Risk Factors for Peri-implant Diseases 3030391841, 9783030391843

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Risk Factors for Peri-implant Diseases Yorimasa Ogata Editor

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Risk Factors for Peri-implant Diseases

Yorimasa Ogata Editor

Risk Factors for Peri-implant Diseases

Editor Yorimasa Ogata Department of Periodontology Nihon University School of Dentistry at Matsudo Chiba Japan

ISBN 978-3-030-39184-3    ISBN 978-3-030-39185-0 (eBook) https://doi.org/10.1007/978-3-030-39185-0 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Acknowledgments

I had received an e-mail on 5th October 2018 (3 weeks before the American Academy of Periodontology (AAP) meeting at Vancouver) from Mr. Markus Bartels who is a member of the Clinical Medicine Department at Springer and responsible for developing the international book program in dentistry. When reviewing the AAP 2018 program he noted that I will give a lecture in the area of Risk Factors for Peri-implant Diseases, which is one of the topics he has identified as an interesting field. During the meeting, we met and discussed a potential book project in this area. After the AAP meeting, I selected all chapter authors and then Mr. Narendran Natarajan was assigned as the project coordinator for my project. He assisted us with the manuscript development process more efficiently to lessen my workload as volume editor. Ms. Deepika Devan helped me with the fine details of corrections of this book. Finally, I would like to thank all the authors of the chapters for their contributions to this book. December 2019

Yorimasa Ogata

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Contents

Prevalence and Etiology for Peri-­implant Diseases������������������������������   1 Yorimasa Ogata Polymicrobial Peri-Implant Infection����������������������������������������������������  11 Takahiko Shiba and Takayasu Watanabe Microbiological Factors of Peri-­Implantitis: Methodologies for Biofilm Analysis����������������������������������������������������������������������������������������  23 Anmar Adnan Kensara, Hanae Saito, Emmanuel F. Mongodin, and Radi Masri Microbiological Factors of Peri-­Implantitis: Characteristics and Significance���������������������������������������������������������������������������������������  35 Hanae Saito, Anmar Adnan Kensara, and Radi Masri Association of Periodontitis and Biologic Implant Complications������  47 Harlan J. Shiau, Hanae Saito, and Mark A. Reynolds Diabetes as a Systemic Factor for Peri-implantitis ������������������������������  59 Thomas W. Oates, Alyssa Dierkes, Katherine Ni, and Hanae Saito Diabetes and Smoking as the Potential Risk Factors for Peri-implant Diseases������������������������������������������������������������������������������  69 Ann M. Decker and Hom-Lay Wang Significance of Keratinized Mucosa and Implant Health��������������������  83 Guo-Hao Lin and Hom-Lay Wang The Impact of Implant Surface Characteristic and Genetics on Peri-implant Diseases �������������������������������������������������������������������������� 99 Shan-Huey Yu and Hom-Lay Wang Additional Risk Factors for Peri-­implant Diseases������������������������������ 115 Kitetsu Shin, Junichi Tatsumi, and Joichiro Hayashi

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Prevalence and Etiology for Peri-­implant Diseases Yorimasa Ogata

Contents Introduction

 1

Prevalence of Peri-implant Mucositis and Peri-implantitis

 2

Risk Factors/Indicators for Peri-­implant Diseases

 2

Pathogenesis of Peri-implant Diseases

 6

Structure and Composition of Periodontium and Peri-implant Tissue

 7

Conclusion

 8

References

 8

Introduction Peri-implant diseases are present in two forms: peri-implant mucositis and peri-implantitis [1– 4]. Peri-implant mucositis represents a reversible inflammatory reaction of the soft tissues surrounding dental implants in the absence of loss of supporting bone [3]. Peri-implantitis is characterized by inflammation in the connective tissues and progressive loss of supporting bones around dental implants [4]. They are the most frequent complications of dental implants [1, 5, 6]. However, the lack of widely accepted diagnostic criteria for peri-implant diseases makes interpretation of the published prevalence Y. Ogata (*) Department of Periodontology, Nihon University School of Dentistry at Matsudo, Chiba, Japan e-mail: [email protected] © Springer Nature Switzerland AG 2020 Y. Ogata (ed.), Risk Factors for Peri-implant Diseases, https://doi.org/10.1007/978-3-030-39185-0_1

very difficult [1, 7, 8]. Consensus of the 2017 World Workshop described that diagnosis of peri-implant mucositis requires the presence of peri-implant signs of inflammation and bleeding and/or suppuration on gentle probing with or without increased probing depth compared to previous examinations and the absence of bone loss beyond crestal bone level changes resulting from initial bone remodeling [5, 6]. Moreover, diagnosis of peri-implantitis requires the presence of peri-implant signs of inflammation and bleeding and/or suppuration on gentle probing with increased probing depth compared to previous examinations and the presence of bone loss beyond crestal bone level changes resulting from initial bone remodeling. If there is no data of previous examination, diagnosis of peri-implantitis can be based on the combination of presence of bleeding and/or suppuration on gentle probing, probing depth of ≥6 mm, and 1

Y. Ogata

2 Table 1  Diagnosis criteria of the 2017 World Workshop [5, 6] Peri-implant mucositis

Peri-­implantitis

Diagnosis criteria 1.  Presence of peri-implant signs of inflammation 2.  Bleeding and/or suppuration on gentle probing 3.  With or without increased probing depth compared to previous examinations 4. Absence of bone loss beyond crestal bone level changes resulting from initial bone remodeling 1.  Presence of peri-implant signs of inflammation 2.  Bleeding and/or suppuration on gentle probing 3.  Increased probing depth compared to previous examinations 4. Presence of bone loss beyond crestal bone level changes resulting from initial bone remodeling In the absence of previous examination data, diagnosis of peri-implantitis can be based on the combination of: 1.  Presence of bleeding and/or suppuration on gentle probing 2.  Probing depths of ≥6 mm 3.  Bone levels ≥3 mm apical of the most coronal portion of the intraosseous part of the implant

bone levels ≥3  mm apical of the most coronal portion of the intraosseous part of the implant [5, 6] (Table 1).

implantitis ranging from 19% to 65% and from 1% to 47%, respectively. Meta-analyses estimated weighted mean prevalence of peri-implant mucositis and peri-implantitis of 43% (95% confidence intervals (CI): 32–54%) and 22% (CI: 14–30%), respectively [12, 13]. Moreover, results from Prevalence of Peri-implant recent cross-sectional studies [14–20] reported Mucositis and Peri-implantitis prevalence for peri-implantitis within ranges A number of studies have reported fairly high comparable to those described by Derks and prevalence of peri-implant mucositis and peri-­ Tomasi [12]. A cross-sectional multicenter study implantitis [7–14]. Zitzmann and Berglundh enrolled Japanese patients with dental implants reported only a few studies provided data on who attended regular check-ups as part of a perithe prevalence of peri-implant diseases. Peri- odontal maintenance program. The prevalence of implant mucositis occurred in approximately patient-based peri-implant mucositis was 33.3% 80% of the subjects and in 50% of the implants. and peri-implantitis was 9.7% [21]. Peri-­ implantitis was found in 28–56% of subjects and in 12–43% of implant sites [7, 8]. The prevalence of peri-­ implant mucositis and peri- Risk Factors/Indicators for Peri-­ implantitis in 212 partially edentulous Brazilian implant Diseases subjects was 64.6% and 8.9%, respectively [9]. Peri-implant mucositis was found in 39.4% of There are reports that peri-implant diseases have subjects and 27.3% of implant sites, and peri- several risk factors/indicators, such as poor oral implantitis occurred in 47.1% of subjects and hygiene [8, 9, 15, 17, 20, 21], history of peri36.6% of implant sites in 109 Norwegian subjects odontitis [8, 9, 15, 16, 19–24], lack of compli[10]. Peri-implant mucositis occurred in 38.8% ance with regular maintenance therapy [19, 24, of patients and 21.6% of the studied implants, 25], smoking [8, 19, 20, 26–28], diabetes meland peri-­implantitis affected 16.3% of patients litus [9, 16], materials and surface characterisand 9.1% of the implants in 245 patients with tics of implant components [29–32], design of 964 dental implants attending the dental clinic implant-supported prostheses [33–35], keratinto comply with a periodontal maintenance pro- ized mucosa [19, 28, 36–39], excess cement [18, gram [11]. Current systemic review described the 40, 41], genetic factors [8, 27, 28, 42, 43] and prevalence of peri-implant mucositis and peri-­ occlusal overload [18, 37, 44, 45] (Table 2).

Prevalence and Etiology for Peri-implant Diseases Table 2  Risk factors/indicators for peri-implant diseases Element 1.  Poor oral hygiene Risk factors/ 2.  History of periodontitis indicators for peri-implant diseases 3. Lack of compliance with regular maintenance therapy 4. Smoking 5.  Diabetes mellitus 6. Materials and surface characteristics of implant components 7. Design of implant-­ supported prostheses 8.  Keratinized mucosa 9.  Excess cement 10.  Genetic factors 11.  Occlusal overload

 oor Oral Hygiene/History P of Periodontitis/Lack of Compliance with Regular Maintenance Therapy There are many reports describing the relationship between poor oral hygiene, history of periodontitis, lack of compliance with regular maintenance therapy and peri-implant diseases. The study consisted of 212 partially edentulous patients rehabilitated with osseointegrated implants. Higher total plaque scores were statistically associated with peri-implant diseases, and a very poor status of oral hygiene was highly associated with peri-implantitis [9]. The periodontal status was statistically associated with a worse peri-implant condition [9]. A total of 786 implants were placed in 239 patients. For peri-implant mucositis and peri-implantitis, the adjusted odds ratios indicate significant association with plaque index [15]. One hundred and eighty-six patients with 597 implants were examined clinically and radiographically. The history of periodontal disease was the most significant risk indicator for peri-­ implantitis and the level of oral hygiene was significantly associated with peri-implant mucositis [17]. A total 267 patients (one implant and one natural tooth per patient were included) were analyzed. The score of modified plaque index was significantly higher in the peri-implant mucositis and peri-implantitis groups than in the healthy peri-implant tissue group [21]. The data on the association between history of periodon-

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tal diseases and peri-implant diseases indicated slight chronic periodontitis (CP) was significantly associated with healthy peri-implant tissue and that moderate and severe CP were significantly associated with healthy peri-implant tissue and peri-implant mucositis [21]. One hundred and twelve partially edentulous patients with 246 implants were consecutively enrolled in private specialist practice. After 10 years, clinical measures were recorded, blinded to the initial patient periodontal condition. Patients with a history of periodontitis presented a lower survival rate and a statistically significant higher number of sites with peri-implant bone loss. Furthermore, CP patients who did not completely adhere to the supportive periodontal therapy (SPT) were found to present a higher implant failure rate [22, 24]. For 70 patients, comprehensive periodontal treatment was followed by installation of 165 dental implants. Subsequently, the patients entered a SPT program, and mean follow-up time was 7.9  years. In periodontitis susceptible patients, residual pockets (≥5 mm) at the end of active periodontal therapy represent a significant risk for the development of peri-implantitis and implant loss [23]. Among patients without regular SPT or maintenance program, peri-implant mucositis and peri-implantitis were reported to be a common finding with prevalences of 48.5% (subject) and 40% (implant), and 20% (subject) and 8.8% (implant) [19]. A five-year follow-up study of peri-implant diseases in subjects with and without preventive maintenance showed that the absence of preventive maintenance in individuals with pre-existing peri-implant mucositis was associated with a high incidence of peri-­ implantitis [25].

Smoking Smoking has been identified as a strong risk factor/indicator for peri-implant diseases [21, 28]. One hundred and thirty-four patients with 478 implants, installed during a 10-year period, were recruited for clinical and radiographic follow-up examinations. Smoking and lack of keratinized mucosa were associated with peri-implantitis

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at high odds ratio [19]. Results from a cross-­ sectional study (238 patients with a total of 512 implants; two-piece system with a tube-in-tube internal connection) showed a significant correlation of peri-implant mucositis with plaque and gender “male,” while peri-implantitis showed a significant correlation with plaque and smoking [20]. Results of systematic reviews and metaanalyses identified smoking as a significant risk factor for dental implant therapy and augmentation procedures accompanying implantations [26].

Y. Ogata

surface roughness in nine patients had all five of their original abutments exchanged to test abutments. There was a statistically significant difference between patients regarding the amount of accumulated plaque on the abutment surfaces and inflammatory cells, but no difference between the surface modification in relation to plaque accumulation or number of inflammatory cells [30]. Twenty edentulous subjects received two endosseous mandibular implants which were fitted with either a zirconia (ZrO2) or a titanium (Ti) abutment. Sulcular bacterial sampling and the assessment of probing pocket depth (PPD), recession and bleeding on probing (BOP) were performed at 2 weeks Diabetes Mellitus and 3 months post-surgery. No difference in health of the soft tissues adjacent to ZrO2 and Ti abutment Diabetes mellitus is recognized as a risk fac- surfaces or in early bacterial colonization could tor/indicator for periodontitis [46, 47]. A cross-­ be demonstrated, although mean PPD around Ti sectional study including 212 Brazilian subjects abutments was slightly deeper than around ZrO2 treated with dental implants suggested the pres- abutments after 3 months [31]. A cross-sectional ence of periodontitis and diabetes were statistically assessments of host-derived markers in periassociated with increased risk of peri-implantitis implant/gingival crevicular fluid and clinical con[9]. Another cross-sectional study was performed ditions at zirconia implants, titanium implants and on 96 patients with 225 implants that were placed contralateral natural teeth were performed. The between 1998 and 2003. The mean follow-up time mean plaque index (PI) was significantly lower for the patients was 10.9 years and the implant sur- at zirconia implants compared to teeth, while the vival rate were 91.6%. Peri-implantitis was found mean gingival index (GI), PPD and BOP were sigin 26% of the patients and 16% of the implants. nificantly higher. A correlation was found in the Peri-implantitis is associated with younger ages expression of interleukin (IL)-1RA, IL-8, granuand diabetes at the time of placement and with locyte colony stimulating factor (G-CSF), macroperiodontal status at the time of follow-up [16]. phage inflammatory protein (MIP)-1β and tumor necrosis factor (TNF)-α at zirconia implants and teeth. The levels of IL-1β and TNF-α were significantly higher at zirconia implants than at teeth. No Materials and Surface Characteristics of Implant Components [29–32] significant differences were found between zirconia and titanium implants. A correlation was found Teeth, denture and implants providing non-­ between the levels of IL-1RA, IL-8, granulocyte shedding surfaces allow the formation of thick macrophage (GM)-CSF and MIP-1β at zirconia biofilms. From a series of split-mouth studies, it and titanium implants [32]. could be concluded that both an increase in surface roughness and/or of the surface-free energy facilitates biofilm formation on restorative mate- Design of Implant-Supported rials. When both surface characteristics interact Prostheses with each other, surface roughness was predominant. The biofilm formation is also influenced by Prosthetic design is closely associated with peri-­ the chemical composition of biomaterial or the implant diseases. Bulky or excessive restorative type of coating [29]. The study investigating the contours may hinder daily oral hygiene measures early inflammatory response (4-week) to mucosa-­ by patients as well as diagnostic and supportive penetrating abutments prepares with varying care efforts by professional care providers dur-

Prevalence and Etiology for Peri-implant Diseases

5

ing scheduled maintenance. It is very important to make prosthesis to allow for adequate access for oral hygiene around implants [33]. Twentynine patients with one implant diagnosed with peri-implant mucositis were randomly assigned to a control and test group. All patients received non-surgical mechanical debridement at the implant sites and were instructed to brush around the implant twice daily using a gel provided for a period of 4 weeks. The test group received a chlorhexidine gel and the control group received a placebo gel. Non-surgical debridement and oral hygiene were effective in reducing periimplant mucositis, but adjunctive chlorhexidine gel application did not enhance the results. Implant with supramucosal restoration margins showed greater therapeutic improvement compared to those with submucosal restoration margins [34]. The 23 subjects were selected from consecutive patients. Out of them, 13 patients had no current periodontitis and bone loss; 5 had bone loss at teeth exceeding 1/3 of the length of the root but not current periodontitis and only 5 had current periodontitis. The site level analysis showed that 17 teeth (6%) of the 281 teeth present had ≥6 mm PPD. Of the total 109 implants, 58 (53%) had ≥6 mm PPD and 81 (74%) had no accessibility to proper oral hygiene. Fiftythree (49%) of the implants presenting periimplantitis were those with no accessibility for proper oral hygiene with respect to 5 (4%) of the implants with accessibility [35].

