The Characteristics of Microbiome and Cytokines in Healthy Implants and Peri-Implantitis of the Same Individuals

Submucosal Microbiome Profiles in Paired and Unpaired Samples From Healthy and Peri-Implantitis Dental Implants

BACKGROUND

This cross-sectional study aimed to compare the composition of the submucosal microbiome of peri-implantitis with paired and unpaired healthy implant samples.

METHODS

We evaluated submucosal plaque samples obtained in 39 cases, including 13 cases of peri-implantitis, 13 cases involving healthy implants from the same patient (paired samples), and 13 cases involving healthy implants from different individuals (unpaired samples). The patients were evaluated using next-generation genomic sequencing (Illumina) based on 16S rRNA gene amplification. The sequences were grouped according to the amplicon sequence variant (ASV) to define the taxonomic categories. Alpha diversity was analyzed using Shannon's and Simpson's indices, while beta diversity was evaluated using principal coordinate analysis, analysis of similarities, and permutational multivariate variance analysis. Additionally, UniFrac distances were evaluated using Quantitative Insights into Microbial Ecology 2. Finally, we evaluated between-group differences in the taxonomic components.

RESULTS

There were no significant between-group differences in alpha diversity. The average bacterial ratios of Filifactor alocis, Porphyromona endodontalis, Tannerella forsythia, Treponema denticola, Peptostreptococcaceae [Eubacterium nodatum], Desulfobulbus sp. HTM 041, and Mogibacterium timidum significantly differed between peri-implantitis samples and unpaired samples from the healthy implants (p < 0.05). However, there were few differences in the microbiota between peri-implantitis samples and those paired with healthy implants in the same patient.

CONCLUSIONS

Future studies comparing the microbiome compositions using sequencing techniques between healthy implants and implants with peri-implantitis should focus on retrieving samples from the same patient, especially in individuals with a history of periodontitis.

The Effect of Peri-Implant Therapy on the Expression of Th17-Related Cytokines in Patients with Peri-Implant Mucositis and Peri-Implantitis: A Prospective Longitudinal Study

Background/Objectives: Cytokines related to the Th17 response have been associated with peri-implant diseases; however, the effect of peri-implant therapy on their modulation remains underexplored. To evaluate the effect of peri-implant therapy on the expression of cytokines related to the Th17 response in the peri-implant crevicular fluid (PICF) (GM-CSF, IFN-γ, IL-1β, IL-4, IL-6, IL-10, IL-12 (p70), IL-17A, IL-21, IL-23, and TNF-α) of partially edentulous patients with peri-implant disease (PID). Methods: Thirty-seven systemically healthy individuals presenting with peri-implant mucositis (PIM) (n = 20) or peri-implantitis (PI) (n = 17) were treated and evaluated at baseline (T0) and three months after therapy (T1). Clinical parameters (probing depth (PD), clinical attachment level (CAL), plaque index, and bleeding on probing index (BoP), were evaluated. The PIM group underwent non-surgical therapy, while the PI group received a surgical approach. PICF was collected with absorbent paper strips and analyzed with a multiplex assay. Results: Eighty-eight implants were treated in 37 patients (56 in the PIM group and 32 in the PI group). After therapy, significant reductions in PD, CAL, plaque index, and BoP were observed in the PIM group (p < 0.05). In the PI group, significant reductions in PD, CAL, and BoP were noted (p < 0.05). The PIM group showed a significant reduction of IL-17A and TNF-α after therapy, while the PI group showed a significant reduction of IL-1β, IL-6, and TNF-α (p < 0.05). Conclusions: The peri-implant therapy for patients with PID reduced the expression of cytokines related to the Th17 response in PICF.

