develop new materials and/or new types of treatment surfaces, which will reduce or inhibit adhesion of pathogenic microorganisms, and, thus, restrict the use of the abutments with indication propensity for bacterial adhesion.

Modeling Susceptibility to Periodontitis

Chronic inflammatory diseases like periodontitis have a complex pathogenesis and a multifactorial etiology, involving complex interactions between multiple genetic loci and infectious agents. We aimed to investigate the influence of genetic polymorphisms and bacteria on chronic periodontitis risk. We determined the prevalence of 12 single-nucleotide polymorphisms (SNPs) in immune response candidate genes and 7 bacterial species of potential relevance to periodontitis etiology, in chronic periodontitis patients and non-periodontitis control individuals (N = 385). Using decision tree analysis, we identified the presence of bacterial species Tannerella forsythia, Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and SNPs TNF -857 and IL-1A -889 as discriminators between periodontitis and non-periodontitis. The model reached an accuracy of 80%, sensitivity of 85%, specificity of 73%, and AUC of 73%. This pilot study shows that, on the basis of 3 periodontal pathogens and SNPs, patterns may be recognized to identify patients at risk for periodontitis. Modern bioinformatics tools are valuable in modeling the multifactorial and complex nature of periodontitis.