Excess Cement

Keratinized Mucosa

Genetic Factors

The systematic review and meta-analysis aim to investigate the effect of keratinized mucosa on various peri-implant health-related parameters. A lack of adequate keratinized mucosa around endosseous dental implants is associated with more plaque accumulation, tissue inflammation, mucosal recession and attachment loss [36]. However, it remains unclear whether a zone of keratinized mucosa is required to maintain the health of peri-implant tissue. Several reviews noted insufficient evidence for the need for keratinized mucosa around implants to maintain peri-­ implant health [28, 38, 39].

Single nucleotide polymorphisms (SNPs) are a risk factor/indicator for peri-implant diseases and may affect gene and protein expressions [28]. One hundred and eighty patients (53 were smokers) were recruited as part of a long-term oral implant maintenance program at the University of Bern, Switzerland, with 292 implants analyzed with respect to the occurrence of biological complications for their implants. Sixty-four of 180 (36%) patients tested positive for the IL-1 SNPs. There was a clear association for heavy smokers between a positive IL-1 genotype and implant complications [27]. The study population included 369

Excess cement was associated with clinical signs of peri-implant diseases [3, 4]. A crosssectional study including 183 patients treated with 916 implants showed an increased risk of 2.2 times for history of periodontal disease and 3.6 times for cemented restorations compared to screw-­retained protheses [18]. Seventy-seven patients with 129 implants (32 implants with mechanical failures, and 97 implants affected by biological complications) who had cementretained implant restorations were scheduled for regular maintenance. They were divided into two groups: implants in patients with history of periodontitis (group 1: 35 subjects) and implants in periodontitis-­free individuals (group 2: 42 subjects). Cement remnants were found in 73 implants of 129 total (56%), and peri-implant disease was evident in 62 of 73 implants with cement remnants (85%). All implants in group 1 developed peri-implantitis. The results suggest implants with cement remnants in patients with history of periodontitis may be more likely to develop peri-implantitis [40]. Scientific articles on prosthetic risk factors for peri-implantitis are investigated. Although the studies found on cement remnants have a high risk for bias, cement excess seems to be associated with peri-implant mucositis and possibly with periimplantitis, especially in patients with a history of periodontitis [41].

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Southeastern Europe Caucasians (180 with periimplantitis and 189 with healthy peri-implant tissues) whose genotype for CD14 and TNF-α, local protein levels of receptor activator nuclear factor kappa-B ligand (RANKL), and osteoprotegerin (OPG) from peri-implant crevicular fluid were analyzed. CD14–159C/T and TNF-α-308A/G polymorphisms are associated with peri-implantitis. Peri-implantitis patients with CC genotype at CD14–159 exhibited significantly higher concentrations of RANKL and relative ratio RANKL/ OPG when compared to patients with CT genotype, while concentration of biomarkers between different genotypes at TNF-α-308 remained insignificant [42]. A total of 103 Brazilian patients were investigated for association between IL-6 gene promoter G174C polymorphism and susceptibility to peri-implant disease and/or chronic periodontitis. The frequency of the genotype IL-6174GG and Allele G in healthy patients was different than in patients with peri-implant disease and chronic periodontitis. Therefore IL-6174GG genotype may be a common risk factor for both periodontitis and peri-­implant disease [43].

Occlusal Overload A cross-sectional study suggests a history of periodontal disease, cemented prostheses, and presence of wear facets on the prosthetic crown were identified as risk factors/indicators [18]. Four screw-shaped machined implants were placed bilaterally in the mandible of four beagle dogs, and prosthetic abutments were connected either in supra-occlusal contact with the opposite teeth (overloaded) or in infra-occlusal position (unloaded). In each dog, cotton floss ligatures were placed unilaterally around abutments to induce inflammation; the contralateral side was brushed 3 times a week for 12  months. In the presence of uninflamed peri-implant mucosa, overloading of implants did not increase bone resorption beyond the implant neck. Overloading aggravated plaque-induced bone resorption when peri-implant inflammation was present [44]. The effect of implant overload on bone/implant loss in clinically well-integrated implants is poorly

reported and provides little unbiased evidence to support a cause-and-effect relationship [45].

 athogenesis of Peri-implant P Diseases Several studies noted similarities in the pathogenesis of periodontitis and peri-implantitis [48–50]. Specially, periodontal pathogens could translocate from periodontally involved teeth to peri-implant sulci in partially edentulous patients [51, 52]. These findings highlight the importance of periodontal treatment of residual teeth before placement of dental implants [23, 53]. However, recently a new concept was proposed; although the clinical symptoms of peri-implantitis were similar to those of periodontitis, the core microbiota of the disease differed [54–56]. Candida species can be found in the buccal mucosa, subgingival biofilm and denture. They can co-­aggregate with bacteria and adhere to epithelium and titanium [57]. If Candida albicans exists in mixed-species biofilms, it can express virulence factors in biofilms that could contribute to peri-­implant disease, depending on associated bacteria species [58]. Recent studies suggested Epstein-Barr virus (EBV) is involved in the pathogenesis of periodontitis and peri-implant diseases. EBV is one of the most common viruses, infecting more than 90% of the adult population. EBV is transmitted from host to host by salivary contact, and it passes through the oropharyngeal epithelium to B lymphocytes, where it establishes lifelong latent infection. The latent form of EBV can be induced to enter the lytic replication cycle by treatment with phorbol 12-myristate 13-acetate, anti-immunoglobulin, calcium ionophore, TGF-β and butyric acid. The EBV BZLF1 gene product ZEBRA is a master regulator of the transition from latency to the lytic replication cycle. Butyric acid-producing periodontopathic bacteria may have the potential to trigger EBV reactivation in the inflamed periodontium [59]. Higher levels of EBV and Porphyromonas gingivalis were detected in subgingival plaque from deeper inflamed peri-­ implant sulci. The results suggest coexistence of EBV and P. gingivalis may serve as pathogenic factors for peri-implantitis [60].

Prevalence and Etiology for Peri-implant Diseases

Structure and Composition of Periodontium and Peri-implant Tissue The structure of gingival tissues from teeth and implant is different. They have a keratinized oral epithelium which ends at the crest of the gingival margin where it continues to a junctional epithelium (JE). At the tooth sites, JE terminates at the cemento-enamel junction. At the implant sites, JE ends at a varying distance from the gingival margin, always leaving a connective tissue portion coronal to the bone crest in direct contact with the implant surface. The connective tissue of the implant sites, major collagen fiber bundles extended from the alveolar bone crest to the gingival margin, and they were aligned in a direction parallel to the implant surface. The connective tissue of the tooth sites, the collagen fiber bundles extended from the supraalveolar root cementum to the marginal gingival connective tissue, to the alveolar bone crest, and circular fibers present in the connective tissue of the marginal gingiva

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and the supra alveolar connective tissue [61]. Experimental inflamed lesions in the peri-implant and periodontal tissues were induced by ligature placements in beagle dogs. Inflamed tissue from the tooth revealed the presence of an infiltrated connective tissue (ICT) in the gingiva and advanced loss of connective tissue attachment and bone. The pocket epithelium facing the tooth surface was ulcerated and consistently in contact with a subgingival plaque. Between the ICT and the alveolar bone crest, a normal noninflammatory connective tissue was present. Peri-­implant tissue showed a large ICT in the peri-implant mucosa and in advanced alveolar bone loss. A subgingival plaque was present and the adjacent pocket epithelium was invariably ulcerated. ICT occupied a substantial portion of the soft peri-implant tissue found to extend into the alveolar bone (Fig. 1) [13, 62]. The composition of the connective tissues that form an attachment to the dental implants was examined. Zone A was located immediately next to the implant surface, and zone B was the lateral portion that was continuous with zone A. Zone A

GM

GM

Subgingival plaque

Subgingival plaque

ICT

ICT BC

A

BC

B

Fig. 1  Schematic diagram of periodontitis (a) and peri-implantitis (b) sites (the gingival margin (GM) to the bone crest (BC)) obtained 1 month following ligature removal. Periodontitis site (a) showed the presence of an infiltrated connective tissue (ICT) in the gingival connective tissue and bone resorption. Between the ICT and BC, a noninflammatory connective tissue was present. Peri-implantitis (b) site revealed a large ICT in the peri-implant gingival connective tissue and in advanced alveolar bone loss. ICT occupied a substantial portion of the soft peri-implant tissue found to extend into the alveolar bone

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had an absence of blood vessels and abundance of fibroblasts which were interposed between thin collagen fibers. Zone B contained comparatively fewer fibroblasts, but more collagen fibers and blood vessels. The fibroblast-rich barrier tissue next to the titanium implant surface may play an important role in maintenance of a proper mucosal seal [63]. Soft tissue biopsies were obtained from peri-implantitis and periodontitis diseased sites (PPD ≥ 7 mm with BOP) and prepared for histologic and immunohistochemical analysis. In contrast to periodontitis lesions, the size of periimplantitis lesions was more than double and contained large area proportions, numbers and densities of plasma cells, macrophage and neutrophils. Peri-implantitis lesions extended to an apical position of the pocket epithelium and were not surrounded by non-ICT, as well as presented with significantly larger densities of vascular structures in the connective tissue area lateral to the ICT than within the infiltrate. These results suggest peri-implantitis and periodontitis lesions exhibit critical histopathologic differences [64].

Conclusion The prevalence of peri-implant mucositis and peri-­implantitis is very high. Therefore, dentists should provide detailed information on dental implant to patients, such as prevalence and etiology of peri-implant disease, need for periodontal treatment before implant placement and routine checkup after treatment. The quality of periodontal therapy before and after implant installation and patient compliance and motivation, as indicated by plaque control level, appear to be important in maintaining peri-­implant tissue health.

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Y. Ogata 2. Araujo MG, Lindhe J.  Peri-implant health. J Periodontol. 2018;89(Suppl 1):S249–56. 3. Heitz-Mayfield LJA, Salvi GE. Peri-implant mucositis. J Periodontol. 2018;89(Suppl 1):S257–66. 4. Schwarz F, Derks J, Monje A, Wang HL.  Peri-­ implantitis. J Periodontol. 2018;89(Suppl 1):S267–90. 5. Renvert S, Persson GR, Pirih FQ, Camargo PM.  Peri-implant health, peri-implant mucositis, and peri-implantitis: case definitions and d­ iagnostic considerations. J Periodontol. 2018;89(Suppl 1): S304–12. 6. Berglundh T, Armitage G, Araujo MG, Avila-Ortiz G, Blanco J, Camargo PM, Chen S, Cochran D, Derks J, Figuero E, Hämmerle CHF, Heitz-Mayfield LJA, Huynh-Ba G, Iacono V, Koo KT, Lambert F, McCauley L, Quirynen M, Renvert S, Salvi GE, Schwarz F, Tarnow D, Tomasi C, Wang HL, Zitzmann N.  Periimplant diseases and conditions: consensus report of workgroup 4 of the 2017 World Workshop on the classification of periodontal and peri-implant diseases and conditions. J Periodontol. 2018;89(Suppl 1):S313–8. 7. Zitzmann NU, Berglundh T.  Definition and prevalence of peri-implant diseases. J Clin Periodontol. 2008;35(8 Suppl):286–91. 8. Lindhe J, Meyle J, Group D of European Workshop on Periodontology. Peri-implant diseases: consensus report of the sixth European workshop on periodontology. J Clin Periodontol. 2008;35(8 Suppl): 282–5. 9. Ferreira SD, Silva GL, Cortelli JR, Costa JE, Costa FO.  Prevalence and risk variables for peri-implant disease in Brazilian subjects. J Clin Periodontol. 2006;33:929–35. 10. Koldsland OC, Scheie AA, Aass AM.  Prevalence of peri-implantitis related to severity of the disease with different degrees of bone loss. J Periodontol. 2010;81:231–8. 11. Mir-Mari J, Mir-Orfila P, Figueiredo R, Valmaseda-­ Castellón E, Gay-Escoda C.  Prevalence of peri-­ implant diseases. A cross-sectional study based on a private practice environment. J Clin Periodontol. 2012;39:490–4. 12. Derks J, Tomasi C.  Peri-implant health and disease. A systematic review of current epidemiology. J Clin Periodontol. 2015;42(Suppl 16):S158–71. 13. Salvi GE, Cosgarea R, Sculean A.  Prevalence and mechanisms of peri-implant diseases. J Dent Res. 2017;96(1):31–7. 14. Schuldt Filho G, Dalago HR, Oliveira de Souza JG, Stanley K, Jovanovic S, Bianchini MA.  Prevalence of peri-implantitis in patients with implant-­ supported fixed prostheses. Quintessence Int. 2014;45(10):861–8. 15. Aguirre-Zorzano LA, Estefanía-Fresco R, Telletxea O, Bravo M. Prevalence of peri-implant inflammatory disease in patients with a history of periodontal disease who receive supportive periodontal therapy. Clin Oral Implants Res. 2015;26(11):1338–44.

Prevalence and Etiology for Peri-implant Diseases 16. Daubert DM, Weinstein BF, Bordin S, Leroux BG, Flemming TF.  Prevalence and predictive factors for peri-implant disease and implant failure: a cross-­ sectional analysis. J Periodontol. 2015;86(3):337–47. 17. Konstantinidis IK, Kotsakis GA, Gerdes S, Walter MH.  Cross-sectional study on the prevalence and risk indicators of peri-implant diseases. Int J Oral Implantol. 2015;8(1):75–88. 18. Dalago HR, Schuldt Filho G, Rodrigues MA, Renvert S, Bianchini MA. Risk indicators for peri-implantitis. A cross-sectional study with 916 implants. Clin Oral Implants Res. 2017;28(2):144–50. 19. Rokn A, Aslroosta H, Akbari S, Najafi H, Zayeri F, Hashemi K. Prevalence of peri-implantitis in patients not participating in well-designed supportive periodontal treatments: a cross-sectional study. Clin Oral Implants Res. 2017;28(3):314–9. 20. Schwarz F, Becker K, Sahm N, Horstkemper T, Rousi K, Becker J.  The prevalence of peri-implant diseases for two-piece implants with an internal tube-­ in-­tube connection: a cross-sectional analysis of 512 implants. Clin Oral Implants Res. 2017;28(1):24–8. 21. Ogata Y, Nakayama Y, Tatsumi J, Kubota T, Sato S, Nishida T, Takeuchi Y, Onitsuka T, Sakagami R, Nozaki T, Murakami S, Matsubara N, Tanaka M, Yoshino T, Ota J, Nakagawa T, Ishihara Y, Ito T, Saito A, Yamaki K, Matsuzaki E, Hidaka T, Sasaki D, Yaegashi T, Yasuda T, Shibutani T, Noguchi K, Araki H, Ikumi N, Aoyama Y, Kogai H, Nemoto K, Deguchi S, Takiguchi T, Yamamoto M, Inokuchi K, Ito T, Kado T, Furuichi Y, Kanazashi M, Gomi K, Takagi Y, Kubokawa K, Yoshinari N, Hasegawa Y, Hirose T, Sase T, Arita H, Kodama T, Shin K, Izumi Y, Yoshie H. Prevalence and risk factors for peri-implant diseases in Japanese adult dental patients. J Oral Sci. 2017;59:1–11. 22. Roccuzzo M, Bonino F, Aglietta M, Dalmasso P. Ten-­ year results of a three arms prospective cohort study on implants in periodontally compromised patients. Part 2: clinical results. Clin Oral Implants Res. 2011;23:389–95. 23. Pjetursson BE, Helbling C, Weber HP, Matuliene G, Salvi GE, Brägger U, Schmidlin K, Zwahlen M, Lang NP.  Peri-implantitis susceptibility as it relates to periodontal therapy and supportive care. Clin Oral Implants Res. 2012;23:888–94. 24. Roccuzzo M, De Angelis N, Bonino L, Aglietta M. Ten-year results of a three-arm prospective cohort study on implants in periodontally compromised patients. Part 1: implant loss and radiographic bone loss. Clin Oral Implants Res. 2010;21:490–6. 25. Costa FO, Takenaka-Martinez S, Cota LO, Ferreira SD, Silva GL, Costa JE. Peri-implant disease in subjects with and without preventive maintenance: a 5-year follow-up. J Clin Periodontol. 2012;39:173–81. 26. Strietzel FP, Reichart PA, Kale A, Kulkarni M, Wegner B, Küchler I. Smoking interferes with the prognosis