Revisiting Socransky's Complexes: A Review Suggesting Updated New Bacterial Clusters (GF-MoR Complexes) for Periodontal and Peri-Implant Diseases and Conditions

This review aimed to identify newly discovered bacteria from individuals with periodontal/peri-implant diseases and organize them into new clusters (GF-MoR complexes) to update Socransky's complexes (1998). For methodological development, the PCC (Population, Concept, Context) strategy was used for the focus question construction: "In patients with periodontal and/or peri-implant disease, what bacteria (microorganisms) were detected through laboratory assays?" The search strategy was applied to PubMed/MEDLINE, PubMed Central, and Embase. The search key terms, combined with Boolean markers, were (1) bacteria, (2) microbiome, (3) microorganisms, (4) biofilm, (5) niche, (6) native bacteria, (7) gingivitis), (8) periodontitis, (9) peri-implant mucositis, and (10) peri-implantitis. The search was restricted to the period 1998-2024 and the English language. The bacteria groups in the oral cavity obtained/found were retrieved and included in the GF-MoR complexes, which were based on the disease/condition, presenting six groups: (1) health, (2) gingivitis, (3) peri-implant mucositis, (4) periodontitis, (5) peri-implantitis, and (6) necrotizing and molar-incisor (M-O) pattern periodontitis. The percentual found per group refers to the number of times a specific bacterium was found to be associated with a particular disease. A total of 381 articles were found: 162 articles were eligible for full-text reading (k = 0.92). Of these articles, nine were excluded with justification, and 153 were included in this review (k = 0.98). Most of the studies reported results for the health condition, periodontitis, and peri-implantitis (3 out of 6 GF-MoR clusters), limiting the number of bacteria found in the other groups. Therefore, it became essential to understand that bacterial colonization is a dynamic process, and the bacteria present in one group could also be present in others, such as those observed with the bacteria found in all groups (Porphyromonas gingivalis, Tannarela forsythia, Treponema denticola, and Aggregatibacter actinomycetemcomitans) (GF-MoR's red triangle). The second most observed bacteria were grouped in GF-MoR's blue triangle: Porphyromonas spp., Prevotela spp., and Treponema spp., which were present in five of the six groups. The third most detected bacteria were clustered in the grey polygon (GF-MoR's grey polygon): Fusobacterium nucleatum, Prevotella intermedia, Campylobacter rectus, and Eikenella corrodens. These three geometric shapes had the most relevant bacteria to periodontal and peri-implant diseases. Specifically, per group, GF-MoR's health group had 58 species; GF-MoR's gingivitis group presented 16 bacteria; GF-MoR's peri-implant mucositis included 17 bacteria; GF-MoR's periodontitis group had 101 different bacteria; GF-MoR's peri-implantitis presented 61 bacteria; and the last group was a combination of necrotizing diseases and molar-incisor (M-I) pattern periodontitis, with seven bacteria. After observing the top seven bacteria of all groups, all of them were found to be gram-negative. Groups 4 and 5 (periodontitis and peri-implantitis) presented the same top seven bacteria. For the first time in the literature, GF-MoR's complexes were presented, gathering bacteria data according to the condition found and including more bacteria than in Socransky's complexes. Based on this understanding, this study could drive future research into treatment options for periodontal and peri-implant diseases, guiding future studies and collaborations to prevent and worsen systemic conditions. Moreover, it permits the debate about the evolution of bacterial clusters.

The Oral Microbiome of Peri-Implant Health and Disease: A Narrative Review

Peri-implantitis disease has increased significantly over the last years, resulting in increased failure of implants. Many factors may play a role in implant complications and failure, including ones related to the oral microbiota. This literature review aims to summarize the current knowledge of microbiome of implants in health and disease, focusing not only on the presence/absence of specific microbiota or on their relative abundance, but also on their phenotypic expression and their complex relationships with the host. The authors examined the MEDLINE database and identified key topics about peri-implant oral microbiome in health and disease. The peri-implant microbiome differs from that of the tooth, both in health and disease, as they are structurally and chemically different. The adhesion and formation of the peri-implant biofilm can be affected by the surface energy, topography, wettability, and electrochemical charges of the implant surface. In addition, the morphogenesis of the tissues surrounding the dental implant also differs from the tooth, making the dental implant more susceptible to bacterial infection. This interplay between the microbiome and the host immune system in peri-implant infections still needs to be elucidated.

