Abstract
Objective
To estimate the actual parameters of bacterial load in subgingival plaque during periodontitis and peri-implantitis pathologies using the RT-PCR (real-time polymerase chain reaction) method and evaluate their associations with clinical periodontal indicators.
Materials and Methods
Five different groups of subjects were selected according to a formulated design of the study: with mild/moderate periodontitis, with severe periodontitis, with peri-implantitis, healthy periodontal group and healthy peri-implant group. Subgingival plaque samples were formed with paper points inserted in the pocket/sulcus area for 30 seconds. A standardized test the “ParodontoScreen” was provided for identification of target opportunistic pathogens (A. actinomycetemcomitans, P. gingivalis, T. forsythia, P. intermedia, T. denticola) by the RT-PCR.
Results
Bacterial load parameters demonstrated a significant tendency towards an increase within periodontitis progression and during the presence of peri-implantitis pathology. Each targeted mean bacterial load level was statistically associated with periodontitis or peri-implantitis pathology (p < 0, 05) according to the provided univariate analyses and upon condition that bacterial load parameters of healthy sites were used as reference for equiparation. The highest correlation values were found between periodontal probing depth and bacterial load parameters of A. actinomycetemcomitans (r=0, 37; p < 0, 05) and P. gingivalis (r=0, 28; p < 0, 05); and also between clinical attachment loss and bacterial load values of A. actinomycetemcomitans (r=0, 38; p < 0, 05) and P. gingivalis (r=0, 24; p < 0, 05).
Conclusions
Periodontitis and peri-implantitis are associated with the same microbial pathogens even though the distribution pattern of their bacterial load and detection frequency parameters registered with RT-PCR could be distinct and linked to the individual patient-related conditions and the severity stage of pathology.
Key words: Periodontitis, Peri-implantitis, Bacterial Load, Real-Time Polymerase Chain Reaction
Introduction
Although periodontitis and peri-implantitis pathologies have been associated with impact of so-called periopathogens, the results of recent systematic reviews and meta-analyses provided high quality evidence that quantitative characteristics of oral microbiome rather than just the presence of specific pathogen anticipate in disease pattern (1-4). Qualitative microbiome essence seems to be relatively similar between healthy and diseased periodontal/peri-implant sites. However, some variable-based composition changes have been observed during inflammatory-associated periodontal tissue alteration around the tooth or dental implant (1-3).
That is why it is important to consider the presence of periodontal pathogens even among healthy subjects and relevant theories of periodontitis and peri-implantitis development based on the phenomenon of periodontal pathogen imbalance and changes in host susceptibility levels (5, 6). The current shift of keystone pathogen theory to the periodontal pathogen disequilibrium theory was supported by the amount of evidence obtained over the last decade by using metagenomics and culturomics in research (6-8).
There is a consensus in the scientific community about the fact that periodontally affected patients are characterized by the higher risk of peri-implantitis development. We must take into account that original bacteriological nature of periodontitis and peri-implantitis are similar, even though these lesions differ by their pattern of progression (9-11). Such a difference could be related not only to the fact that peri-implant tissue complex diverges from the periodontal one by its structure, but also to the possible dissimilarities in bacterial environment around the affected implant and the affected tooth (10, 11).
Moreover, further investigation of microbiome parameters at peri-implant and periodontal regions would be supportive for the continued differential analysis of their relationship with host genetic factors (11). In the systematic review of Nibali et al. (12), authors have mentioned that parameters of bacterial colonization patterns and their association with transcriptome information in the area of periodontal tissue alteration may be used for further correction of possible infectogenomics effects, which in turn could influence clinical development of the pathology. In other words, numerical verification of periodontal/peri-implant bacterial load patterns and their changes could be used as predictive criteria for diseases progression, or even for prediction of disease onset before any clinical signs of inflammation could be registered.