9 of dental implant treatment: a systematic review and meta-analysis. J Clin Periodontol. 2007;34:523–44. 27. Gruica B, Wang HY, Lang NP, Buser D.  Impact of IL-1 genotype and smoking status on the prognosis of osseointegrated implants. Clin Oral Implants Res. 2004;15(4):393–400. 28. Heitz-Mayfield LJ.  Peri-implant diseases: diagnosis and risk indicators. J Clin Periodontol. 2008;35(8 Suppl):292–304. 29. Teughels W, Van Assche N, Sliepen I, Quirynen M.  Effect of material characteristics and/or surface topography on biofilm development. Clin Oral Implants Res. 2006;17(Suppl 2):68–81. 30. Wennerberg A, Sennerby L, Kultje C, Lekholm U.  Some soft tissue characteristics at implant abutments with different surface topography. A study in humans. J Clin Periodontol. 2003;30(1):88–94. 31. van Brakel R, Cune S, van Winkelhoff AJ, de Putter C, Verhoeven JW, van der Reijden W. Early bacterial colonization and soft tissue health around zirconia and titanium abutments: an in vivo study in man. Clin Oral Implants Res. 2011;22(6):571–7. 32. Cionca N, Hashim D, Cancela J, Giannopoulou C, Mombelli A. Pro-inflammatory cytokines at zirconia implants and teeth. A cross-sectional assessment. Clin Oral Investig. 2016;20(8):2285–91. 33. Dixon DR, London RM. Restorative design and associated risks for peri-implant diseases. Periodontol. 2019;81(1):167–78. 34. Heitz-Mayfield LJ, Salvi GE, Botticelli D, Mombelli A, Faddy M, Lang NP.  Implant Complication Research Group. Anti-infective treatment of peri-­ implant mucositis: a randomised controlled clinical trial. Clin Oral Implants Res. 2011;22(3):237–41. 35. Serino G, Ström C. Peri-implantitis in partially edentulous patients: association with inadequate plaque control. Clin Oral Implants Res. 2009;20(2):169–74. 36. Lin GH, Chan HL, Wang HL.  The significance of keratinized mucosa on implant health: a systematic review. J Periodontol. 2013;84:1755–67. 37. Hämmerle CHF, Tarnow D. The etiology of hard- and soft-tissue deficiencies at dental implants: a narrative review. J Periodontol. 2018;89(Suppl 1):S291–303. 38. Wennström JL, Derks J. Is there a need for keratinized mucosa around implants to maintain healthand tissue stability? Clin Oral Implants Res. 2012;23(Suppl 6):136–46. 39. Brito C, Tenenbaum HC, Wong BK, Schmitt C, Nogueira-Filho G.  Is keratinized mucosa indispensable to maintain peri-implant health? A systematic review of the literature. J Biomed Mater Res B Appl Biomater. 2014;102:643–50. 40. Linkevicius T, Puisys A, Vindasiute E, Linkeviciene L, Apse P.  Does residual cement around implant-­ supported restorations cause peri-implant disease? A retrospective case analysis. Clin Oral Implants Res. 2013;24(11):1179–84.

10 41. Pesce P, Canullo L, Grusovin MG, de Bruyn H, Cosyn J, Pera P.  Systematic review of some prosthetic risk factors for periimplantitis. J Prosthet Dent. 2015;114(3):346–50. 42. Rakic M, Petkovic-Curcin A, Struillou X, Matic S, Stamatovic N, Vojvodic D.  CD14 and TNFα single nucleotide polymorphisms are candidates for genetic biomarkers of peri-implantitis. Clin Oral Investig. 2015;19(4):791–801. 43. Casado PL, Villas-Boas R, de Mello W, Duarte ME, Granjeiro JM.  Peri-implant disease and chronic periodontitis: is interleukin-6 gene promoter polymorphism the common risk factor in a Brazilian population? Int J Oral Maxillofac Implants. 2013;28(1):35–43. 44. Kozlovsky A, Tal H, Laufer BZ, Leshem R, Rohrer MD, Weinreb M, Artzi Z. Impact of implant overloading on the peri-implant bone in inflamed and non-­ inflamed peri-implant mucosa. Clin Oral Implants Res. 2007;18(5):601–10. 45. Naert I, Duyck J, Vandamme K.  Occlusal over load and bone/implant loss. Clin Oral Implants Res. 2012;23(Suppl 6):95–107. 46. Taylor GW, Borgnakke WS.  Periodontal disease: associations with diabetes, glycemic control and complications. Oral Dis. 2008;14(3):191–203. 47. Genco RJ, Borgnakke WS. Risk factors for periodontal disease. Periodontol 2000. 2013;62(1):59–94. 48. Mombelli A, van Oosten MA, Schurch E Jr, Land NP. The microbiota associated with successful or failing osseointegrated titanium implants. Oral Microbiol Immunol. 1987;2:145–51. 49. Renvert S, Lindahl C, Renvert H, Persson GR. Clinical and microbiological analysis of subjects treated with Brånemark or AstraTech implants: a 7-year follow-up study. Clin Oral Implants Res. 2008;19:342–7. 50. Shibli JA, Melo L, Ferrari DS, Figueiredo LC, Faveri M, Feres M. Composition of supra- and subgingival biofilm of subjects with healthy and diseased implants. Clin Oral Implants Res. 2008;19: 975–82. 51. Botero JE, González AM, Mercado RA, Olave G, Contreras A.  Subgingival microbiota in peri-implant mucosa lesions and adjacent teeth in partially edentulous patients. J Periodontol. 2005;76:1490–5. 52. Quirynen M, Vogels R, Peeters W, van Steenberghe D, Naert I, Haffajee A. Dynamics of initial subgingival colonization of ‘pristine’ peri-implantpockets. Clin Oral Implants Res. 2006;17:25–37.

Y. Ogata 53. Cho-Yan Lee J, Mattheos N, Nixon KC, Ivanovski S.  Residual periodontal pockets are a risk indicator for peri-implantitis in patients treated for periodontitis. Clin Oral Implants Res. 2012;23:325–33. 54. Koyanagi T, Sakamoto M, Takeuchi Y, Maruyama N, Ohkuma M, Izumi Y. Comprehensive microbiological findings in peri-implantitis and periodontitis. J Clin Periodontol. 2013;40:218–26. 55. Maruyama N, Maruyama F, Takeuchi Y, Aikawa C, Izumi Y, Nakagawa I. Intraindividual variation in core microbiota in peri-implantitis and periodontitis. Sci Rep. 2014;4:6602. 56. Shiba T, Watanabe T, Kachi H, Koyanagi T, Maruyama N, Murase K, Takeuchi Y, Maruyama F, Izumi Y, Nakagawa I.  Distinct interacting core taxa in cooccurrence networks enable discrimination of polymicrobial oral diseases with similar symptoms. Sci Rep. 2016;6:30997. 57. Cortellini S, Favril C, De Nutte M, Teughels W, Quirynen M.  Patient compliance as a risk factor for the outcome of implant treatment. Periodontol 2000. 2019;81(1):209–25. 58. Cavalcanti YW, Wilson M, Lewis M, Del-Bel-Cury AA, da Silva WJ, Williams DW.  Modulation of Candida albicans virulence by bacterial biofilms on titanium surfaces. Biofouling. 2016;32(2):123–34. 59. Kato A, Imai K, Ochiai K, Ogata Y. Higher prevalence of Epstein-Barr virus DNA in deeper periodontal pockets of chronic periodontitis in Japanese patients. PLoS One. 2013;8(8):e71990. 60. Kato A, Imai K, Sato H, Ogata Y.  Prevalence of Epstein-Barr virus DNA and Porphyromonas gingivalis in Japanese peri-implantitis patients. BMC Oral Health. 2017;17:148. 61. Berglundh T, Lindhe J, Ericsson I, Marinello CP, Liljenberg B, Thomsen P.  The soft tissue barrier at implants and teeth. Clin Oral Implants Res. 1991;2(2):81–90. 62. Lindhe J, Berglundh T, Ericsson I, Liljenberg B, Marinello C.  Experimental breakdown of peri-­ implant and periodontal tissues. A study in the beagle dog. Clin Oral Implants Res. 1992;3:9–16. 63. Moon IS, Berglundh T, Abrahamsson I, Linder E, Lindhe J. The barrier between the keratinized mucosa and the dental implant. An experimental study in the dog. J Clin Periodontol. 1999;26(10):658–63. 64. Carcuac O, Berglundh T.  Composition of human peri-implantitis and periodontitis lesions. J Dent Res. 2014;93(11):1083–8.

Polymicrobial Peri-Implant Infection Takahiko Shiba and Takayasu Watanabe

Contents  olymicrobial Infection and the Human Oral Cavity as a Space for P Microbial Colonization

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Bacteriological History of Periodontitis

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Bacteriological History of Peri-­Implant Infection

 14

An Example of Investigating Bacterial Interaction at Peri-­Implantitis Sites

 15

Future Perspectives of Polymicrobial Peri-Implant Infection

 18

References

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Polymicrobial Infection and the Human Oral Cavity as a Space for Microbial Colonization

a pathogen to fulfill four criteria, have contributed to the progress of contemporary medicine. However, for several decades, the colonization of multiple microbial species has been considered as a set of causative agents for infectious diseases [1, 2]. To date, an infection by multiple microbial species is called “polymicrobial infection” [3]. Polymicrobial infection can be established by the colonization of not only multiple polybacterial species but also multiple polyviral and polymycotic species. Multiple species in a polymicrobial infection can belong to one or several of these taxonomic groups. In the disease etiology of animals and humans, examples of polymicrobial infection have been reported. In cows and pigs, polymicrobial infection occurs in the respiratory disease complex [4, 5]. In humans, abscess in the liver and perirectal area are caused by a polybacterial infection [6, 7]. The causative microbes can be derived from

With regard to the etiology of infectious diseases, a main principle for more than one century has been that a single microbial species is the only causative agent. Koch’s postulates, which require

T. Shiba Department of Periodontology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan e-mail: [email protected] T. Watanabe (*) Department of Chemistry, Nihon University School of Dentistry, Tokyo, Japan e-mail: [email protected] © Springer Nature Switzerland AG 2020 Y. Ogata (ed.), Risk Factors for Peri-implant Diseases, https://doi.org/10.1007/978-3-030-39185-0_2

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the aggregation of multiple microbial species at a healthy body site, called the “microbiota.” In this case, a commensal microbiota is formed at a body site to contribute to the healthy state. The microbial composition is balanced within the microbiota, but disease occurs when the microbiota loses its balance, which causes a state of dysbiosis or dysbiotic microbiota [8]. On the other hand, there is another case that the causative microbes can be entirely foreign such as contaminating microbes from another environment. This can occur at a body site where the invasion of any microbes is strictly restricted. The term “microbiota”, equivalent to “microbiome”, is used to describe a community of multiple bacterial species as well as multiple microbes, and tends to be preferred over the term “flora”, which is a term originally used for plants. The human oral cavity has the potential to cause polymicrobial infectious diseases. In the oral cavity, microbiota is formed to harbor hundreds of microbial species, which primarily consist of bacteria [9]. This feature is common among epithelial tissues such as skin and the gastrointestinal tract. In actuality, the intestinal microbiota has been actively studied as a “new organ” that affects a host’s health [10]. However, the oral cavity is remarkably different from other epithelial tissues with respect to the presence of hard tissues. Teeth, the only hard tissue exposed outside of the body, are appropriate sites for microbial colonization. Moreover, the dentition provides complicated shapes in the mucosal surfaces where microbes can colonize. The gingival sulcus has a particularly unique structure where the hard tissue is in contact with the soft tissue. Low oxygen tension in the gingival sulcus occurs when the depth of the sulcus is abnormally increased, which is called a periodontal pocket [11], and provides a habitat for anaerobic microbes. Thus, the condition for microbial colonization is diverse in the oral cavity and causes a variety of polymicrobial infectious diseases in the oral cavity. Periodontitis, an inflammatory disease of the periodontal tissue, is an oral polymicrobial infec-

T. Shiba and T. Watanabe

tious disease. Nearly one-half of people over 30 years old in the United States were reported to have periodontitis [12]. The prevalence of periodontitis among adults is a major cause of tooth loss [13], and is associated with various systemic diseases such as diabetes [14], nonalcoholic fatty liver disease [15, 16], and threatened preterm labor [17]. By contrast, peri-implantitis is an inflammatory disease caused by polymicrobial infection of tissue surrounding a dental implant. Peri-implant mucositis is inflammation that is localized only at the peri-implant mucosa. It becomes peri-implantitis when the inflammation extends to absorb the alveolar bone. Periimplant infectious diseases are becoming more prevalent with the increase in implant treatment. Periodontitis and peri-implantitis occur around hard tissue and artificial material, respectively, and present with similar clinical symptoms [18]. However, they have several differences with respect to the speed of disease progression, the effectiveness of clinical treatment, and the stability of the tissue after treatment (Fig. 1) [19–21]. Although these factors may be associated with the difference in surface property and the surrounding tissue structure between a tooth and an implant, an open question is why periodontitis and peri-implantitis have differences. From the next section, we will highlight the history in the understanding of peri-implantitis, based on a bacteriological point of view.

Bacteriological History of Periodontitis Before highlighting the history of peri-­implantitis, we will review how periodontitis has been studied bacteriologically. In the 1950s, periodontitis was believed to occur and progress by the accumulation of plaque, regardless of its bacterial content [22, 23]. This belief was called the “nonspecific plaque hypothesis.” It was supported by a study that demonstrated the occurrence of gingivitis by refraining from plaque control [24]. On the other hand, there were a certain number of exceptions

Polymicrobial Peri-Implant Infection

13

Peri-implantitis

Clinical symptoms

Periodontitis

Similar

Progression speed

Fast

Slow

Effectiveness of treatment

Poor

Fine

Microbiota

Live Dead

Previous studies : could not distinguish

DNA

live and dead bacteria

Our study : could capture

mRNA

transcriptional activeness

Fig. 1  Similarity and differences between peri-­implantitis and periodontitis. Peri-implantitis and periodontitis are similar in the clinical symptoms, whereas they differ in the progression speed and effectiveness of treatment. A microbiota at the lesion has been focused on, but the DNA-based studies could not find whether the microbiota

differs between the two diseases, because of detecting not only live but also dead bacteria. Our study aimed to capture the transcriptional activeness by conducting the RNA-seq for the peri-implantitis and periodontitis samples collected from the same individuals

that could not be applied to the hypothesis. The exceptions included the actual cases in which the amount of plaque was not in proportion to the progression speed of periodontitis, and in which periodontitis was observed site-specifically. The inconsistency between the nonspecific plaque hypothesis and these actual cases led scientists to propose the “specific plaque hypothesis” in which particular bacterial species were suspected of being responsible for the etiology of periodontitis [25]. However, potential pathogens for periodontitis could not be specified, because multiple bacterial species were culturable from the periodontal pocket and many other bacterial species may have been unculturable [26]. The development of DNA-based detection methods for bacteria has contributed to the characterization of oral bacterial species in the etiology of periodontitis. The study by Socransky et al. using checkerboard DNA-DNA hybridization was the comprehensive outcome of the spe-

cific plaque hypothesis [27]. They reported that a bacterial group consisting of Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola exhibited a strong association with pocket depth and bleeding on probing. This bacterial group was defined as the “red complex,” and tens of other bacterial species in the periodontal pocket were grouped in other complexes with regard to their association with disease symptoms. The classification of bacterial species with complexes remains influential in modern bacteriology of periodontitis; however, this classification has contradictions. Old studies have demonstrated that Streptococcus mutans and Streptococcus salivarius, which were not suspected of being causative agents of periodontitis in the complex-based classification, caused attachment loss of the periodontal tissue in an experimental animal model [28, 29]. Another example of the contradictions was instances in which bacterial species in a complex responsible

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for periodontitis were detected but periodontitis was not observed [26]. In the 1990s, the technology of high-­throughput DNA sequencing was embodied as a next-generation sequencer (NGS) [30]. The t­echnology was epoch-making in that it enabled scientists to comprehensively capture microbial contents within a microbiota. In particular, bacteria were fit to be captured by the NGS because 16S rRNA genes of bacteria are indispensable for all bacterial species, and have an approximate length of 1-kbp of nucleotide sequences that are highly conserved among bacterial species [31]. The project of collecting 16S rRNA sequences of oral bacterial species was conducted to construct the Human Oral Microbiome Database (HOMD) [9]. In this project, more than 600 bacterial taxa were identified as oral bacteria, whereas hundreds of other bacterial taxa in the plaque were taxonomically unclassifiable. Following this finding, a study was conducted to compare 16S rRNA gene-based bacterial profiles in the subgingival plaque between patients with chronic periodontitis and periodontally healthy controls [32]. The proportion of the amount of red complex species to all detected species was indeed high at the periodontitis sites in the study, but the microbiota in the periodontitis group included the bacterial species that were not previously regarded as being responsible for the etiology of periodontitis. This finding suggested that dysbiosis within a microbiota potentially has a role in periodontal inflammation. Hajishengallis proposed the “keystone-­pathogen hypothesis,” in which a particular bacterial species such as P. gingivalis is a keystone pathogen that alters the balance within a microbiota and causes inflammation through dysbiosis despite the low abundance of the pathogen [33, 34]. Crosstalk during the interaction between the bacterial species and host cells on the periodontal tissue is receiving attention as being involved in causing a dysbiotic state [35].