CXCL5/CXCL8 induces neutrophilic inflammation in peri-implantitis

OBJECTIVE AND BACKGROUND

This research aimed to examine the role of C-X-C motif chemokine ligand 5 (CXCL5) and C-X-C motif chemokine ligand 8 (CXCL8; also known as IL-8) in neutrophilic inflammation triggered by peri-implantitis and to shed light on the underlying mechanisms that link them to the development of this condition.

MATERIALS

This study included 40 patients who visited the Department of Periodontology at Kyungpook University Dental Hospital. They were divided into two groups based on their condition: healthy implant (HI) group (n = 20) and peri-implantitis (PI) group (n = 20). Biopsy samples of PI tissue were collected from the patients under local anesthesia. HI tissue was obtained using the same method during the second implant surgery. To construct libraries for control and test RNAs, the QuantSeq 3' mRNA-Seq Library Prep Kit (Lexogen, Inc., Austria) was used according to the manufacturer's instructions. Samples were pooled based on representative cytokines obtained from RNA sequencing results and subjected to Reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Hematoxylin and eosin staining, and immunohistochemistry (IHC) analysis were performed to visually assess expression levels and analyze tissue histology. Student's t-test was employed to conduct statistical analyses.

RESULTS

Initially, heatmaps were used to examine gene expression variations between the HI and PI groups based on the results of RNA sequencing. Notably, among various cytokines, CXCL5 and CXCL8 had the highest expression levels in the PI group compared with the HI group, and they are known to be associated with inflammatory responses. In the gingival tissues, the expression of genes encoding cytokines such as interleukin (IL)-1β, tumor necrosis factor-alpha (TNF)-α, interleukin (IL)-6, and CXCL5/CXCL8 was assessed via RT-qPCR. The mRNA expression level of CXCL5/CXCL8 significantly increased in the PI group compared with the HI group (p < .045). Contrarily, the mRNA expression level of interleukin 36 receptor antagonist (IL36RN) significantly decreased (p < .008). IHC enabled examination of the distribution and intensity of CXCL5/CXCL8 protein expression within the tissue samples. Specifically, increased levels of CXCL5/CXCL8 promote inflammatory responses, cellular proliferation, migration, and invasion within the peri-implant tissues. These effects are mediated through the activation of the PI3K/Akt/NF-κB signaling pathway.

CONCLUSIONS

This study found that the PI sites had higher gene expression level of CXCL8/CXCL5 in the soft tissue than HI sites, which could help achieve more accurate diagnosis and treatment planning.

Metabolic profiling of peri-implant crevicular fluid in peri-implantitis

OBJECTS

This study aims to explore the etiology of peri-implantitis by comparing the metabolic profiles in peri-implant crevicular fluid (PICF) from patients with healthy implants (PH) and those with peri-implantitis (PI).

MATERIALS AND METHODS

Fifty-six patients were enrolled in this cross-sectional study. PICF samples were collected and analyzed using both non-targeted and targeted metabolomics approaches. The relationship between metabolites and clinical indices including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL) was examined. Additionally, submucosal microbiota was collected and analyzed using 16S rRNA gene sequencing to elucidate the association between the metabolites and microbial communities.

RESULTS

Significant differences in metabolic profiles were observed between the PH and PI groups, with 179 distinct metabolites identified. In the PI group, specific amino acids and fatty acids were significantly elevated compared to the PH group. Organic acids including succinic acid, fructose-6-phosphate, and glucose-6-phosphate were markedly higher in the PI group, showing positive correlations with mean PD, BOP, and MBL. Metabolites that increased in the PI group positively correlated with the presence of Porphyromonas and Treponema and negatively with Streptococcus and Haemophilus.

CONCLUSIONS

This study establishes a clear association between metabolic compositions and peri-implant condition, highlighting enhanced metabolite activity in peri-implantitis. These findings open avenues for further research into metabolic mechanisms of peri-implantitis and their potential therapeutic implications.

