Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.
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ABSTRACT:
SUBMITTER: Wang CW
PROVIDER: S-EPMC8171076 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.
| S-EPMC8160334 | biostudies-literature
Project description:BackgroundDental implants have become well-established in oral rehabilitation for fully or partially edentulous patients. However, peri-implantitis often leads to the failure of dental implants. The aim of this study was to understand the core microbiome associated with peri-implantitis and evaluate potential peri-implantitis pathogens based on canine peri-implantitis model.ResultsIn this study, three beagle dogs were used to build peri-implantitis models with ligature-induced strategy. The peri-implant sulcular fluids were collected at four different phases based on disease severity during the peri-implantitis development. Microbial compositions during peri-implantitis development were monitored and evaluated. The microbes were presented with operational taxonomic unit (OTU) classified at 97% identity of the high-throughput 16S rRNA gene fragments. Microbial diversity and richness varied during peri-implantitis. At the phylum-level, Firmicutes decreased and Bacteroides increased during peri-implantitis development. At the genus-level, Peptostreptococcus decreased and Porphyromonas increased, suggesting peri-implantitis pathogens might be assigned to these two genera. Further species-level and co-occurrence network analyses identified several potential keystone species during peri-implantitis development, and some OTUs were potential peri-implantitis pathogens.ConclusionIn summary, canine peri-implantitis models help to identify several potential keystone peri-implantitis associated species. The canine model can give insight into human peri-implantitis associated microbiota.
| S-EPMC7526148 | biostudies-literature
Project description:Dental implants to replace lost teeth are a common dentistry practice nowadays. Titanium dental implants display a high success rate and improved safety profile. Nevertheless, there is an increasing peri-implantitis (PI), an inflammatory disease associated with polymicrobial infection that adversely affects the hard and soft tissues around the implant. The present review highlights the contribution of different metabolic conditions to PI. The considerations of both local and systemic metabolic conditions are crucial for planning successful dental implant procedures and during the treatment course of PI. Un- or undertreated PI can lead to permanent jaw bone suffering and dental implant losses. The common mediators of PI are inflammation and oxidative stress, which are also the key mediators of most systemic metabolic disorders. Chronic periodontitis, low-grade tissue inflammation, and increased oxidative stress raise the incidence of PI and the underlying systemic metabolic conditions, such as obesity, diabetes mellitus, or harmful lifestyle factors (cigarette smoking, etc.). Using dental biomaterials with antimicrobial effects could partly solve the problem of pathogenic microbial contamination and local inflammation. With local dentistry considering factors, including oral microbiota and implant quality control, the inclusion of the underlying systemic metabolic conditions into the pre-procedure planning and during the treatment course should improve the chances of successful outcomes.
| S-EPMC9854649 | biostudies-literature
Project description:Peri-implantitis is one of the most important biological complication of dental implants. It has inflammatory nature, proved association with plaque accumulation in peri-implant tissues, and can be progressive on background of several factors, like comorbidity factors and bad habits. The prophylaxis and different methods of treatment were discussed during last 30 years, and surgical and nonsurgical techniques have their foes, benefits, and disadvantages. In this article, we describe the case series of various nonsurgical treatments of peri-implantitis with the use of protocols based on the application of local antibiotics (doxycycline, lincomycin, and erythromycin), mechanical and chemical debridement of dental implant surface, and mini-invasive regenerative technique with injections of bovine collagen. All these three cases demonstrated good results with the maintenance of bone level and absence of clinical signs of inflammation for at least a year according to the X-ray imaging (bone defect volume) and clinic assessments (probing depth, bleeding or suppuration, mucosa color, and pain presence).
| S-EPMC7557973 | biostudies-literature
Project description:Peri-implantitis and periodontitis are both polymicrobial diseases induced by subgingival plaque accumulation, with some differing clinical features. Studies on the microbial and gene transcription activity of peri-implantitis microbiota are limited. This study aimed to verify the hypothesis that disease-specific microbial and gene transcription activity lead to disease-specific clinical features, using an integrated metagenomic, metatranscriptomic, and network analysis. Metagenomic data in peri-implantitis and periodontitis were obtained from the same 21 subjects and metatranscriptomic data from 12 subjects were obtained from a database. The microbial co-occurrence network based on metagenomic analysis had more diverse species taxa and correlations than the network based on the metatranscriptomic analysis. Solobacterium moorei and Prevotella denticola had high activity and were core species taxa specific to peri-implantitis in the co-occurrence network. Moreover, the activity of plasmin receptor/glyceraldehyde-3-phosphate dehydrogenase genes was higher in peri-implantitis. These activity differences may increase complexity in the peri-implantitis microbiome and distinguish clinical symptoms of the two diseases. These findings should help in exploring a novel biomarker that assist in the diagnosis and preventive treatment design of peri-implantitis.