There are various methods used for quantification purpose of peri-implant and periodontal microbiomes and their differentiation, but question of routinely accessible and standardized approach that can estimate actual bacterial load at the different stages of each of these pathologies remains not fully resolved (13, 14).
That is why the objective of this study was to estimate the actual parameters of bacterial load in subgingival plaque during periodontitis and peri-implantitis pathologies using the RT-PCR (real-time polymerase chain reaction) method and evaluate their associations with clinical periodontal indicators.
Material and Methods
Five different groups of subjects were formed out of a cohort of dental patients from the “DM” (Uzhhorod, Ukraine) private dental clinic according to the formulated design. The patients included in the study groups were screened according to the following inclusion criteria: 1) presence of periodontitis/peri-implantitis signs; 2) healthy systematic condition 3) willingness to participate in the research after explanation of all aspects of the study and the signature of patient’s consent form. As exclusion criteria, used during the selection of patients into study groups, the following criteria were chosen: 1) presence of somatic comorbidities; 2) smoking; 3) systematic or sporadic medication intake that could potentially influence the oral microbiome during previous 14 days; 4) periodontitis or peri-implantitis management received during previous 6 months. According to above mentioned inclusion and exclusion criteria, 67 patients were distributed among the following three study groups: 28 patients were included in the group with mild/moderate periodontitis (MMP group), 16 patients – in the group with severe periodontitis (SP group), and 23 patients – in the group with peri-implantitis (PI group).
Control groups of subjects were formed out of dental patients according to the following inclusion criteria: 1) absence of periodontitis/peri-implantitis signs; 2) in cases of implant treatment implant screws were installed more than 12 months ago; 3) healthy somatic condition; 4) willingness to participate in the research after explanation of all aspects of the study and the obtained signature of patient’s consent form. Exclusion criteria used for control groups were the same as for study groups. According to the above mentioned inclusion and exclusion criteria, 41 patients were distributed in the following two control groups: 21 patients were included in the healthy periodontal group, who have not undergone any implant procedure (HP group), and 20 patients – in the healthy peri-implant group, who have received dental implant treatment more than 12 months ago with no clinical signs of peri-implantitis present at the time of clinical examination (HPI group).
Periodontal examination
Periodontal check-up was provided by previously calibrated three dental professionals with registration of such parameters as bleeding on probing (BOP), interdental clinical attachment loss (CAL) and periodontal probing depth (PPD) (15). At the time of initial clinical examination, each patient with clinical signs of periodontitis or peri-implantitis has undergone the procedure of peri-apical radiography.
Periodontitis staging was done according to the recommendation of 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions (16), considering which cases of I and II stages of periodontitis were clustered in the study group with mild/moderate periodontitis (MMP group), and cases with III stage periodontitis were clustered in the study group with severe periodontitis (SP group).
Identification of peri-implantitis cases was done according to the case definition criteria and diagnostic considerations described by Renvert et al. (17).
Plaque sampling
Subgingival plaque samples among patients of study groups were gathered from the sites with deepest periodontal probing parameters, which were identified with the use of North Carolina Periodontal Probe according to its 1 mm marking scale. Among patients of control groups, subgingival plaque samples were taken from the area of teeth and implants topographically analogical to those in patients with periodontal and peri-implant pathology. These sites were isolated before sampling procedure with cotton rolls, subsequently dried and mechanically cleaned with Gracey-curettes from the supragingival plaque. After that, paper-points were inserted in the pocket/sulcus area for 30 seconds. The paper points were placed in prepared sterile tubes after extraction and immediately transported within the next hour to the “Astra-Dia” laboratory (Uzhhorod, Ukraine).