Oral Fluid Biomarkers for Peri-Implantitis: A Scoping Review

Peri-implantitis, a prevalent complication in dental implant therapy, poses a significant threat to long-term implant success. The identification of reliable biomarkers for the early detection and monitoring of peri-implantitis is crucial for timely intervention and improved treatment outcomes. Salivary and peri-implant sulcular fluid (PISF) biomarkers have become promising diagnostic tools in the field of implant dentistry. This scoping review aims to explore current studies in the literature on salivary and PISF biomarkers for peri-implantitis. A systematic search was conducted on 2 databases (PubMed and Scopus) to identify relevant studies published up to January 2023. A total of 86 articles were included, which underwent data extraction and analysis. Several biomarkers have been investigated in salivary and PISF samples for association with peri-implantitis. Investigations included a wide range of biomarkers, including inflammatory markers, matrix metalloproteinases and bone loss markers. The findings suggested that certain salivary and PISF biomarkers demonstrated potential in distinguishing healthy peri-implant conditions from peri-implantitis. Elevated levels of proinflammatory cytokines, such as interleukin-1β (IL-1β) and interleukin-6 (IL-6), tumour necrosis factor-alpha (TNF-α), and matrix metalloproteinases, have been consistently associated with peri-implantitis. Additionally, alterations in bone loss markers have shown potential as indicators of disease progression and treatment response. In conclusion, this scoping review provides an overview of current knowledge on salivary and PISF biomarkers for peri-implantitis. The identified biomarkers are promising as noninvasive diagnostic tools for early detection, monitoring, and personalised management of peri-implantitis. Future studies should focus on establishing standardised protocols and conducting well-designed clinical trials to validate the diagnostic accuracy and clinical relevance of these biomarkers.

Microbiological profile of peri-implantitis: Analyses of peri-implant microbiome

PURPOSE

To characterize the microbiome composition in peri-implant pocket of peri-implantitis and peri-implant sulcus controls using 16S rRNA gene sequencing.

MATERIALS AND METHODS

In this controlled clinical cross-sectional study, 23 subjects with control implants (n = 14) and diseased implants (peri-implantitis, n = 21) were included. The peri-implant pocket/sulcus was sampled and used to extract DNA and amplify the 16S rRNA gene using universal primers targeting the V3-V4 regions. The resulting 16S PCR amplicons were sequenced on Illumina MiSeq, and the sequences were processed using DADA2 and the Human Oral Microbiome Database (HOMD) as references. Alpha and Beta diversity, as well as core microbiome and differential abundance analyses, were performed using the MicrobiomeAnalyst workflow.

RESULTS

There were no significant differences in microbial diversity between control implants and implants with peri-implantitis (Shannon p = 0.82). Overall bacterial community structure assessed through beta diversity analysis was also not significantly different between the two groups (p = 0.18). However, high levels of Gram-negative bacteria were detected in peri-implant pockets compared to the control sulcus. Abundant species in peri-implantitis were Capnocytophaga leadbetteri, Treponema maltophilum, Peptostreptococcus, Neisseria, P. gingivalis, and Porphyromonas endodontali, Lactococcus lactis and Filifactor alocis (p < 0.05). Gram-positive bacteria such as Streptococcus salivaris, Prevotella melaninogenica, L. wadei, and Actinomyces spp. serve were more abundant in peri-implant control sulcus.

CONCLUSIONS

Peri-implant sulcus in control implants harbors predominantly Gram-positive bacteria, whereas pockets of implants with peri-implantitis harbor predominantly Gram-negative bacteria.