Bacteriological History of Peri-­Implant Infection An association of bacteria with the etiology of peri-implantitis, on the other hand, was indicated

T. Shiba and T. Watanabe

after the course of investigations for periodontitis and soon after the establishment of a basic methodology for implant treatment in the 1980s. The bacterial species responsible for the etiology of periodontitis were detected at peri-implantitis sites by using a culture-dependent method [36]. Lang et al. experimentally demonstrated that periimplantitis in monkeys was caused by the accumulation of plaque at peri-implant sites, and had similar clinical symptoms as periodontitis [37]. Tanner et al. demonstrated a similarity in culturable bacterial species between peri-­ implantitis and periodontitis sites when their clinical states were similar [38]. The trend of considering periimplantitis and periodontitis bacteriologically similar remained potent after the use of cultureindependent DNA-based detection methods became prevalent. The genotypes of P. gingivalis, which were investigated by pulsed-­field gel electrophoresis, were the same between peri-implantitis and periodontitis sites in the same individual [39], suggesting the transmission of pathogenic bacteria from a periodontitis site to a peri-implant region [40]. The possibility of transmission was supported by a study that used quantitative PCR in which the extraction of all teeth reduced but could not eradicate the bacterial species that were etiologically responsible for periodontitis, such as P. gingivalis, T. forsythia, Aggregatibacter actinomycetemcomitans, and Prevotella intermedia, in the saliva and at the tongue [41]. In the edentulous oral cavity, bacterial species in the saliva and on the tongue may have been derived from the periodontitis sites while the teeth were still alive, and may be transmitted to peri-implant sites when the implants are newly installed. However, one study also exists in which bacterial species that were not previously considered to be responsible for periodontitis were detected at peri-implantitis sites [18]. Although this study was based on a culture-dependent method, the association of bacterial composition within the microbiota at the peri-implantitis sites with the etiology of peri-implantitis was gradually being recognized. Checkerboard DNA-DNA hybridization was used to demonstrate differences in bacterial composition within a microbiota between peri-implantitis sites and healthy peri-implant

Polymicrobial Peri-Implant Infection

sites. The red complex species was in high proportion among all bacterial species at the supragingival and subgingival regions of peri-implantitis sites, compared to healthy controls, whereas the beneficial bacterial species at the peri-implantitis sites were lower than those in the controls [42]. Open flap debridement reduced the amount of the red complex species and Fusobacterium nucleatum subsp. nucleatum at the peri-implantitis sites [43]. Helicobacter pylori, which primarily inhabits the human stomach, was detected by checkerboard DNA-DNA hybridization at the peri-implantitis sites [44]. Following the trend of bacteriology for periodontitis, NGSs have facilitated the investigation of bacterial composition at peri-implantitis sites. Kumar et al. reported that the diversity in a microbiota was significantly lower at the peri-­ implantitis sites than at the healthy peri-implant sites [45]. Two studies have demonstrated that peri-implantitis sites harbored in abundance particular bacterial species that were previously not recognized as etiologically important [46, 47]. Both of these studies demonstrated that different species were highly abundant among the peri-­implantitis species; Eubacterium nodatum, Eubacterium brachy, and Eubacterium saphenum were predominant at the peri-implantitis sites, based on the report of Tamura et al. [46], whereas the proportion of Dialister invisus and Mitsuokella sp. was higher at the peri-implantitis sites than at the healthy peri-implant sites, based on the report of da Silva et  al. [47] On the other hand, both of the aforementioned two studies commonly showed that Filifactor alocis was highly abundant at the peri-implantitis sites, suggesting its possible involvement in the etiology of peri-implantitis. The aforementioned Eubacterium species are asaccharolytic anaerobic Gram-positive rods (AAGPRs) [48]. Their association with the etiology of periodontitis is suggested because of their ability to produce butyrate [49], which disturbs the function of host cells [50–53]. Filifactor alocis, formerly classified as Fusobacterium alocis, is also an AAGRP and is able to persist under oxidative stress [54]. Although F. alocis reportedly interacts with P. gingivalis synergistically [54],

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most of its virulence traits remain unknown [55]. These bacterial species may have a role in causing dysbiosis within the microbiota at periimplantitis sites.

 n Example of Investigating A Bacterial Interaction at Peri-­Implantitis Sites As described in the previous sections, peri-­ implantitis has features different from those of periodontitis such as the progression speed and effectiveness of clinical treatment, whereas peri-­ implantitis seems to be similar to periodontitis with respect to the bacterial composition within the microbiota. The similarity was suggested, based on the findings of previous studies that investigated the microbiota from different individuals but did not compare the bacterial composition within the same individual. On the other hand, a few studies exist in which the bacterial composition was compared between peri-­implantitis and periodontitis sites within the same individual [56–59]. However, these studies were inconsistent regarding the similarity of bacterial composition. Some of these studies demonstrated that the bacterial composition seemed different between the peri-implantitis and periodontitis sites [56–58], whereas one study showed no significant difference in bacterial composition between the periimplantitis and periodontitis sites [59]. The use of DNA for detecting bacterial species was a possible reason for the inconsistency among these studies (Fig.  1) because the DNA molecule remains chemically stable over a long period [60], even if it is derived from dead bacterial cells [61]. DNA is versatile for detecting and analyzing particular organisms because of its chemical stability, but the information predicted from DNA may be biased by the contamination of dead organisms. In this regard, RNA may be suited for investigating bacterial composition within a microbiota, although RNA is generally more difficult to handle than is DNA because the degradability is higher for RNA than for DNA [62]. In addition, the degradability of RNA allows scientists to capture the transcriptional

T. Shiba and T. Watanabe

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state at the time of collecting RNA.  In a previous study, we actually observed that the bacterial composition predicted with 16S rRNA was different from the composition predicted with 16S rDNA (i.e. the gene encoding 16S rRNA as a term used to distinguish the genomic region from the transcribed RNA) [63]. Taking into account these RNA characteristics, we conducted a study of RNA-seq, i.e. the comprehensive quantification of mRNA in a specimen (Figs.  1 and 2). In this paper, we will introduce our study as an example of investigating bacterial interaction at the peri-­implantitis sites [64]. In our study, 12 patients were recruited, and subgingival plaque was collected from peri-­implantitis and periodontitis sites within the same individual, which resulted in 12 peri-implantitis samples and 12 periodontitis samples. Bacterial total RNA was extracted from each sample, and was subjected to a NGS to determine its nucleotide sequences. The obtained data were analyzed to profile the bacterial genes that were transcriptionally active, i.e. transcriptional profile, and the bacterial compo-

sition, based on the amount of mRNA (Fig. 2). We expected that the transcriptional profile would differ between the peri-­implantitis and periodontitis sites in a similar manner that has been reported in previous studies, based on 16S rDNA [56–58]. However, the transcriptional profile was not significantly different between the two groups. We next hypothesized that the bacterial composition would differ, based on RNA expression. To understand the transcriptional activeness of each bacterial species, the amount of mRNA was divided by the amount of 16S rRNA, which was also included in the extracted RNA. This ratio was calculated for each of the bacterial species for which mRNA and 16S rRNA were both detected, i.e. viable taxa with in situ function (VTiF). The VTiF with a ratio > 7 differed between the two groups; the peri-implantitis samples contained Slackia exigua and Eubacterium saphenum, whereas the periodontitis samples contained Porphyromonas sp., Prevotella oralis, Campylobacter concisus, Treponema socranskii, and Veillonella sp. We considered these species as the “active taxa” because

-AATTGTAGCA-AGTTGTAGCG-AGTTGTAGC-

Plaque samples from the disease sites

Extraction of DNA/RNA

High-throughput sequencing

Supercomputerbased data analysis

Taxonomic/ transcriptional profiles, and metabolic pathways

Fig. 2 A workflow of analyzing the microbiota of peri-­ to obtain the data of nucleotide sequences. The data are implantitis and periodontitis using the NGS. Plaque sam- analyzed by using the supercomputer to predict taxonomic ples are collected from the disease sites, and are subjected and transcriptional profiles, and metabolic pathways to extraction of DNA and/or RNA. The NGS is then used

Polymicrobial Peri-Implant Infection

they abundantly transcribed mRNA per expression of 16S rRNA. This finding supported the finding of a previous study showing Eubacterium saphenum as a species potentially responsible for the etiology of peri-implantitis [46]. Slackia exigua was formerly classified as Eubacterium exiguum [65]; this bacterium is an AAGPR.  The high ratio of S. exigua in our study was also consistent with the findings of a previous study showing its high abundance in the peri-­implantitis samples [46]. By focusing on the quantitative relation between mRNA and 16S rRNA, we found that the RNA-based data were effective for understanding transcriptional activeness in a microbiota. We further analyzed the data to determine how the bacterial species interacted with each other within a microbiota. For each pair of two VTiF, the correlation coefficient was calculated from the amount of mRNA.  The pair with a coefficient  ≥  0.3 was then connected by a line, which resulted in the construction of a network

Complicated network Periimplantitis

Bacterial species Positive correlation of mRNA abundance

Fig. 3  A schematic diagram of difference in the network structure of the microbiota between peri-implantitis and periodontitis sites. Bacterial species are indicated by circles, and are connected by a line when they exhibit positive correlation of mRNA abundance. The circles for interacting core taxa are thickened, and connected by a

17

structure. The connection between two species indicated that their mRNA expression levels were similarly high or low, depending on the samples. The predicted network had a different structure between the two groups, and was more complicated in the peri-implantitis samples than in the periodontitis samples (Fig. 3). In the peri-­ implantitis samples, the AAGPRs E. brachy, E. nodatum, and F. alocis were connected with each other and with more than five other species, including the red complex species T. forsythia and T. denticola. Moreover, these species were also connected with Porphyromonas endodontalis, Treponema medium, and Fretibacterium fastidiosum, which formed a partially dense network. P. endodontalis was originally characterized as a bacterium colonizing the root canal [66], whereas its potential role in the etiology of periodontitis was suggested because of the significantly larger amount of P. endodontalis at the periodontitis sites than at the healthy periodontal sites [67]. T. medium was originally isolated

Simple network

Periodontitis

Interacting core taxa Positive correlation with statistical significance

thick line that indicates positive correlation with statistical significance. Our study demonstrated that the network which was predicted based on mRNA abundance was more complicated in the microbiota of peri-implantitis than in the microbiota of periodontitis

T. Shiba and T. Watanabe

18

from the subgingival plaque at the periodontitis site [68], but little is known about its characteristics. F. fastidiosum was isolated from the periodontal pocket and taxonomically classified recently [69, 70]. Although its high prevalence in the periodontitis sites has been reported [71], its association with the etiology of periodontitis and peri-implantitis remains a mystery. One reason for the uncertainty is because of the fastidiousness of F. fastidiosum, which was reported to be culturable only by coculturing it with other oral bacterial species such as F. nucleatum subsp. nucleatum [69]. Furthermore, we defined active species in the network as the “interacting core taxa,” if they were detected in more than 8 of 12 patients and were connected, based on a statistically significant correlation (Fig.  3). Different species were listed as the interacting core taxa between the two groups; the aforementioned E. brachy, F. alocis, P. endodontalis, T. medium, and F. fastidiosum were the interacting core taxa in the peri-implantitis sites. These species may primarily interact with each other in the peri-­implantitis sites, which suggests their contribution to differences in features such as the progression speed and effectiveness of clinical treatment between peri-implantitis and periodontitis.

Future Perspectives of Polymicrobial Peri-Implant Infection As we introduced previously, our investigation of bacterial mRNA expression and network provided a clue in understanding how bacterial species interact with each other in the microbiota. Previous studies have examined the network structure with respect to 16S rDNA abundance [72, 73]. By contrast, our study was significant in comprehensively capturing the bacterial interaction at the transcriptional level. However, our findings were limited because our investigation only involved 12 individuals. Investigators have reported that a small number of samples leads to a low specificity of networks [74], which could be improved by increasing the sample size. Another issue to

consider is that the samples in our study were collected at just one time point. Collecting samples in a time series will enable scientists to analyze robustness, i.e. the ability of networks to withstand the removal of nodes [75], and to analyze it structurally and dynamically [76]. The development of a dysbiotic microbiota at the peri-implant sites will be investigated in the future to clarify its role in the occurrence and progression of peri-implantitis. In addition, our data were based on bacterial RNA expression, called the “metatranscriptome,” as a set of transcripts within a microbiota. Trans-omics has focused on connecting multi-omics data such as the genome, transcriptome, proteome, and metabolome [77]. Analysis of multi-omics data by connecting multi-omic elements will reveal how the microbiota metabolically adapts to the environment [78, 79]. Future studies will further characterize the peri-implant microbiota as a community that differs from the microbiota at periodontitis sites, which will elucidate the association of its dynamic changes with the etiology of polymicrobial peri-implant infection.