ParodontoScreen Procedure with RT-PCR
The ParodontoScreen standardized test was provided in the laboratory conditions, which is aimed at identification of opportunistic pathogens in the gathered subgingival plaque samples with the use of real-time polymerase chain reaction (the RT-PCR method). Target microorganisms in the standardized ParodontoScreen analysis include the following: Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans), Porphyromonas gingivalis (P. gingivalis), Tannerella forsythia (T. forsythia), Prevotella intermedia (P. intermedia), Treponema denticola (T. denticola). The RT-PCR is based on the DNA-amplification process with further completion of polynucleotide chain with Taq-polymerase. The standardized ParodontoScreen test includes the following steps: allotment of DNA (preparation of specimen), PCR-amplification in real time condition with the use of specific reagents (mixture for PCR-amplification that is specific for all bacteria, mixture for PCR-amplification that is specific for opportunistic bacteria, mixture for PCR-amplification that is specific for human genomic DNA), registration of amplification results and their interpretation. The PCR and post-PCR processing was provided by the specific software. A laboratory analysis of all study and control samples was held by laboratory specialists with further representation of obtained results in the form of report (Figure 1).
Figure 1.
_32-43-f1.jpg)
Report form of bacterial load of each periodontal pathogen established with “ParodontoScreen” test (original in Ukrainian)
Bacterial load levels of species were conventionally represented in “Lg (genome equivalents/sample)” units, also referred as Lg (GE/sample).
Detailed descriptions of the ParodontoScreen procedure aspects are presented on the manufacturer’s website (18).
The research received acceptance from the Ethics Committee of Medical Faculty at Pavol Jozef Šafárik University (Košice, Slovak Republic).
Statistical analysis
A descriptive statistical analysis included an estimation of mean values and their standard deviations (SD) for each parameter (age, BOP, PPD, CAL, bacterial load of each microbial species) in all study and control groups. Statistical differences between groups in the means of all studied parameters were assessed using the independent Student-t test (19). Univariate analyses aimed at the evaluation of possible associations between detection frequency rate and mean bacterial load of each species during periodontal and peri-implant diseases was provided as previously described in the study of Ismail et al. (20), using p-values < 0,05 as statistically significant for all parameters (21). The correlation between periodontal indicators and bacterial load parameters was assessed using the Spearman correlation coefficient as appropriate. A statistical analysis was provided using software package IBM SPSS Statistics (IBM Corporation) (19, 20), while data acquisition and organization was held in Microsoft Excel software (Microsoft Office 2019, Microsoft).
Results
Distribution of age, gender and initial clinical parameters registered among patients of study and control groups during primary examination are shown in Table 1.
Table 1. Distribution of baseline parameters among study and control groups.
|
Group/ Parameters |
MMP group (n=28) | SP group (n=16) | PI group (n=23) | HP group (n=21) | HPI group (n=20) |
|---|---|---|---|---|---|
| Age (years) | 44.3±1,2 | 47.2±3,1 | 51.5±2,4 | 45.6±1,9 | 52.8±1,8 |
| Gender male%/female% | 57.1%/42,9% | 43.8%/56,2% | 52.2%/47,3% | 47.6%/52,4% | 50%/50% |
| BOP (%) | 67.9% | 81.3% | 82.6% | 23.8% | 35.0% |
| PPD (mm) | 4.2±0,4 | 6.1±0,7 | 5.2±0,6 | 2.2±0,7 | 3.7±0.5 |
| Interdental CAL (mm) | 3.6±0,6 | 5.8±0,3 | 4.9±0,5 | 1.4±0,5 | 3.5±0,4 |
MMP group – study group of patients with mild/moderate periodontitis, SP group – study group of patients with severe periodontitis, PI group – study group of patients with peri-implantitis, HP group – control group of patients with natural dentition and healthy periodontal status, HPI group – control group of patients with dental implants and healthy peri-implant status
Groups of severe periodontitis and peri-implantitis were characterized by the greatest levels of BOP, PPD and interdental CAL, which were statistically different from those noted among healthy periodontal and peri-implant sites and even at the sites with mild/moderate periodontitis (p < 0, 05). PPD and interdental CAL were statistically different during comparison of healthy periodontal and peri-implant sites (p < 0,05), while there was no statistically significant difference between mean PPD and interdental CAL values in MMP and HPI groups (p > 0,05), except that clinical cases of those were distinguished by the presence and absence of clinically observed inflammation. MMP and HPI groups were significantly different considering the prevalence of BOP cases (67, 9% vs 35, 0%; p < 0, 05).