Spectroscopic insights into peri-implant mucositis and peri-implantitis: unveiling peri-implant crevicular fluid profiles using surface enhanced Raman scattering

The precise identification and differentiation of peri-implant diseases, without the need for intrusive procedures, is crucial for the successful clinical treatment and overall durability of dental implants. This work introduces a novel approach that combines surface-enhanced Raman scattering (SERS) spectroscopy with advanced chemometrics to analyse peri-implant crevicular fluid (PICF) samples. The primary purpose is to offer an unbiased evaluation of implant health. A detailed investigation was performed on PICF samples obtained from a cohort of patients exhibiting different levels of peri-implant health, including those with healthy implants, implants impacted by peri-implantitis, and implants with peri-implant mucositis. The obtained SERS spectra were analysed using canonical-powered partial least squares (CPPLS) to identify unique chemical characteristics associated with each inflammatory state. Significantly, our research findings unveil the presence of a common inflammatory SERS spectral pattern in cases of peri-implantitis and peri-implant mucositis. Furthermore, the SERS-based scores obtained from CPPLS were combined with established clinical scores and subjected to a linear discriminant analysis (LDA) classifier. Repeated double cross-validation was used to validate the method's capacity to discriminate different implant conditions. The integrated approach showcased high sensitivity and specificity and an overall balanced accuracy of 92%, demonstrating its potential to serve as a non-invasive diagnostic tool for real-time implant monitoring and early detection of inflammatory conditions.

The Microbiome of Peri-Implantitis: A Systematic Review of Next-Generation Sequencing Studies

(1) Introduction: Current evidence shows that mechanical debridement augmented with systemic and topical antibiotics may be beneficial for the treatment of peri-implantitis. The microbial profile of peri-implantitis plays a key role in identifying the most suitable antibiotics to be used for the treatment and prevention of peri-implantitis. This systematic review aimed to summarize and critically analyze the methodology and findings of studies which have utilized sequencing techniques to elucidate the microbial profiles of peri-implantitis. (2) Results: Fusobacterium, Treponema, and Porphyromonas sp. are associated with peri-implantitis. Veillonella sp. are associated with healthy implant sites and exhibit a reduced prevalence in deeper pockets and with greater severity of disease progression. Streptococcus sp. have been identified both in diseased and healthy sites. Neisseria sp. have been associated with healthy implants and negatively correlate with the probing depth. Methanogens and AAGPRs were also detected in peri-implantitis sites. (3) Methods: The study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42023459266). The PRISMA criteria were used to select articles retrieved from a systematic search of the Scopus, Cochrane, and Medline databases until 1 August 2023. Title and abstract screening was followed by a full-text review of the included articles. Thirty-two articles were included in the final qualitative analysis. (4) Conclusions: A distinct microbial profile could not be identified from studies employing sequencing techniques to identify the microbiome. Further studies are needed with more standardization to allow a comparison of findings. A universal clinical parameter for the diagnosis of peri-implantitis should be implemented in all future studies to minimize confounding factors. The subject pool should also be more diverse and larger to compensate for individual differences, and perhaps a distinct microbial profile can be seen with a larger sample size.

Difference in microbiome compositions of healthy peri-implant sulcus and peri-implantitis sulcus from the same patient

Objective

The objective of this study is to compare the microbiome of healthy peri-implant sulcus (C) and peri-implantitis sulcus (U) from the same patient and analyze the difference in the microbiome composition.

Materials and methods

DNA samples of subgingival biofilms from 10 C (control group) and 10 U (uncontrolled group) sites were sent to Microbiome Center in Korea Research Institute of Biomedical Science and analyzed using 16s rRNA gene amplification and sequencing (MiSeq, Illumina) and human oral microbiome database (HOMD).

Results

At the phylum level, Firmicutes and Proteobacteria were more abundant in group C, while Firmicutes and Bacteroidetes were dominant in group U. At the genus level, the core peri-implant microbiome was Streptococcus in group C. On the other hand, the core peri-implant microbiome was Porphyromonas, especially P. gingivalis in group U.

Conclusion

In this study, the microbiome composition of peri-implantitis sulcus was different from that of healthy peri-implant sulcus from the same patient. The peri-implantitis microbiome was pathogen-enriched and was similar to the microbiome associated with periodontitis.

Pro- and anti-inflammatory cytokines and osteoclastogenesis-related factors in peri-implant diseases: systematic review and meta-analysis

BACKGROUND

Pro- and anti-inflammatory cytokines are acknowledged, during inflammatory bone destruction, as key regulators of osteoclast and osteoblast differentiation and activity. However, evidence regarding the exact role of pro- and anti-inflammatory cytokines and osteoclastogenesis-related factors in peri-implant diseases is unclear. We aimed to execute a systematic review and meta-analysis about the pro- and anti-inflammatory cytokines and osteoclastogenesis-related factors levels in peri-implant diseases.