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20 38. Tanner A, Maiden MF, Lee K, Shulman LB, Weber HP.  Dental implant infections. Clin Infect Dis. 1997;25(Suppl 2):S213–7. 39. Sumida S, Ishihara K, Kishi M, Okuda K. Transmission of periodontal disease-associated bacteria from teeth to osseointegrated implant regions. Int J Oral Maxillofac Implants. 2002;17(5):696–702. 40. Lindhe J, Meyle J.  Peri-implant diseases: consensus report of the sixth European workshop on periodontology. J Clin Periodontol. 2008;35(8 Suppl):282–5. 41. Van Assche N, Van Essche M, Pauwels M, Teughels W, Quirynen M.  Do periodontopathogens disappear after full-mouth tooth extraction? J Clin Periodontol. 2009;36(12):1043–7. 42. Shibli JA, Melo L, Ferrari DS, Figueiredo LC, Faveri M, Feres M. Composition of supra- and subgingival biofilm of subjects with healthy and diseased implants. Clin Oral Implants Res. 2008;19(10):975–82. 43. Maximo MB, de Mendonca AC, Renata Santos V, Figueiredo LC, Feres M, Duarte PM. Short-term clinical and microbiological evaluations of peri-implant diseases before and after mechanical anti-infective therapies. Clin Oral Implants Res. 2009;20(1):99–108. 44. Persson GR, Samuelsson E, Lindahl C, Renvert S.  Mechanical non-surgical treatment of peri-­ implantitis: a single-blinded randomized longitudinal clinical study. II Microbiological results. J Clin Periodontol. 2010;37(6):563–73. 45. Kumar PS, Mason MR, Brooker MR, O'Brien K.  Pyrosequencing reveals unique microbial signatures associated with healthy and failing dental implants. J Clin Periodontol. 2012;39(5):425–33. 46. Tamura N, Ochi M, Miyakawa H, Nakazawa F.  Analysis of bacterial flora associated with peri-­ implantitis using obligate anaerobic culture technique and 16S rDNA gene sequence. Int J Oral Maxillofac Implants. 2013;28(6):1521–9. 47. da Silva ES, Feres M, Figueiredo LC, Shibli JA, Ramiro FS, Faveri M.  Microbiological diversity of peri-implantitis biofilm by Sanger sequencing. Clin Oral Implants Res. 2014;25(10):1192–9. 48. Uematsu H, Hoshino E. Predominant obligate anaerobes in human periodontal pockets. J Periodontal Res. 1992;27(1):15–9. 49. Uematsu H, Sato N, Hossain MZ, Ikeda T, Hoshino E. Degradation of arginine and other amino acids by butyrate-producing asaccharolytic anaerobic gram-­ positive rods in periodontal pockets. Arch Oral Biol. 2003;48(6):423–9. 50. Tse CS, Williams DM. Inhibition of human endothelial cell proliferation in vitro in response to n-butyrate and propionate. J Periodontal Res. 1992;27(5):506–10. 51. Pollanen MT, Overman DO, Salonen JI. Bacterial metabolites sodium butyrate and propionate inhibit epithelial cell growth in vitro. J Periodontal Res. 1997;32(3):326–34. 52. Jeng JH, Chan CP, Ho YS, Lan WH, Hsieh CC, Chang MC. Effects of butyrate and propionate on the adhesion, growth, cell cycle kinetics, and protein synthesis

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21 72. Zheng H, Xu L, Wang Z, Li L, Zhang J, Zhang Q, et al. Subgingival microbiome in patients with healthy and ailing dental implants. Sci Rep. 2015;5:10948. 73. Kröger A, Hulsmann C, Fickl S, Spinell T, Huttig F, Kaufmann F, et  al. The severity of human peri-­ implantitis lesions correlates with the level of submucosal microbial dysbiosis. J Clin Periodontol. 2018;45(12):1498–509. 74. Berry D, Widder S.  Deciphering microbial inter actions and detecting keystone species with co-­ occurrence networks. Front Microbiol. 2014;5:219. 75. Callaway DS, Newman ME, Strogatz SH, Watts DJ. Network robustness and fragility: percolation on random graphs. Phys Rev Lett. 2000;85(25):5468–71. 76. Tanaka G, Morino K, Aihara K.  Dynamical robustness in complex networks: the crucial role of low-­ degree nodes. Sci Rep. 2012;2:232. 77. Yugi K, Kuroda S.  Metabolism-centric trans-omics. Cell Syst. 2017;4(1):19–20. 78. Yugi K, Ohno S, Krycer JR, James DE, Kuroda S. Rate-oriented trans-omics: integration of multiple omic data on the basis of reaction kinetics. Curr Opin Syst Biol. 2019;15:109–20. 79. Gerosa L, Haverkorn van Rijsewijk BR, Christodoulou D, Kochanowski K, Schmidt TS, Noor E, et  al. Pseudo-transition analysis identifies the key regulators of dynamic metabolic adaptations from steady-­ state data. Cell Syst. 2015;1(4):270–82.

Microbiological Factors of Peri-­Implantitis: Methodologies for Biofilm Analysis Anmar Adnan Kensara, Hanae Saito, Emmanuel F. Mongodin, and Radi Masri

Contents Section 1: Detection Methods

 24

Molecular Methods

 25

Section 2: Functional Methods

 29

Conclusion

 31

References

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A. A. Kensara Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, MD, USA Department of Restorative Dentistry, College of Dentistry, Umm Al Qura University, Makkah, Saudi Arabia Institute for Genome Sciences, School of Medicine, University of Maryland, Baltimore, MD, USA H. Saito Division of Periodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, MD, USA E. F. Mongodin Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA R. Masri (*) Graduate Prosthodontics Program, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, MD, USA e-mail: [email protected]

© Springer Nature Switzerland AG 2020 Y. Ogata (ed.), Risk Factors for Peri-implant Diseases, https://doi.org/10.1007/978-3-030-39185-0_3

Peri-implantitis is a polymicrobial disease that constantly colonize in a biofilm form [1]. Study of this biofilm harboring pathogens in peri-­implant pockets and implant surfaces is crucial in devising effective and suitable prevention and treatment protocols to combat peri-implantitis. Several studies have profiled the composition of microorganisms associated with peri-implantitis, also referred as the microbiota, and compare it with peri-implant mucositis, healthy implants, periodontitis, or natural teeth. These studies, discussed in details in other chapters, have identified a large number of potential pathogens associated with peri-implantitis using different detection methods. Microbiological detection methods in peri-implantitis can be divided into conventual methods and molecular methods. This chapter presents an overview of the most common microbiological methods used to study pathogens associated with peri-implantitis including advantages and drawbacks of each method. The first section (Detection Methods) focuses on the most 23

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A. A. Kensara et al.

prevalent methods used to study the microbiota of peri-implantitis. The second section (Functional Methods) covers the most common techniques that could be used to complement the detection methods to study the microbiome, including the microbiota and its assosiated  environment. This information might help in analysis of the results of different pathogen detection studies and help in choosing the appropriate method that answer the proposed research question in future studies.

alone or as a complementary method with cultures to detect the potential associated pathogens [2, 3]. In fact, the first studies that profiled bacteria in peri-implant failure were based solely on microscopy [4, 7]. Most common microscopy techniques that have been utilized in peri-­implantitis are darkfield, phase-contrast, immunofluorescence, transmission electron microscopy (TMS) and scanning electron microscopy (SEM) (Fig.  1). In general, microscopy allows the detection of a variety of organisms and their morphologies in peri-implantitis samples. Microscopic evaluation is also a Section 1: Detection Methods first step to determine the appropriate isolation media and culture methods for future experiments Conventional Methods [6]. However, the visualization of samples using microscopy is less sensitive and less specific than Early studies of peri-implantitis were based on culture methods especially when samples contain microscopy and/or culture dependent methods small numbers of organisms [6]. Specific volumes [2–5]. These studies were instrumental in char- of viable organisms per milliliter are required acterizing the potential pathogens related to peri-­ in order to be visualized under the microscope, implantitis. However, results of these methods which is, sometimes, very difficult to obtain from suffer from significant limitations, as we discuss the peri-implant sulcus [6, 8]. Moreover, certain below. microscopy techniques require fastidious sample preparations that might wash some of potential pathogens. And more critically, laboratory techniMicroscopy cians must have experience and receive sufficient training in order to increase the reliability and The microscopic detection of pathogens in quality of the results [6]. Currently, microscopy clinical samples is one of the early and rapid techniques are used as a confirmative or as an methods in diagnostic microbiology [6]. Early illustrative tool in a complement with other detecperi-­implantitis studies have utilized microscopy tion methods in peri-implantitis studies [9].

Fig. 1  Images of biofilm attached to failed implant taken on an scanning electron microscope (SEM)

Microbiological Factors of Peri-Implantitis: Methodologies for Biofilm Analysis

Culture-Dependent Methods Culture-dependent methods refer to any methods that require growing the collected microorganisms in a clinical sample in an agar-containing solid culture media under specific oxygenated condition. Following pathogen growth, biochemical assays, microscopic examination and/or antibodies are further used to profile and specify pathogens. Culture-dependent studies provided valuable foundational knowledge of the microbiota associated with peri-implant health and disease. In fact, cultivation has been the gold standard for bacteria and fungi identification in periimplantitis. However, there are many challenges conducting such studies since they are technique sensitive, require time and require pathogens to be viable [10, 11]. Besides, some oral microbes cannot be cultivated or require specific media or specific conditions for cultivation [10]. Such challenges limit the use of culture dependent methods for pathogen profiling since the oral microbiome is widely diverse and complex, and attempting to identify the entire microbiota using the culture technique is unreasonable. This method is commonly used to  complement other methods to detect the viable and cultivable bacteria and fungi. It is also still valid as a basic detection method for studies with limited resources [12, 13].

Molecular Methods The  oral  microbiome is considered as the second most complex microbiome in the human body and the majority of microorganisms in it are not cultivable [14, 15]. Thus, culture-independent methods are essential in identifying putative pathogens in peri-implantitis. Molecular approaches refer to methods that detect microorganisms based on their nucleic acids [16]. The microbiology of peri-implantitis became more discernible with the introduction of molecular approaches since they eliminated the need for culture. Currently, due to the advances in molecular methods, only few studies utilize the conventional methods to detect pathogens associated with peri-­implantitis. Molecular methods can be

25

further divided into close-ended and open-ended methods, also known as genomic or metagenomic methods.

Close-Ended Methods Close-ended methods refer to any molecular method that require a prior knowledge of the microbiota associated with collected samples. Although they are considered as culture-­ independent methods, culturing may be necessary to select the suitable probes or primers. Most of close-ended studies in peri-implantitis were polymerase chain reaction (PCR)-based or nucleic acid hybridization-based methods.

PCR PCR was first introduced as a molecular approach in the mid 80’s by K.  Mullis [17]. Hereinafter, it has been used as a cost-effective molecular method for the specific detection and quantification of nucleic acids from bacteria, archaea, viruses and fungi in peri-implantitis [18–22]. The rational of PCR as a pathogen detection method is based on targeting and synthesizing targeted pathogens-specific nucleic acid segments by enzymatic reaction. The main component of the PCR alongside the targeted segment are DNA polymerase enzyme, deoxynucleotides triphosphate (dNTPs), the designed primer and specific ions [23, 24]. Through a number of PCR cycles, DNA double strands will be separated by denaturation and the single strand primer will be annealed to the targeted segment, then by the polymerase and dNTP the elongation of the targeted sequence will be initiated. Previous peri-­implantitis studies have used several variations of the PCR protocol as a pathogen detection method as follows: Real Time-PCR (Quantitative PCR—qPCR) qPCR is the most common PCR protocol used as pathogen detection method in peri-implantitis studies [18–20, 25, 26]. In this protocol, the variation from the conventional PCR is to measure the amount of specific pathogens’ DNA amplicons in a sample by measuring the fluorescence level after each PCR cycle [27]. Also it can be

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semiquantitative when a comparison control is used (e.g. disease vs. healthy) instead of using the standard as a reference. [23, 27–29] Results from semi-quantitative PCR are prone to errors due to the failure of the fluorescent signal to distinguish between specific and nonspecific PCR products. Thus, it is thought that this technique is unable to produce absolute quantitative values [23, 24]. Nested PCR The main modification in nested PCR is to use two separate PCR reactions by using a universal primer in the first reaction to cover the target sequence in addition to extra sequences at both sides, and then to use a specific primer on the product of the first PCR in the second reaction, to bind to the target amplified sequence [23, 24]. Thus, this protocol is thought to overcome the qPCR nonspecific binding issue [23, 24] Nested PCR is useful for the identification of pathogen DNA present at very low levels. Peri-implantitis studies aimed to increase the specificity of PCR utilize nested PCR [30–32]. However, numerous false negative results have been reported by using this method in addition, the possibility of contaminating other reaction tubes is very high [23, 24]. Multiplex PCR Another variation of conventional PCR where multiple primers are used for different targeted pathogen in a single PCR reaction [23]. The rational of using this approach is to identify a variety of targeted pathogens in one reaction tube [23, 24]. Several peri-implantitis studies used multiplex PCR as a detection method [12, 33]. However, the potential of interference between used primers during amplification is the main limitation of this technique [24]. Reverse Transcriptase PCR (RT-PCR) In RT-PCR, RNA is extracted and converted to cDNA using reverse transcriptase enzyme in preparation for a PCR run [23]. This technique is sensitive and uses only transcripts (mRNA) that relates specific genes, point mutations, deletions, and insertions. RT-PCT is very helpful when attempting to identify RNA viruses and can be can be quantitative as described above for qPCR [23, 24].

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Collectively, besides avoiding the culture step, PCR approaches are rapid and have great sensitivity in detecting bacteria, fungi, and virus detection using a very small sample. However, the possibility of contamination during the amplification process, in addition to the introduction of bias as a result of primer selection, can limit the use of this technique in pathogen detection [11, 34, 35].

Hybridization Approaches The main principle of all hybridization molecular methods is the annealing of targeted single strand DNA with a complementary DNA probe that allow for pathogen detection. The most common methods used in peri-implantitis research are “checkerboard” DNA-DNA hybridization and fluorescent in-situ hybridizations (FISH). The Checkerboard DNA-DNA Hybridization The checkerboard DNA-DNA hybridization was developed by Socransky and coworkers in 1994 [36]. Thereafter, it became a widespread detection method in peri-implantitis thanks to its probe flexibility and simultaneous multiple species detection in multiple samples [10, 11, 37–39]. The rationale of this approach is to use a pre-determine whole genome probe to hybridize the pathogen’s DNA against the probe [36, 40, 41]. This technique is more sensitive than culture methods for bacterial detection [40]. However, although this technique is considered as a culture-­independent method, culture may be needed to design the genomic probes allowing the detection of the targeted pathogens [35]. Furthermore, the major drawback of this technique is a low ­specificity for pathogen detection as a result of cross reactivity of the genomic probe [41]. Fluorescent In-situ Hybridizations (FISH) FISH is a rapid technique that allows to detect, count, localize and visualize targeted pathogens [42]. It is a good technique for the identification of bacteria, archaea, yeasts and protozoa [42]. The principle is to use fluorescent-labeled DNA probes (peptide nucleic acid PNA) that

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16S rRNA Gene Sequencing  The 16S rRNA gene is a microbial biomarker, about 1500 base pair in length, that is present in all prokaryotes. It is highly conserved, allowing for the design of universal PCR primers but also contains  9 hypervariable regions, allowing to distinguish between strain or species. 16S rRNA gene sequencing aims to sequence all the bacterial taxa present in a sample, regardless of their viability and cultivability, thus greatly improving identification of microorganisms associated with peri-implantitis [10, 11, 35]. Full sequences of oral bacteria have become increasingly available in public databases facilitating microbiome studies of the oral environment [10]. Although, 16S rRNA gene sequencing metagenomics conOpen-Ended Methods (Microbiome sidered the contemporary gold standard to study Strategies) bacterial composition, this approach is likely to fail in distinguishing between closely related Since close-ended methods depend on species, which might be critical to discriminate prior knowledge of pathogens for detection, they between health and disease condition [11, 35]. may be not representative of the total microor- Furthermore, DNA extraction techniques are not ganisms associated with peri-implantitis. Open-­ universal, thus the quality of data varies between ended methods aim to fill this gap as they allow studies depending on the quality of extracted to profile the microbial community in the peri-­ DNA.  Several studies profiled the  microbiome implant pocket despite its cultivability and the composition associated with peri-implantitis prior knowledge of the microbiota associated to by using 16S rRNA gene sequencing  [45–54]. peri-implant pockets. Metagenomics is the pro- These studies surveyed the bacterial composition cess of extraction and sequencing the complete and abundance without any information about set of microbiota genomes in a defined environ- the functional roles of those detected bacteria in ment (microbiome) [43]. The resulting  metage- peri-implantitis. nome is then assembled or mapped to a reference database followed by annotation using bioin- Internal Transcribed Spacer formatics [43]. Microbiome studies can be con- (ITS) Sequencing  ducted using  shotgun sequencing, also referred Fungi are eukaryotic organisms; thus, the previto as de-novo metagenomics, or targeted regions ous described conserved gene (16S rRNA gene) to catalogue the genes that are present [44]. This is absent in these organisms.  The internal transection covers the targeted microbiome stud- scribed spacer (ITS) is a non-functional DNA ies  only as they are used as detection methods segment that is located between 18S, 5.8S, and and the next section covers the functional meth- 28S rRNA genes. This segment is conserved and ods including shotgun metagenomics. proposed as a single marker barcode that allow to design universal primers to target this segment Targeted Microbiome Strategies  and sequence it to profile fungi [55]. Several Targeted  microbiome  sequencing inspects only studies identified fungi in peri-implantitis, yet a specific gene to reveal organism diversity in a none used ITS sequencing to profile and discover microbiome. The most common 2 examples are novel fungi in peri-implantitis. However, the fail16S rRNA gene for prokaryotic detection and ure of the universal primer to identify some fungi ITS for fungal detection. species is possible [55]. are designed to be a complement to the targeted pathogen DNA (e.g. 16S rRNA gene) to detect absence or presence of that pathogen in tissue or clinical samples by the aid of fluorescence microscopy [23]. One study used FISH to target a specific bacteria species in peri-implantitis [9]. This technique can identify and quantify viable pathogens in a clinical sample, e.g. dental biofilm. However, beside the limitation of microscopy methods as described previously, The PNA probe is expensive and not useful for all bacteria. Hence, FISH is seldom used as a pathogen detection method and does not seem to be suitable for all kinds of samples [42].