The highest detection frequency rate using the real-time PCR method was observed for P. gingivalis in all healthy and diseased periodontal cases, while the lowest detection rate was noted for A. actinomycetemcomitans in all analyzed samples. The highest frequency of detection among all healthy peri-implant cases was noted for T. forsythia, which statistically differs from healthy periodontal cases (p < 0, 05). Also, higher detection rates were noted for T. denticola (p > 0, 05), P. intermedia (p < 0,05) and A. actinomycetemcomitans (p < 0,05) in healthy peri-implant samples compared to healthy periodontal samples. Such a tendency could be interpreted as indirect evidence of microbiome structure redistribution in the areas of installed implants compared to the area of natural teeth.
In all analyzed cases detection frequency of each microorganism increased with the pathology progression. Provided univariate analyses dedicated to the identification of significant dependencies between bacteria detection frequency and periodontal/peri-implant disease revealed that such associations were noted for: severe periodontitis and P. gingivalis (p < 0,05), T. denticola (p < 0,05), T. forsythia (p < 0,05); mild/moderate periodontitis and T. denticola (p < 0,05), T. forsythia (p < 0,05); peri-implantitis and T. denticola (p < 0,05), T. forsythia (p < 0,05), A. actinomycetemcomitans (p < 0,05) (Table 2).
Table 2. Detection frequency rates and results of univariate analyses for the interrelation with periodontal and peri-implant diseases.
|
Groups / Periodontal pathogen |
MMP group (n=28) | p-value | SP group (n=16) | p-value | PI group (n=23) | p-value | HP group (n=21) | HPI group (n=20) |
|---|---|---|---|---|---|---|---|---|
| P. gingivalis (%) | 92.9 | p > 0.05 | 100 | p < 0.05 | 78.3 | p > 0.05 | 85.7 | 70.0 |
| T. denticola (%) | 78.6 | p < 0.05 | 93.8 | p < 0.05 | 82.6 | p < 0.05 | 61.9 | 65 |
| T. forsythia (%) | 75.0 | p < 0.05 | 8.5 | p < 0.05 | 82.6 | p < 0.05 | 52.3 | 75 |
| P. intermedia (%) | 82.1 | p > 0.05 | 87.5 | p > 0.05 | 73.9 | p > 0.05 | 52.3 | 65 |
| A. actinomycetemcomitans (%) | 14,3 | p > 0.05 | 18,8 | p > 0.05 | 39,1 | p > 0.05 | 4.8 | 10.0 |
MMP group – study group of patients with mild/moderate periodontitis, SP group – study group of patients with severe periodontitis, PI group – study group of patients with peri-implantitis, HP group – control group of patients with natural dentition and healthy periodontal status, HPI group – control group of patients with dental implants and healthy peri-implant status
Even though detection frequency of some microorganisms increased during pathology compared to healthy state, such associations between detection frequency values and periodontal/peri-implant diseases were not statistically proven because of an uneven distribution of such frequency rates between the study subjects in each group (inter-subject detection frequency variations).
Bacterial load parameters demonstrated a significant increase tendency within periodontitis progression, and during the comparison of healthy and diseased periodontal/peri-implant sites. According to the provided univariate analyses, each registered mean bacterial load level was statistically associated with periodontitis or peri-implantitis pathology, upon condition that bacterial load levels of healthy periodontal and peri-implant sites were used as reference for equiparation (Table 3).