METHODS

The focused question was elaborated to summarize the levels of pro-and anti-inflammatory cytokines and osteoclastogenesis-related factors in tissue samples (mRNA) and biofluids (protein levels) of patients with/without peri-implant diseases. Electronic searches of the PubMed, Cochrane Controlled Trials Registry, Web of Science, EMBASE, Scopus and Google scholar databases were conducted for publications up to March 2023. Meta-analysis evaluating the mediator´s levels (protein levels by ELISA) in peri-implant crevicular fluid (PICF) were made. The effect size was estimated and reported as the mean difference. The 95% confidence interval was estimated for each mediator, and the pooled effect was determined significant if two-sided p-values < 0.05 were obtained.

RESULTS

Twenty-two publications were included in the systematic review (qualitative analysis), with nine of these subjected to meta-analyses (quantitative analysis). In the qualitative analysis, higher pro-inflammatory cytokines [Interleukin (IL)-1β, IL-6] and pro-osteoclastogenic mediator [Receptor Activator of Nuclear Factor-Kappa B ligand (RANKL)] levels were observed in PICF of individuals with peri-implant diseases in comparison to healthy individuals. Higher RANKL/osteoprotegerin (OPG) ratios were observed in PICF from individuals with peri-implant diseases in comparison to healthy individuals. Meta-analysis showed higher RANKL levels in diseased groups compared to controls.

CONCLUSIONS

The results showed that the levels of IL-1β, IL-6, IL-10, and RANKL/OPG are not balanced in peri-implant disease, suggesting that these mediators are involved in the host osteo-immunoinflammatory response related to peri-implantitis.

Short-term influence of antimicrobial photodynamic therapy as an adjuvant to mechanical debridement in reducing soft-tissue inflammation and subgingival yeasts colonization in patients with peri-implant mucositis

OBJECTIVE

The objective of this short-term follow-up study was to evaluate the influence of antimicrobial photodynamic therapy (aPDT) as an adjuvant to mechanical debridement (MD) in reducing soft-tissue inflammation and subgingival yeasts colonization (SYC) in patients with peri‑implant mucositis (PiM).

METHODS

Individuals diagnosed with PiM were included. Demographic data was collected using a questionnaire. Peri-implant plaque index (PI), bleeding index (BI), probing depth (PD), crestal bone levels and SYC were measured at baseline. Therapeutically, these individuals were divided into test and control groups. In the control-group patients underwent MD and in the test-group patients underwent MD with adjunct single session of aPDT. Clinical peri‑implant parameters and SYC were reassessed after 12-weeks. Correlation between age, gender and duration of implants with SYC and clinical peri‑implant status was assessed using logistic regression models. P < 0.05 was selected as an indicator of statistical significance.

RESULTS

The test and control-groups comprised of 24 and 23 individuals, respectively. In the test and control groups, toothbrushing twice daily was reported by 7 (29.2%) and 5 (21.7%) individuals, respectively. None of the individuals had ever used a dental floss. At baseline, there was no difference in peri‑implant PI, BI, PD and CBL in the test and control groups. At follow-up, peri‑implant PI (P < 0.01), BI (P < 0.01) and PD (P < 0.01) were significantly higher in the control compared with the test-group. At baseline, SYC in the test and control groups were 1865.3 ± 403.4 CFU/ml and 1963.7 ± 512.4 CFU/ml, respectively. At 90 days' follow-up, SYC in the test and control groups were 1472 ± 202.7 and 1538.4 ± 331.7 CFU/ml, respectively. There was no significant difference in SYC in both groups when baseline values were compared with 90 days' follow-up.

CONCLUSION

One session of aPDT after MC with adjunct aPDT is effective in reducing soft tissue inflammation but not SYC in patients with PiM.