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 echnologies Used for 16S rRNA Gene T Surveys in Peri-Implantitis Since all microbiome  studies in peri-implantitis were 16S rRNA gene based this part covers the most common technologies that have been utilized for sequencing: Sanger Sequencing (Chain Terminating Sequencing) Sanger sequencing is a first-generation DNA sequencing method developed by Frederick Sanger in 1977 [56]. The main principle of this method is chain termination sequencing. Accordingly, single strand DNA templates ­synthesized by the incorporation of nucleotides (dNTPs) using DNA polymerase in the presence of complement primer, to initiate replication. The DNA template is divided into four separate sequencing reactions, each reaction contains only one of the four terminators, dideoxynucleotides ddNTPs (ddATP, ddGTP, ddCTP, or ddTTP), that lack of a 3′-OH group. When ddNTPs is incorporated, then the synthesis of the new chain is terminated. After rounds of template DNA extension, different lengths of template DNA are formed based on when the terminators are added. These DNA fragments are separated by size using capillary electrophoresis with each of the Fig. 2  Example of platforms used for Sanger sequencing, on the left Applied Biosystems™ 3130 × l DNA Analyzers, on the right the following generation Applied Biosystems™ 3730 × l DNA Analyzers by Applied Biosystems, Foster City, CA, USA

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four reactions in one of four separated lanes. The DNA bands can then be visualized by ultraviolet (UV) light or autoradiography, or by reading the radioactivity if the ddNTPs used in reaction were radioactively or fluorescently labeled, and mapped in the sequence (Fig.  2) [23, 57]. The read length of sanger sequencing is relatively short segment (800–1000 base reads) [23]. Early sequencing studies of peri-implantitis have used sanger sequencing of the 16S rRNA gene, which resulted in a very high accurate output [45–48]. Nevertheless, this method has a low-depth of coverage, i.e. low throughput, so it is not suitable for detecting very rare species [11, 23, 58]. Moreover, it is very demanding and less cost-­ effective when compared to other methods [10]. Pyrosequencing 454 pyrosequencing technology (Fig.  3) uses the principle of sequencing by synthesis. Thus, a nucleotide is added to the reaction well and it is incorporated to the single strand DNA template only if it is complementing the next nucleotide. Once incorporated, light signal will be emitted from the enzymatic reaction and captured by the pyrosequencing machine then plot light products on a graph called pyrogram. The cycle will repeat until the complete DNA template is sequenced

Microbiological Factors of Peri-Implantitis: Methodologies for Biofilm Analysis

Fig. 3  454 pyrosequencing (GS Flex +) Platform by Roche Applied Science, Basel, Switzerland

[23, 57, 58]. Several studies have utilized pyrosequencing as a pathogen detection method in peri-implantitis [49–51]. Compared to Sanger method, this technology is faster, provides higher throughput, and it is more cost-effective [34, 58]. However, the length of individual DNA reads is shorter than Sanger method (approximately 300– 500 nucleotides) [23]. It also has higher error rate that might be interpreted incorrectly. Illumina Illumina is a next generation sequencing (NGS) method that provides a very high throughput and is more cost effective than the previously described techniques. NGS improved the accuracy and speed at lower cost leading to revolutions in sequencing research. Illumina generates billions of short reads in a relatively short time. The main principle of this technology is the same as pyrosequencing, sequencing by synthesis, but it is applied differently. After DNA is fragmented and adapters and barcodes are ligated, DNA is incorporated by the adapters to the complementary oligonucleotides in the flowcell. DNA is replicated in clusters form by bridge

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amplification. Sequencing is started by synthesis after modified nuclides (modified with 3′ reversal blockers and labeled with fluorescence) and a primer added to the reaction. The primer will be hybridized to DNA and polymerase will extend the primer by adding the modified nucleotides. Each of the clusters will then emit fluorescent signal based on the incorporated nucleotide that is detected by a coupled-charge device (CCD) camera and is translated into a nucleotide sequence using software [23, 59]. This technology can perform massive parallel sequencing as thousands of sequencing reaction can be performed in one assay run [23]. However, Because this technology provides short reads, it is difficult to assemble these reads and impossible to generate a finished sequence in highly repetitive areas. There are multiple platforms available in the market utilize Illumina technology, e.g. HiSeq™ and MiSeq™ (Fig. 4). The differences between these platforms are the read length, the throughput (number of DNA sequenced in one run) and the sequencing time. Although this technology is highly accurate, there is still a 1.5% margin of error. This technology has been used recently to profile bacteria associated with peri-implantitis [52–54].

Section 2: Functional Methods  The previous section focused on methods that profiled pathogens associated with peri-­implantitis. Microbiota referred to the gathering of micro-­ organisms in a defined environment. This term is different than the microbiome, referred to the entire habitat, including the micro-organisms and the surrounding environmental conditions [43]. Currently, in addition to targeted microbiome studies that were discussed in the previous section, most common microbiome studies  are: shotgun metagenomics, meta-transcriptomics, metaproteomics and metabolomics, or what is referred as omics’ approaches. Currently, no study has utilized all omics’ technology to study the microbiome of peri-implantitis. Only 16S rRNA gene sequencing were utilized.

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a

b

Fig. 4  Example of platforms use Illumina Technology, (a) HiSeq™ 4000, (b) MiSeq™ by Illumina Inc., San Diego, CA, USA (Courtesy of Illumina Inc.)

Shotgun Metagenomics

Metaproteomics

Shotgun metagenomics sequencing,  unlike targeted 16S rRNA gene sequencing, inspects the entire genome of all the organisms present in the sample including archaea, bacteria, viruses and fungi. Since the entire metagenome is collected, this method is able to predict the function of a profiled pathogen. The main challenge of conducting shotgun sequencing are the cost and the availability of resources.

Metagenomics and metatranscriptomics do not necessarily indicate protein expression [60]. Thus, In order to provide accurate functional information of pathogens, it is necessary to complement the results of nucleic acids sequencing with proteins assessment. Metaproteomics is a sequence-independent approach aims to characterize the complement of proteins secreted by microbiota in a clinical sample at specific time [43, 61]. Peptide profiling often achieved by liquid-chromatographybased separation coupled to mass spectrometry (LC-MS/MS) [43, 61]. Metaproteomic allows to identify the functional roles and interactions of microbiota in a complex microbiome [61]. However, main challenges of conducting metaproteomic study are the availability of comprehensive databases, the lack of functional and taxonomic annotation levels of proteomic sample, complexity of the microbiota and their secreted protein levels in addition to patients variability [62]. These limitations might complicate data analysis.

Metatranscriptomics Metatranscriptome  sequencing  is another sequence-dependent approach that provides information on the regulation and expression of genes in the microbiome [43]. Metatranscriptomics survey mRNAs by high-throughput sequencing of the corresponding cDNA in specific conditions [43]. Unlike metagenomics that capture DNA regardless of the viability of microbiome composition, mRNA sequencing investigate the active members at selected time. Metatranscriptomics could be de-novo, survey all genes, or targeted to a specific gene. However, this approach has more challenges than metagenomic. Unlike DNA, RNA is unstable. Thus, any environmental changes during sample collection, storage or lab process will reflect on the quality of RNA yield. Therefore, careful sample handling and processing is critical. Furthermore, any contamination will swamp the sequencing and bioinformatics analysis.

Metabolomics Metabolomic is another sequence-­ independent approach aims to determine the metabolite profiles, e.g. sugars, lipids, amino acids, fatty acids, in a microbiome (Metabolome) at any given time point [43]. The metabolome is the direct indicator

Microbiological Factors of Peri-Implantitis: Methodologies for Biofilm Analysis

of the dysbiosis in a microbiome [63]. Hence, it is necessary to complement the genomic, transcriptomic and proteomic results with metabolomic in order to complete the picture of the disease. Metametabolomics can be targeted, detect specific metabolites, or non-­ targeted, detect all known and unknown metabolites [64]. Similar to metaproteomic same platforms could be used to characterize the metabolome. However, Metametabolomics analysis relies on the available databases of stored profiles of known compounds, which are inherently limited.

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Conclusion Conventional methods provided the earliest evidence of morphotypes of microorganisms associated with peri-implantitis. Consequently, the development of close-ended molecular methods allows to identify more pathogens involved with peri-implantitis that could not be detected by conventional methods. 16S rRNA gene sequencing opened the window for identifying novel pathogens since it allows profiling prokaryotes in a complex microbiome (Table  1). Currently,

Table 1  Summary of the most common methods to profiles the microbiota of peri-implantitis Detection Method Microscopy

Culture-­ dependent

PCR-based method

Checkboard DNA-DNA hybridization

16S rRNA gene sequencing

ITS sequencing

Advantages   – Rapid technique   – Illustrative

Disadvantages  – Sample preparation  – Less sensitive and less specific that other methods  – Specific volumes of viable organisms per milliliter are required  – Need a well-trained lab worker  –  Technique sensitive – Basic detection method – Only viable bacteria can  –  Time consuming  – Pathogens must be grow viable   – Underestimated  – Sensitive and specific  – Unable to detect novel pathogens  – Quantitative (if  – Possibility of real-time) contamination during  – Very small amount of amplification sample needed  – Unable to detect novel  – Sensitive pathogens   – Quantitative  – Low specificity as a   – Flexible probe result of cross reactivity  – Simultaneous multiple of the genomic probe species detection  – Fail to distinguish  – Survey all bacteria between closely related regardless of species cultivability and  – DNA extraction viability techniques are not universal  – Provide no information about the functional role of detected bacteria  – Survey most of fungi  – Same as 16S rRNA gene sequencing regardless of  – Not been used yet in cultivability and Peri-implantitis viability

Application  – Identify bacteria, fungi, virus and their morphologies  – Diagnostic method  – Confirmative with other methods

 – Good as a pilot study for undetermined bacteria and fungi  – Confirmative with other methods  – Detecting a limited range of bacteria, fungi, and virus

 – Detecting an abundant number of predetermined bacteria in an abundant number of samples simultaneously  – The contemporary gold standard method to identify prokaryotic in a microbiome

 – Identify Fungi in a microbiome

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by developing the high-throughput next generation sequencing, the focus has shifted to studying other aspects of pathogeneses such as the impact of genetic and environmental factors, also known as microbiome. Currently, periimplantitis microbiome is still not completely understood. The knowledge of microbiome function in peri-­ implantitis necessitates welldesigned metagenomic, metatranscriptomics, metaproteomic and metabolomics studies.

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34 54. Sanz-Martin I, Doolittle-Hall J, Teles RP, Patel M, Belibasakis GN, Hämmerle CHF, et  al. Exploring the microbiome of healthy and diseased peri-implant sites using Illumina sequencing. J Clin Periodontol. 2017;44(12):1274–84. 55. Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, et  al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci U S A. 2012;109(16):6241–6. 56. Sanger F, Nicklen S, Coulson A.  DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977;74(12):5463–7. 57. Zhou X. Techniques for oral microbiology. In: Atlas of oral microbiology. Chapter 2. UK: Elsevier Ltd. 2015. p. 15–40. 58. Siqueira JF, Fouad AF, Rôças IN. Pyrosequencing as a tool for better understanding of human microbiomes. J Oral Microbiol. 2012;4. https://www.tandfonline. com/action/journalInformation?journalCode=zjom20 59. Kchouk M, Gibrat JF, Elloumi M.  Generations of sequencing technologies: from first to next generation. Biol Med. 2017;09(03).

A. A. Kensara et al. 60. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13(4):227–32. 61. Kleiner M. Metaproteomics: much more than measuring gene expression in microbial communities. mSystems. 2019;4(3):1–6. 62. Rechenberger J, Samaras P, Jarzab A, Behr J, Frejno M, Djukovic A, et al. Challenges in clinical metaproteomics highlighted by the analysis of acute leukemia patients with gut colonization by multidrug-resistant enterobacteriaceae. Proteomes. 2019;7(1):E2. 63. Aguiar-pulido V, Huang W, Suarez-ulloa V, Cickovski T, Mathee K, Narasimhan G.  Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis. Evol Bioinforma. 2016;12(S1): 5–16. 64. Lee HJ, Kremer DM, Sajjakulnukit P, Zhang L, Lyssiotis CA.  A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics. Metabolomics. 2019;15(7):1–25.

Microbiological Factors of Peri-­ Implantitis: Characteristics and Significance Hanae Saito, Anmar Adnan Kensara, and Radi Masri

Contents Introduction

 35

Healthy Implants vs Healthy Natural Teeth

 37

Peri-Implant Mucositis vs Gingivitis

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Periodontitis vs Peri-Implantitis

 38

Disease Progression in Dental Implants

 40

Microbiological Factors Related to Dental Implant Structure

 41

Summary

 42

References

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H. Saito (*) Division of Periodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, MD, USA e-mail: [email protected] A. A. Kensara Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, MD, USA Department of Restorative Dentistry, College of Dentistry, Umm Al Qura University, Makkah, Saudi Arabia Institute for Genome Sciences, School of Medicine, University of Maryland, Baltimore, MD, USA R. Masri Graduate Prosthodontics Program, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, MD, USA

© Springer Nature Switzerland AG 2020 Y. Ogata (ed.), Risk Factors for Peri-implant Diseases, https://doi.org/10.1007/978-3-030-39185-0_4

Introduction In Chap. 3, we discussed the various microbiological approaches to study peri-implant disease. Here, we discuss the characteristics and significance of microbiological factors of peri-­ implantitis. The incidence of peri-implantitis has been steadily increasing, with mean frequency of approximately  22% [1]. Peri-implantitis is characterized by an inflammatory process around implants that includes both soft tissue inflammation and progressive bone loss [2]. The importance of dental biofilms in the etiology of peri-implantitis has been extensively studied [3]. Studies have investigated whether dental biofilm profiles of implants are similar to periodontitis or if they represent another type of bacterial colonization  and  showed important differences [4, 5]. 35

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Although peri-implant disease has similarities to periodontal disease, they seem to be distinct entities [6]. Likewise, the differences in bacterial profiles of healthy and diseased dental implants have also been studied [7]. Although oral microbiome is the second complex microbiome in the body [8], it has been studied widely since saliva and biofilms are easily harvested from oral surfaces. Its analysis is important for our understanding of its role in the development and pathogenesis of infectious oral diseases. Socransky et al., identified six bacterial complexes that commonly occur together, and color-coded them as blue, green, yellow, purple, orange, and red. The blue (e.g. Actinomyces viscosus), yellow (e.g. Streptococcus mitis and Streptococcus spp.), green (e.g.  Eikenella corrodens, Capnocytophaga sputigena, Capnocytophaga ochracea, Campylobacter concisus, and Aggregatibacter actinomycetemcomitans), and purple (e.g. Veillonella parvula and Actinomyces odontolyticus) complexes are compatible with periodontal health, whereas the orange and red complexes are thought to be associated with periodontal disease and include members of the yellow, green, and purple complexes. The orange complex bacteria generally appear after the early colonizers and include many putative periodontal pathogens, such as Prevotella intermedia,  Fusobacterium nucleatum. The red complex (Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola) was considered the climax community and is on the list of Table 1  The list of orange and red complex bacteria Orange complex Campylobacter gracilis Campylobacter rectus Campylobacter showae Eubacterium nodatum Fusobacterium nucleatum Fusobacterium periodonticum Peptostreptococcus micros Prevotella intermedia Prevotella nigrescens Streptococcus constellatus