Table 3. Mean levels of bacterial load and results of univariate analyses for the association with periodontal and peri-implant diseases.
|
Groups / Periodontal pathogen |
MMP group (n=28) | p-value | SP group (n=16) | p-value | PI group (n=23) | p-value | HP group (n=21) | HPI group (n=20) |
|---|---|---|---|---|---|---|---|---|
| P. gingivalis (Lg GE/sample) | 5.5±0.7 | p < 0.05 | 6.9±0,5 | p < 0.05 | 6.0±0,4 | p < 0.05 | 2.4±0,2 | 3.9±0.4 |
| T. forsythia (Lg GE/sample) | 5.0±0.4 | p < 0.05 | 5.6±0.4 | p < 0,05 | 5.2±0.4 | p < 0.05 | 1.9±0,2 | 3.5±0.5 |
| P. intermedia (Lg GE/sample) | 4.9±0.5 | p < 0.05 | 6.3±0,5 | p < 0.05 | 5.3±0.6 | p < 0.05 | 2.7±0.3 | 2.9±0.1 |
| T. denticola (Lg GE/sample) | 4.1±0.6 | p < 0.05 | 6.4±0,3 | p < 0.05 | 4.7±0.5 | p < 0.05 | 2.9±0.2 | 3.2±0.2 |
| A. actinomycetemcomitans (Lg GE/sample) | 4.7±0.5 | p < 0.05 | 5.0±0,7 | p < 0.05 | 4.9±0.6 | p < 0.05 | 1.1±0.4 | 1.9±0.6 |
MMP group – study group of patients with mild/moderate periodontitis, SP group – study group of patients with severe periodontitis, PI group – study group of patients with peri-implantitis, HP group – control group of patients with natural dentition and healthy periodontal status, HPI group – control group of patients with dental implants and healthy peri-implant status
Statistical analysis of overall obtained data helped to register specific values of correlation between such parameters as BOP, PPD, CAL and bacterial load of each target periodontal pathogen. It was noted that BOP parameter was not statistically associated with any of identified microorganisms (p > 0, 05), while PPD was related to the increased load levels of each studied bacteria apart from P. intermedia, even though such correlation values were categorized as weak. CAL has shown analogical pattern of interrelation with increased bacterial load of each studied periopathogen apart from P. intermedia despite the fact that such correlation values were lower compared to those registered with PD. The highest correlation values were found between PPD and bacterial load parameters of A. actinomycetemcomitans (r=0, 37; p < 0, 05) and P. gingivalis (r=0, 28; p < 0, 05); and also between CAL and bacterial load values of A. actinomycetemcomitans (r=0, 28; p < 0, 05) and P. gingivalis (r=0, 24; p < 0, 05) (Table 4).
Table 4. Correlations between bacterial load of periopathogens and clinical parameters of BOP, PPD and CAL.
|
Study parameters / Periodontal pathogen |
BOP | PPD | CAL | |||
|---|---|---|---|---|---|---|
| r | p-value | r | p-value | R | p-value | |
| P. gingivalis | 0.12 | p > 0.05 | 0.28 | p < 0.05 | 0.24 | p < 0.05 |
| T. forsythia | 0.09 | p > 0.05 | 0.20 | p < 0.05 | 0.18 | p < 0.05 |
| P. intermedia | 0.11 | p > 0.05 | 0.13 | p > 0.05 | 0.11 | p > 0.05 |
| T. denticola | 0.14 | p > 0.05 | 0.21 | p < 0.05 | 0.21 | p < 0.05 |
| A. actinomycetemcomitans | 0.18 | p > 0.05 | 0.37 | p < 0.05 | 0.28 | p <0.05 |
BOP – bleeding on probing, PPD – periodontal probing depth, CAL – clinical attachment loss
Discussion
The newest model of periodontitis and peri-implantitis development considering the presence of keystone pathogen is not capable for provoking disease itself, but is responsible for formation of specific bacterial interrelation under which the overall level of pathogenicity increases at the problematic periodontal or peri-implant areas (7, 8). Most of the previous studies have been devoted to the analysis of microbial ecosystem during peri-implantitits and periodontitis, which can be categorized in two groups: studies aimed at verification of specific pathogens and studies aimed at detailed identification of complex microbiota. Such approaches of detailed quantitative and qualitative analyses of peri-implantitis and periodontitis biofilms structure support possibilities for treatment individualization, thus gaining effectiveness of epigenetic therapeutic modalities (22). Also such concepts of examination help minimize the risk of superinfection development, as previously described during the use of broad-spectrum antibiotics for peri-implantitis treatment (23).
In our research we have provided the estimates of actual bacterial load levels of five periodontopathogen species in subgingival plaque during periodontitis and peri-implantitis pathologies using the RT-PCR method. The main advantages of using the PCR-method in dentistry and periodontology are reasoned by time and cost-efficiency, higher sensitivity compared to other approaches, possibility to reproduce results in the same manner, and relatively easy algorithm for quantification of obtained parameters (24, 25). Recently, real-time polymerase chain reaction has been applied for qualitative analysis, thus completely replacing the method of bacterial culture (14).
The results of our study are concordant with previously obtained findings that could be summarized in the way that an increase in load levels of periodontopathogens associated with the development of periodontitis and peri-implantitis (26, 27). Despite that, it should be noted that interrelation changes between different species in subgingival microbiome, and specific individual host response mechanisms could play a more pronounced role in periodontal or peri-implant pathology development than just quantity variations of bacterial load, but such an aspect will be addressed in our further studies since it is beyond the scope of this research (1-3, 22, 26).
A previously statistically important increase in bacterial frequency was noted while comparing healthy periodontal and peri-implant conditions and states of periodontitis and peri-implantitis. However, no significant difference of bacterial frequency was registered between gingivitis and mucositis compared to periodontitis and peri-implantitis respectively (28). The same pattern was noted in our study, even though we have used criteria of bacterial load instead of previously described bacterial frequency: III stage periodontitis was characterized by higher bacterial load compared to the I stage periodontitis or healthy periodontal status, while the same tendency was also noted during comparison of healthy peri-implant region and peri-implantitis lesion.
In the study of Torrungruanag et al. (29), the prevalence of P. ginigvalis and T. denticola among patients with severe periodontitis was relatively similar to the detection rate frequency registered in our research, while detection rate frequency of T. forsythia and P. intermedia in our study was comparatively lower, which can be explained by the smaller number of study subjects in the corresponding group. It is important to notice that in both studies the prevalence/ detection frequency of A. actinomycetemcomitans was the lowest both among periodontitis and healthy sites, while bacterial load of this species was also the lowest one.
A significant difference was found in values of bacterial load between healthy periodontal sites and those with signs of periodontitis, while the same tendency was also noted during evaluation of peri-implant healthy sites and those with peri-implantitis. Moreover, the absolute difference of levels of microorganisms’ bacterial load was also found during equiparation of healthy periodontal sites and healthy peri-implant regions, while statistically significant variations were noted considering bacterial load of P. gingivalis, T. forsythia and A. actinomycetemcomitans (p < 0, 05). Nevertheless, the results of our research should be interpreted with caution, since in another study of microbiota composition it was found that even though variations of species have been observed between affected and unaffected peri-implant/periodontal sites, such differences were smaller compared to inter subject differences (30). Analogical facts have been described in a number of previous studies, wherein individual oral microbiome composition and its associated changes were the most predominant factors of pathology development, and on which further treatment algorithms should be targeted (22, 25, 26).