Red complex Bacteroides forsythus Porphyromonas gingivalis Treponema denticola

putative periodontal pathogens (Table 1) [9]. The periodontal microbiota have been studied in various sites and conditions, such as around teeth or dental implants, and significant differences in its constitution have been demonstrated between health and disease states [10–14]. Although culture-independent methods developed in the last two decades, such as checkerboard DNA–DNA hybridization or 16S ribosomal RNA (rRNA) gene sequencing (see Chap. 3), have provided considerable additional knowledge on the nature of the microbiota associated with oral health and disease, studies based on 16S rRNA gene sequencing clone libraries have shown that 40–60% of the oral microbiota is composed of as-yet-uncultivated bacteria [10]. The recently released expanded Human Oral Microbiome Database (eHOMD) lists approximately 230 oral phylotypes that have not been cultivated [15]. In the oral cavity, growth conditions and nutrients are encountered in the development of different communities. Indeed, some bacteria are much more prevalent in some environments of the oral cavity than in others because they find ideal conditions to survive [16, 17]. microbiome differences also occur between different individuals, even in health [10]. DNA checkerboard studies were used to evaluate the microbiota in health and periodontal/peri-implant diseases and the impact of different periodontal therapies on these groups of bacteria [18, 19]. Red and orange microbial complexes were predominant in disease, and successful treatments were associated with the reduction of these complexes and the increase of host-compatible microorganisms. Other species not considered members of the normal oral microbiota, including Staphylococci, Acinetobacter, Pseudomonas, and enteric bacteria, were detected in relative high frequency in periodontitis/peri-implantitis sites [20–22]. Genetic methods incorporating 16S rRNA sequencing led to reorganization of bacterial taxonomy, and amplification of microbial DNA by polymerase chain reaction (PCR) was introduced to study selected oral species. In particular, PCR data suggested that Epstein–Barr virus (EPV) and Cytomegalovirus (CMV) were

Microbiological Factors of Peri-Implantitis: Characteristics and Significance

37

highly prevalent in aggressive forms of periodontitis, although the mechanisms by which these viruses may contribute to disease remain unknown [23, 24]. To study microbiome, use of 16S rRNA probes in a “reverse capture checkerboard” format could detect cultured and uncultivated species ­recognized from cloning and sequencing analyses. This approach was miniaturized to a microarray, which could assay around 300 oral species [25]. These assays showed that many new taxa were associated with periodontal and peri-implant diseases, suggesting new potential pathogens [25, 26]. Other approaches combined next-generation sequencing methods with in silico hybridization using specific probe sequences (ProbeSeq), allowing identification of an extended number of oral taxa [27]. In this chapter, we will discuss microbiological characteristics in the following context: 1. Healthy implants vs healthy natural teeth, 2. Periimplant mucositis vs gingivitis, 3. Periodontitis vs peri-implantitis, as well as the progression of the disease from healthy implants to periimplant mucositis and peri-implantitis.

nii, and S. oralis can also be considered as bacteria associated to periodontal health [28]. Certain bacterial species, including S. sanguinis, Veillonella parvula, and C. ochracea are proposed to be protective or beneficial to the host. They are typically found in high numbers at  inactive periodontal sites that do not demonstrate attachment loss but in low numbers at sites with  active periodontal destruction [9, 22]. These species probably function to prevent the colonization or proliferation of pathogenic microorganisms. 16S rRNA pyrosequencing revealed that healthy implants demonstrated the higher levels of Gram-negative anaerobes and lower abundance of Gram-positive aerobes when ­ ­compared to healthy teeth. Healthy implants demonstrated higher levels of Prevotella, Treponema, Leptotrichia, S. mutans, Butyrivibrio, Catonella, Propionibacter and Lactococcus and lower levels of Arthrobacter, Synergistes, Corynebacterium, Neisseria, Veillonella, Dialister, Granulicatella, Actinomyces, Fusobacterium and non-mutans Streptococcus [13]. Thus, significant differences exist in the microbiome composition of healthy implants compared to healthy natural teeth.

 ealthy Implants vs Healthy Natural H Teeth

Peri-Implant Mucositis vs Gingivitis

The recovery of microorganisms from periodontally healthy sites is lower as compared with diseased sites [28]. The bacteria associated with periodontal health are primarily Gram-­ positive facultative species and members of the genera Streptococcus and Actinomyces (e.g., S. sanguinis, S. mitis, A. oris, A. israelii, A. gerencseriae, A. viscosus, A. naeslundii). Small proportions of Gram-negative species are also found, most frequently P. intermedia, F. nucleatum, F. nucleatum spp. polymorphum, F. periodonticum, Capnocytophaga spp., C. gingivalis, C. ochracea, and C. sputigena, Neisseria spp., and Veillonella spp. Early  microscopic analyses indicate that a few spirochetes and motile rods may also be present [29, 30]. Based on checkerboard DNA– DNA hybridization, Eubacterium saburreum, Propionibacterium acnes, S. anginosus, S. gordo-

The development of gingivitis has been extensively studied in a model referred to as experimental gingivitis [31, 32]. The initial microbiota of experimental gingivitis consists of Gram-­positive rods, Gram-positive cocci, and Gram-­ negative cocci. The transition to gingivitis is initiated by inflammatory changes accompanied by the appearance of Gram-negative rods and filaments then followed by spirochete and motile microorganisms [33]. The analysis using modified DNA–DNA checkerboard hybridization of the subgingival/ submucosal microbial samples from experimental gingivitis and peri-implant mucositis revealed relatively small differences over time and virtually no differences between gingivitis and peri-implant mucositis sites; No differences in the detection frequency were found for putative periodontal pathogens of the red complex (e.g. Porphyromonas gingivalis,

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Tannerella forsythia and Treponema denticola) between gingivitis and peri-implant mucositis sites. Salvi et  al. demonstrated that P. gingivalis was detected only occasionally after 3 weeks of refraining from oral hygiene. T. forsythia and T. denticola were isolated in a small proportion of subjects both at gingivitis and peri-implant mucositis sites without any trends for increasing detection frequency during the period of refraining from oral hygiene. Among the orange complex species, only Eikenella corrodens showed a statistically significant increase irrespective of gingivitis or periimplant mucositis [34].

Periodontitis vs Peri-Implantitis Microscopic examination of plaque from sites with chronic periodontitis has consistently revealed elevated proportions of spirochetes [35, 36]. High percentages of anaerobic (90%) Gram-­ negative (75%) bacterial species  were cultivated from the plaque microorganisms from the site of  chronic periodontitis [37, 38]. Socransky’s landmark study in 1998 with whole genomic probes and the DNA–DNA checkerboard hybridization technique identified three major pathogenic bacteria strongly associated with severe periodontitis [39]. These “red complex” bacteria, Porphyromonas gingivalis, Tannerella forsythia and Treponema denticola, were accepted as strong etiological agents of periodontal disease. Sanger sequencing, pyrosequencing and next generation sequencing  for 16S rRNA gene demonstrated more complexity. Many works have focused on the precise characterization of microbiome composition associated with periodontitis and peri-implantitis using 16S rRNA sequencing bacterial identification [10, 12, 14, 23, 40, 41]. New information came out of these studies: first, the well-known microorganisms of the red complex could be found in sites and subjects in the absence of disease; second, new potential periopathogens emerged, some of which were not necessarily Gram-negative (Filifactor alocis, Peptostreptococcus) (Figs.  1 and 2).

H. Saito et al.

Fig. 1 Clinical photograph of the site affected by peri-implantitis

The introduction of pyrosequencing techniques for microbial community analysis has provided additional sensitivity for detecting periodontal pathogens, although the broad picture has not changed. Unculturable organisms such as Synergistetes appear to correlate with periodontal disease, whereas high proportions of Actinomyces, Rothia, and Streptococcus are correlated with health [40, 42]. When periodontally active sites and inactive sites were compared, concentrations of C. rectus, P. gingivalis, P. intermedia, F. nucleatum, and T. forsythia were found to be elevated in the periodontally  active sites [43]. Furthermore, detectable levels of P. gingivalis, P. intermedia, T. forsythia, C. rectus, and A. actinomycetemcomitans are associated with disease progression, and periodontal therapy aiming their elimination may improve clinical response [42–44].  The findings of these studies showed that Archaea, a group of single-celled microorganisms, were restricted to a small number of methanogen species, whereas more than 700 oral bacterial species belonging to different phyla (Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Spirochaetes, Synergistetes and Tenericutes and the uncultured divisions GN02, SR1 and TM7) were observed [45]. Peri-implantitis represented a heterogeneous infection of more complexity predominantly composed of non-cultivable Gram-negative species compared with periodontitis [46]. Studies using conventional techniques have compared periodontopathic microorganisms in peri-­

Microbiological Factors of Peri-Implantitis: Characteristics and Significance Fig. 2  Anaerobic (a and c) and aerobic (b and d) bacteria from peri-­ implant sulcus from the implant site affected by peri-implantitis (a and b) and healthy implant from the same individual (c and d)

a

b

c

d

implantitis and periodontitis sites and confirmed overall similarities of the microbiota associated with periodontitis and peri-implantitis [4, 47, 48]. However, some variation in microorganism frequencies were reported. P. gingivalis was similar in periodontitis and peri-implantitis, yet, P. intermedia, C. rectus, and T. forsythia were more frequent in periodontitis than periimplantitis. Enteric rods were recovered more frequently and at higher levels in peri-implantitis compared with periodontitis, and Pseudomonas aeruginosa, S. anginosus, and Candida albicans were frequent in peri-implantitis [47, 48]. Periimplantitis sites showed more diversity than periodontitis and were characterized by higher proportions of Peptococcus, Mycoplasma, Campylobacter, Butyrivibrio, Streptococcus mutans, Eubacterium, Porphyromonas, Achromobacter xylosoxidans, TM7 [G-5], HOT437, Actinomyces massiliensis, Porphyromonas, HOT-395, Prevotella nigrescens, and Prevotella oris [5, 46]. The levels of Treponema,

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Campylobacter and Eubacterium were significantly higher in both peri-implant health and disease than in periodontitis [10]. Differences in the composition of the subgingival microbiota between periodontal and peri-implant diseases are discrete, with increased prevalence of staphylococci, Gram-negative bacilli, and Candida spp. in peri-implantitis [21, 26, 49]. Studies have documented an association between chronic periodontitis and viral microorganisms of the herpesviruses group, most notably Epstein–Barr virus (EBV-1) and Cytomegalovirus (CMV) [23, 24]. Furthermore, the presence of subgingival EBV-1 and CMV are associated with high levels of putative bacterial pathogens, including P. gingivalis, T. forsythia, P. intermedia, and T. denticola. These data support the hypothesis that viral infection may contribute to periodontal pathogenesis, but the potential role of viral agents remains to be determined. Oral Candida spp. are commensal oral fungi and are often isolated from teeth and oral muco-

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implantitis, may be an indication of that in most cases the disease evolves gradually from peri-­ implant  mucositis to peri-implantitis [52–54]. Although there is no evidence for the existence of one or a limited number of specific pathogens for peri-implantitis studies have repeatedly indicated that peri-implant infections may occasionally be linked to a microbiota with a different profile than in chronic periodontitis. Longitudinal observations have shown that Staphylococcus aureus may colonize implants early after placement and at different stages of implant restoration and may persist long term [56–57]. Various microbiologic methods have been used to evaluate microbial profiles of peri-­ implantitis and healthy implants, including cultivation, conventional polymerase chain reaction (PCR), quantitative PCR, nested-PCR, DNA hybridization, and sequencing methods [25, 46]. The major periodontopathic microorganisms, red complex: Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola; orange complex: Prevotella intermedia and Fusobacterium nucleatum; and other microorganisms: Aggregatibacter actinomycetemcomitans, Eikenella corrodens, Parvimonas micra, Disease Progression in Dental and Campylobacter rectus, were identified. Implants Some studies also evaluated periodontopathic Shifts in the subgingival microbime composation and opportunistic microorganisms [58, 59]. from a healthy state to gingivitis or peri-implant Studies using sequencing methods showed vast mucositis have been described [34, 49]. Multiple variability in the number of 16S rRNA gene factors appear to contribute to the overall resis- sequences at phylum, genus, and species levtance of biofilm bacteria. Bacterial colonization els, although all studies evaluated periodontal and maturation of biofilms depend on favorable bacteria. According to Lafaurie et  al., comparecological environment, and lead to shifts in the ing differences between major periodontopathic composition and behavior of the endogenous microorganisms, bacteria from the red complex microbiome that may become intolerable for host more frequently were found [46]. Orange comtissues. During oral hygiene abstention, implants plex microorganisms were more frequent in accumulated less plaque than teeth, whereas peri-­implantitis. Porphyromonas gingivalis was the gingivitis microbiome composition is more the most frequently found red complex organdiverse than that of peri-implant mucositis [5, ism in peri-implantitis followed by Tannerella 49]. Thus, changes in local ecological conditions forsythia. Prevotella intermedia and Prevotella that favor the growth of bacterial pathogens, or nigrescens were more frequently found in peritrigger the expression of virulence factors, may implantitis followed by Fusobacterium nucleabe viewed as the true origin of peri-implant dis- tum, and Parvimonas micra. Campylobacter ease [52]. rectus and Eikenella corrodens were less freThe lack of marked microbiological differ- quently found in peri-implantitis. The studies ences between peri-implant  mucositis and peri-­ that evaluated opportunistic ­ microorganisms

sal surfaces. Fungi can also adhere to nonbiological surfaces, such as dental implants. Colony forming units of Candida from the subgingival samples were significantly higher in the patients diagnosed with peri-implantitis as compared to patients without peri-implant. This may indicate that the existence of an increased oral Candida load may play a role in peri-implantitis [50]. The metagenomic techniques revealed more diverse microbiologic profiles in both periodontitis and peri-implantitis than previously thought [8, 12]. The periodontitis-associated microbiota is more heterogeneous and diverse than previously thought (i.e. on the basis of plated culture studies) and many of the newly recognized organisms (e.g. Filifactor alocis and other species from the genera Peptostreptococcaceae, Desulfobulbus and Synergistetes) show a good or better correlation with disease than the classic red-complex bacteria, Porphyromonas gingivalis, Treponema denticola and Tannerella forsythia [7, 8, 12, 51]. Thus, their role needs to be re-interpreted in the face of newly emerging evidence [51].

Microbiological Factors of Peri-Implantitis: Characteristics and Significance

found Gram-­ negative enteric rods and Staphylococcus aureus in peri-implantitis sites [58, 60]. According to Tamura et al. other noncultivable microorganisms were associated with peri-implantitis when the microbiome composition was ­evaluated by 16S rRNA  sequence techniques, including asaccharolytic anaerobic Gram-positive rod associated species such as Eubacterium nodatum, Eubacterium brachy, Eubacterium saphenum, Filifactor alocis, Slackia exigua, and Parascardovia denticolens [61]. Dialister invisus, Eubacterium infirmum, Actinomyces cardiffensis, Eubacterium minutum, and Gemella sanguinis as well as anaerobic Gram-negative rods such as Mitsuokella sp. human oral taxon (HOT) 131, Leptotrichia hofstadii, Kingella denitrificans, and Treponema lecithinolyticum, as well as microorganisms of the orange complex such as P. intermedia; and Gram-positive cocci such as Streptococcus sp. HOT 064 were also associated with peri-implantitis, indicating that these microorganisms could be important in the etiology of peri-implantitis [39, 59].