Due to the complex aim of this study and possibility to analyze both laboratory parameters of actual bacterial load parameters and clinical signs among periodontitis and peri-implantitis sites, we have found correlation values between specific load levels of A. actinomycetemcomitans, P. gingivalis, T. forsythia, P. intermedia, T. denticola and average parameters of BOP, PPD and CAL. Based on the obtained results it can be summarized that PPD and CAL parameters demonstrated a weak but statistically significant correlation with bacterial load of all studied bacterial species except P. intermedia, while BOP has shown no correlation with any of analyzed periodontopathogen loads. In the study of Octavia et al. (31), it was shown that scaling and root planing among patients with periodontitis, associated not only with the reduction of pocket depth and gingival bleeding index but also with decrease of these parameters, was also statistically correlated with the reduction of P. gingivalis and P. forsythia amounts counted in the subgingival plaque. On the other hand, in the largest periodontal epidemiological study with the use of the real time PCR method (29), it was found that the presence of P. gingivalis even in small amounts is strongly associated with severe periodontitis with greatest levels of PPD and CAL, while the bacterial load of A. actinomycetemcomitans, T. denticola and P. intermedia should reach some marginal levels to be statistically related to the advanced periodontal pathology (29). Such a tendency was also observed in our research: all patients with severe periodontitis and greatest values of PPD and CAL revealed 100% detection frequency of P. gingivalis, bacterial load which was also the greatest compared to that of other species.
Resuming the obtained outcomes, it could be highlighted that the use of PCR method helps identify main periodontal pathogens that play predominant role in periodontitis and peri-implantitis development. Moreover, identification of such pathogens during periodontitis pathology and before implant placement could be used for optimization of oral cavity ecosystem balance to provide more advantageous conditions for further implant functioning, thus reducing risks of possible peri-implant complications during a long-term monitoring. One of the further perspectives for using PCR method in implantological practice was described in the Carinci F. et al. study (31), in which the authors, based on the criteria of total bacterial load, pointed to the possibility of using implants with polymeric chlorhexidine inside-chamber layering. In this way, a reduction of bacterial load was obtained, which also could be interpreted as a prevention measure against peri-implantitis development.
Limitations of this research are linked to a relatively small number of participants in study and control groups. Also, the design of the study was characterized by restricted conditions for controlling laboratory phase of research, since all in vitro analyses were performed by lab specialists with further provision of obtained results in the form of reports. Taking into consideration the abovementioned, it was not possible to estimate the level of possible laboratory service bias, even though all used equipment was granted and checked for conformance marking, and laboratory specialists were previously calibrated for periodontally-aimed types of studies.
Despite the limitations mentioned, it was found that the progression of periodontal disease and the presence of peri-implantitis pathology are related to the absolute increase of bacterial load parameters of species such as A. actinomycetemcomitans, P. gingivalis, T. forsythia, P. intermedia, T. denticola with different levels of statistical dependencies. While using the RT-PCR method for the quantification of microbiome structure, it should be noted that the parameter of bacterial detection frequency is characterized by less sensitive interrelation with periodontal and peri-implant diseases, compared to the parameter of absolute bacterial load.
Conclusions
Periodontitis and peri-implantitis are associated with the same microbial pathogens, even though the distribution pattern of their bacterial load and detection frequency parameters registered with the RT-PCR could be distinct and linked to the individual patient-related conditions and the severity stage of pathology. Generally, the development of periodontal and peri-implant lesions is related to real raise of bacterial counts estimated for A. actinomycetemcomitans, P. gingivalis, T. forsythia, P. intermedia, T. denticola. None of the studied periopathogens presented a statistically compelling relationship with clinical bleeding on probing parameter, while the greatest correlation between periodontal pocket depth and the loss of clinical attachment level were registered for P. gingivalis, T. forsythia, T. denticola and A. actinomycetemcomitans bacterial loads. Quantitative verification of periodontal bacterial load levels before implantation could serve to advance arguments for the need to improve oral health and balance the microbial ecology, thus reducing the risk of potential peri-implant complications during a time-lapse survey.
Footnotes
Conflict of interest: The authors report no conflict of interest and the article is not funded or supported by any research grant.
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