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composition of microbiota between teeth and implants in the short term [55, 63]. In the study comparing microbiota of periodontal pocket using real-time PCR analysis, peri-implant sulcus and inside of the implant, the microbiological profile for all three sites were differently characterized between patients with and without peri-implantitis. Both periopathogens and opportunistic pathogens showed tendency to increase in peri-implantitis when compared to healthy implants. Typical opportunistic pathogens such as C. albicans and E. faecalis were increased in peri-implant sulcus as well as in periodontal sulcus. In peri-implantitis Candida albicans and total Enterococcus were increased. Staphylococcus aureus did not show any significant difference among these three sites [64].

I mplant Abutment Connection and Interface

Bacterial leakage along the implant-abutment interface (“microgap”) has been also discussed in the literatures [65–69]. The microgap may function as “nests” for anaerobic or microaerophilic Microbiological Factors Related bacteria [70]. DNA-DNA Checkerboard analysis of the bacteria from internal surfaces and/or the to Dental Implant Structure healing abutment screw-threads from the two-­ piece design implants showed that moderate to Peri-Implant Sulcus high levels of A. actinomycetemcomitans, T. forIn a study of early colonization of dental implants sythensis, C. rectus, E. corrodens, F. nucleatum, in dentate patients with a history of periodonti- P. gingivalis, P. intermedia, T. denticola inhabit tis, Quirynen et  al. observed that periodontitis-­ the internal surfaces of the microgap identified. associated bacteria of the red complex, i.e., A DNA-DNA Checkerboard study comparPorphyromonas gingivalis, Tannerella forsythia, ing the peri-implant sulcus and inner implant and Treponema denticola, could be detected in compartment in healthy implants described the peri-implant sulcus within 1 week after abut- high detection frequencies (27.3–86.4%) of the ment connection [62]. Checkerboard DNA–DNA species including Fusobacterium periodontihybridization and real-time PCR revealed similar cum, Fusobacterium nucleatum sp. Nucleatum, detection frequencies of red and orange complex Campylobacter showae, Leptotrichia buccalis, species in peri-implant sulcus to those observed Streptococcus mutans, Streptococcus intermein the shallow periodontal sulcus of natural teeth. dius, Eubacterium saburreum, Aggregatibacter Colonization of the peri-implant submucoral area actinomycetemcomitans Y4, Streptococcus is similar to the composition of the subgingival mitis, Fusobacterium nucleatum, Naviforme, microbiota, however,  the total bacterial load is Staphylococcus aureus, at the inner implant significantly lower in the implants compared to compartment [66]. When comparing bactethe teeth. Previous studies have shown a similar rial levels between the peri-implant sulcus and

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inner implant compartment, only those of T. forsythia and Streptococcus mutans were significantly lower at the latter. Majority of the species were detected in significantly lower counts at the abutment screw when compared with the peri-implant sulcus. However, a relevant link in counts for a many bacteria species was described between these compartments. Contamination of abutment screws most likely occurred from the peri-implant sulcus via the implant-abutment interface and abutment-prosthesis interface. Canullo et al. analyzed the microbiology of the implant abutment connection with quantitative real-time PCR in four different implant-abutment connections: external hexagon, double internal hexagon, internal hexagon with external collar, and conical connection. None of the connection designs had the capacity to prevent microbiological leakage through the implant/abutment microgap. However, the connection design might influence bacterial activity levels qualitatively and quantitatively, especially inside the implant connection [63]. Repeated screw tightening and abutment material may also influence the bacterial leakage [65, 66, 68].

Dental Implant Surface Implant surface characteristics, including implant chemistry and roughness, have been demonstrated to influence bacterial attachment and proliferation [71, 72]. The surface roughness of dental implant significantly impacts the quantity and quality of the plaque formed. Rough surfaces and those presenting greater surface free energy tend to accumulate more plaque. Bacterial adhesion starts in areas of high wettability and inside the pits and grooves of the roughened surfaces, wherefrom it is difficult to eliminate [72]. Surface roughness has been proposed as the main feature favoring biofilm development in early stage [73]. Microbial colonization begins at the surface of the transmucosal abutment, an area exposed to the oral cavity. Once The implant surfaces become quickly covered by an acquired pellicle due to adsorption of salivary proteins that

provide linking sites for microorganism adhesion [74]. The microbial biofilm developed on implant surfaces can cause peri-implant mucositis and peri-implantitis [75, 76]. The micro and nano roughness of the implant surface attribute to the challenge of achieving adequate decontamination in order to facilitate re-osseointegration of failing implants [77–79]. Resolution of peri-implantitis following treatment with or without systemic or local antimicrobial therapy is possible but the outcome of treatment is influenced by implant surface characteristics [80]. Surgical and non-surgical therapies to treat peri-implantitis are discussed in elsewhere.

Summary The microbiome around dental implants contribute significantly to the disease progression and the etiology of peri-implantitis. The microbiome composition isolated in peri-implantitis and periodontitis are distinctly different. The microbiota is present in peri-implantitis is more diverse and a large proportion is composed of Gram-negative bacteria species. Peri-implantitis was shown to be associated with certain pathogens (e.g. Gram-­ negative anaerobic periopathogens and opportunistic microorganisms). However, more studies are needed to demonstrate how these pathogens contribute to the initiation and maintenance of peri-implant disease. In addition, implant surface characteristics, the implant-abutment interface, and the internal compartment of the implants are all areas that should be considered as when targeting foci to reduce bacterial load and in the treatment of peri-implantitis.

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73. Bevilacqua L, Milan A, Del Lupo V, Maglione M, Dolzani L. Biofilms developed on dental implant titanium surfaces with different roughness: comparison between in vitro and in vivo studies. Curr Microbiol. 2018;75(6):766–72. 74. van Dijk J, Herkstroter F, Busscher H, Weerkamp A, Jansen H, Arends J.  Surface-free energy and bacterial adhesion. An in vivo study in beagle dogs. J Clin Periodontol. 1987;14(5):300–4. 75. Lindhe J, Meyle J.  Peri-implant diseases: consensus report of the sixth European workshop on periodontology. J Clin Periodontol. 2008;35(8 Suppl):282–5. 76. Zitzmann NU, Berglundh T.  Definition and prevalence of peri-implant diseases. J Clin Periodontol. 2008;35(8 Suppl):286–91. 77. Subramani K, Wismeijer D.  Decontamination of titanium implant surface and re-osseointegration to treat peri-implantitis: a literature review. Int J Oral Maxillofac Implants. 2012;27(5):1043–54. 78. Schwarz F, Schmucker A, Becker J. Efficacy of alternative or adjunctive measures to conventional treatment of peri-implant mucositis and peri-implantitis: a systematic review and meta-analysis. Int J Implant Dent. 2015;1(1):22. 79. Tomasi C, Regidor E, Ortiz-Vigon A, Derks J. Efficacy of reconstructive surgical therapy at periimplantitis-related bone defects. A systematic review and meta-analysis. J Clin Periodontol. 2019;46(Suppl 21):340–56. 80. Albouy JP, Abrahamsson I, Persson LG, Berglundh T.  Implant surface characteristics influence the outcome of treatment of peri-implantitis: an experimental study in dogs. J Clin Periodontol. 2011;38(1):58–64.

Association of Periodontitis and Biologic Implant Complications Harlan J. Shiau, Hanae Saito, and Mark A. Reynolds

Contents Introduction

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Associations Between Periodontitis and Peri-Implant Disease

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How Does Periodontitis Increase Risk of Biological Implant Complication?

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Periodontitis and Peri-Implantitis: Role of Common Etiologic Factors

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Conclusion

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References

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Introduction Dental implants represent a predictable and ­successful treatment approach to the restoration of partially and fully edentulous patients. Longitudinal studies suggest a cumulative mean implant survival rate of nearly 95% [1]. Implant supported rehabilitation yields improvements in patient related outcomes, with respect to comfort, function, and esthetics [2, 3]. Despite overall survival rates, expectations regarding clinical outcomes may need to be tempered as the prevalence of implant complications, whether of mechanical or biologic origin, has emerged as a crucial clini-

H. J. Shiau · H. Saito · M. A. Reynolds (*) Division of Periodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland School of Dentistry, Baltimore, MD, USA e-mail: [email protected]

© Springer Nature Switzerland AG 2020 Y. Ogata (ed.), Risk Factors for Peri-implant Diseases, https://doi.org/10.1007/978-3-030-39185-0_5

cal concern [4]. The principle biologic complication in dental implants is peri-implant mucositis and peri-implantitis. Although true estimates of the prevalence of peri-implantitis are hampered by heterogeneity in case definitions, recent systematic reviews and meta-analyses reveal an estimated implant-based prevalence between 9.25% and 12.8% and patient-based prevalence between 18.5% and 19.83% [5, 6]. The latter estimate ranges are based on prevalent cases—that is, persons with peri-implant disease at the outset of the study, as well as those that developed peri-­ implant disease over the course of the study. Available longitudinal studies, however, do not permit accurate estimates of incidence; namely, the rate of new (or newly diagnosed) cases. The assumption is that the prevalence of peri-implant disease will continue to increase, paralleling increases in the clinical use of dental implants to support prosthetic rehabilitation.

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Current approaches to treatment of peri-­ implant disease are heterogeneous, often combinatorial in strategy and technology, with treatment outcomes that remain largely inconsistent. In addition, patients that are non-compliant with routine follow-up care (e.g., supportive therapy/ implant maintenance) often present with more advanced peri-implant disease. Efforts to clarify risk factors or indicators leading to peri-implant disease are critical to developing more effective strategies for prevention and treatment. The results of retrospective and prospective population studies indicate that individuals with a history of periodontitis are at greater risk of biological implant complications, particularly peri-implantitis. Given the relatively high prevalence of periodontitis in the US adult population, ranging upwards of 42% [7], periodontitis represents a particularly important consideration in treatment planning.  As implantbased solutions represent an option to address missing teeth, there is an obligation for the clinician and patient to have a realistic and grounded appraisal of the degree to which  history of periodontitis may modify therapeutic outcome.  There are two principle peri-implant inflammatory conditions of biologic origin that are attributed most often to bacterial biofilms. Peri-­implantitis is an inflammatory condition involving the periimplant tissues that is associated with resorption of implant-supporting bone [8]. Peri-implant mucositis is defined as an exclusively inflammatory lesion of the surrounding mucosa of an implant. In this scenario there is no concomitant resorption of supporting bone, nor resultant loss of osseointegration. The lack of standard, generally agreed upon clinical definitions has hampered the precision of our scientific literature addressing peri-implant infections. Despite differences in clinical definitions such as for peri-implantitis, the components of destructive change to bone, and concomitant inflammation of peri-implant soft tissue are consistently captured by available definitions [9]. Periodontitis is primarily a bacteria biofilm infection, with a robust host-mediated inflammatory process resulting in breakdown of supporting alveolar bone and periodontal attachment apparatus. Modifiable and non-modifiable factors have significance in the pathophysiology of periodonti-

H. J. Shiau et al.

tis, including onset, progression, and severity. Such factors include local modifiable influences, such as calculus, dental restorations, and poor oral hygiene, that contribute to the development of plaque biofilm burden associated with periodontitis. Additionally, there are systemic conditions such as uncontrolled diabetes that increase the risk of periodontitis [10]. There is accumulating evidence for a genetic susceptibility for periodontitis, with genetic polymorphisms in IL-1, IL-6, CD14, TLR4 and MMP1 genes as a factor in specific populations [11]. Periodontitis is associated with transient bacteremia and elevations in systemic markers of inflammation. Consequently, considerable research has focused on the association between periodontitis and systemic disease. For example, epidemiologic evidence supports the postulate that selected systemic conditions, such as cardiovascular disease and diabetes, have attributable risk due to periodontitis [12, 13]. This chapter explores the relationship between periodontitis and peri-implant infections, building first on population studies characterizing the nature of the association. Consideration is then given to current understanding of the etiologic factors and mechanisms underlying these conditions as well as apparent areas of overlap and diversity in the pathobiology.

 ssociations Between Periodontitis A and Peri-Implant Disease Early interest in the relationship between periodontitis and peri-implant diseases arose from the notion that dental implants, similar to teeth, are susceptible to microbial biofilm infection. Indeed, the early conception of peri-implantitis highlighted the consistent findings of gram-­ negative anerobic microflora at failing implant sites [14]; conversely, the microbial milieu of a healthy implant is comparable to that of non-­ periodontally involved teeth [15]. With the increased clinical adoption of dental implants for oral rehabilitation, retrospective and prospective studies have been conducted to examine the relationship between periodontitis and peri-implant disease (see Fig. 1).

Association of Periodontitis and Biologic Implant Complications

Fig. 1  Peri-implantitis lesion with history of periodontitis. A 58-year old male with Stage 3, Grade B periodontitis developed peri-implantitis in the anterior sextant. The

I ndirect Assessments: Implant Survival, Implant Success, and Marginal Bone Loss

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implants had been in function for an estimated 5 years; the patient was erratic in routine supportive dental care

identified studies reporting implant survival in patients with treated periodontitis compared with non-periodontitis patients [19]. The selected studies were variable in follow-up evaluation  times, The association between periodontitis and peri-­ ranging from 1.2 to 16  years. Here, the pooled implant disease has been assessed using implant non-periodontitis group implant survival rate survival as a surrogate outcome measure. Implant ranged from 91.7% to 100%, while the treated loss in specific clinical scenarios, represents the periodontitis group presented survival rates from endpoint of the peri-implantitis lesion, analogous 79.2% to 100%. Longer observation periods may to the periodontally compromised tooth. Such an be advantageous and more appropriate to capture interpretation of implant survival data must be loss of implant, as a surrogate measure for peritempered with the knowledge that other factors implantitis. Studies reporting 10-year or longer contribute to implant complications and loss. follow up periods report a similar and consistent Mechanical overload, for example, has been often pattern of implant loss. Karoussis, et al. sought to identified as a factor contributing to implant fail- evaluate the prognosis of 112 implants placed in ure [16–18]. Nonetheless, implant survival has 53 patients, following them for 10 years. The surbeen evaluated in partially dentate patients who vival rate in a cohort with a past history of chronic have been treated for periodontitis, and compared periodontitis was 90.5%, while those with no past to patients without clinical or radiographic history history of periodontitis had an implant survival of periodontitis. Across these studies there is a rate of 96.5% [20]. Long-term implant survival degree of variability in design, such as variable was also determined by another 10-year study of diagnostic case definitions, and variable support- implants placed in 91 treated-periodontitis and in ive maintenance protocols. A systematic review 32 p­ eriodontally healthy patients. Periodontally

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healthy patients had an implant survival rate of implantitis. The outcome of implants placed in 96.7%, significantly different from the 90% sur- partially edentulous patients with either a history vival rate of those with a history of severe peri- of periodontitis, or no periodontitis, was moniodontitis [21]. tored for at least 5  years in several studies [20, Implant success rate in patients with a history 21, 24, 28–31]. These studies do not provide a of periodontitis  is an additional indirect, surro- strong foundation for estimating the magnitude gate  measure of peri-implantitis. A subset of of the effect of history of periodontitis on risk of implants that deviate from the classic definitions peri-implantitis, given the heterogeneity in study of implant success [22, 23] arguably represent the methodology and case definitions. For example, biological complication of peri-implantitis. studies have used substantially different meaSeveral studies have looked at success rates in sures of periodontitis, including tooth loss due to patients with a history of treated periodontitis, periodontitis, alveolar bone levels, modifications compared to those who are periodontally healthy of the community periodontal index of treatment [20, 24, 25]. For example, one retrospective needs (CPITN) index, clinical attachment level study of 1511 implants placed and having up to (CAL), and combination CAL/probing depth, as five-years with a definitive prosthesis, reported an parameters to define a history of periodontitis. overall minimal difference in success between Likewise, study definitions of peri-implantitis are periodontally healthy and periodontally compro- plagued with heterogeneity—though, most ratiomised patients—93.7% compared to 90.6% [24]. nally include an element of inflammation, such as In contrast, Karoussis and colleagues, with bleeding on probing and/or purulence, and a 10-year follow up, reports a larger gap in success component of implant associated bone destrucrate. Here, with success criteria defined as prob- tion, often specifying a threshold of bone loss. ing depth ≤5 mm, negative bleeding in probing, In spite of these challenges, the literature and