| S-EPMC7793907 | biostudies-literature
Project description:ObjectiveTo describe the submucosal microbial profiles of peri-implantitis and healthy implants, and to explore bacteria that might be correlated with clinical parameters.MethodsIn the present cross-sectional study, 49 patients were recruited. Each patient contributed with one implant, submucosal biofilms were collected from 20 healthy implants and 29 implants with peri-implantitis. DNA was extracted and bacterial 16S ribosomal RNA (16S rRNA) genes were amplified. Submucosal biofilms were analyzed using 16S rRNA sequencing at Illumina MiSeq platform. Differences between the groups were determined by analyzing α diversity, microbial component and microbial structure. The potential correlation between the bacteria with pocket probing depth (PPD) of peri-implant calculated by Spearman correlation analysis.ResultsThe α diversity of submucosal microbial of health group was significantly lower than that in peri-implantitis group (Chao1 index: 236.85±66.13 vs. 150.54±57.43, P < 0.001; Shannon index: 3.42±0.48 vs. 3.02±0.65, P=0.032). Principal coordinated analysis showed that the submucosal microbial structure had significant difference between healthy and peri-implantitis groups [R2=0.243, P=0.001, analysis of similarities (ANOSIM)]. Compared with healthy implants, relative abundance of periodontal pathogens were higher in peri-implantitis, including members of the red complex (Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola) and some members of orange complex (Precotella intermedia, Eubacterium nodatum, Parvimonas micra), as well as some new periodontal pathogens, such as Fillifactor alocis, Fretibacterium fastidiosum, Desulfobulbus sp._HMT_041, and Porphyromonas endodontalis. Spearman correlation analysis revealed that the relative abundance of Treponema denticola (r=0.686, P < 0.001), Tannerella forsythia (r=0.675, P < 0.001), Fretibacterium sp. (r=0.671, P < 0.001), Desulfobulbus sp._HMT_041 (r=0.664, P < 0.001), Filifactor alocis (r=0.642, P < 0.001), Fretibacterium fastidiosum (r=0.604, P < 0.001), Porphyromonas gingivalis (r=0.597, P < 0.001), Porphyromonas endodontalis (r=0.573, P < 0.001) were positive correlated with PPD. While the relative abundance of Rothia aeria (r=-0.615, P < 0.001) showed negatively correlation with PPD.ConclusionMarked differences were observed in the microbial profiles of healthy implants and peri-implantitis. The members of red and orange complex as well as some new periodontal pathogens seem to play an important role in peri-implant disease. Compared with healthy implants, the submucosal microbial of peri-implantitis were characterized by high species richness and diversity.
| S-EPMC9894807 | biostudies-literature
Project description:Background and objectivesThe aim of this study was to examine the salivary microbiome in healthy peri-implant sites and those with peri-implantitis.MethodsSaliva samples were collected from 21 participants with healthy peri-implant sites and 21 participants with peri-implantitis. The V4 hypervariable region of the 16S rRNA gene was sequenced using the Ion Torrent PGM System (Ion 318™ Chip v2 400). The NGS analysis and composition of the salivary microbiome were determined by taxonomy assignment. Downstream bioinformatic analyses were performed in QIIME (v 1.9.1).ResultsClinical differences according to peri-implant condition status were found. Alpha diversity metrics revealed that the bacterial communities of participants with healthy peri-implant sites tended to have a richer microbial composition than individuals with peri-implantitis. In terms of beta diversity, bleeding on probing (BoP) may influence the microbial diversity. However, no clear partitioning was noted between the salivary microbiome of volunteers with healthy peri-implant sites or volunteers with peri-implantitis. The highest relative abundance of Stenotrophomonas, Enterococcus and Leuconostoc genus, and Faecalibacterium prausnitzii, Haemophilus parainfluenzae, Prevotella copri, Bacteroides vulgatus, and Bacteroides stercoris bacterial species was found in participants with peri-implantitis when compared with those with healthy peri-implant sites.ConclusionDifferences in salivary microbiome composition were observed between patients with healthy peri-implant sites and those with peri-implantitis. BoP could affect the diversity (beta diversity) of the salivary microbiome.
| S-EPMC8766799 | biostudies-literature
Project description:MotivationIn a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.ResultsWe demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.Availability and implementationThe Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6792064 | biostudies-literature
Project description:This systematic review sought to analyze different experimental peri-implantitis models, their potential to induce marginal bone resorption (MBR) and the necessity of bacteria for bone loss to occur in these models. An electronic search in PubMed/Medline, Web of Science, and ScienceDirect was undertaken. A total of 133 studies were analyzed. Most studies induced peri-implantitis with ligatures that had formed a biofilm, sometimes in combination with inoculation of specific bacteria but never in a sterile environment. Most vertical MBR resulted from new ligatures periodically packed above old ones, followed by periodically exchanged ligatures and ligatures that were not exchanged. Cotton ligatures produced the most MBR, followed by steel, "dental floss" (not further specified in the studies) and silk. The amount of MBR varied significantly between different animal types and implant surfaces. None of the analyzed ligature studies aimed to validate that bacteria are necessary for the inducement of MBR. It cannot be excluded that bone loss can be achieved by other factors of the model, such as an immunological reaction to the ligature itself or trauma from repeated ligature insertions. Because all the included trials allowed plaque accumulation on the ligatures, bone resorbing capacity due to other factors could not be excluded or evaluated here.
| S-EPMC6306937 | biostudies-literature
Project description:Dental implants are one of the most frequently used treatment options for tooth replacement. Approximately 30% of patients with dental implants develop peri-implantitis, which is an oral inflammatory disease that leads to loss of the supporting tissues, predominately the bone. For the development of future therapeutic strategies, it is essential to understand the molecular pathophysiology of human dental peri-implant infections. Here, we describe the gene and protein expression patterns of peri-implantitis bone tissue compared with healthy peri-implant bone tissue. Furthermore, cells from the osteoblastic lineage derived from peri-implantitis samples were immortalized and characterized. We applied microarray, quantitative reverse transcription polymerase chain reaction, fluorescence-activated cell sorting, and Western blot analyses. The levels of typical bone matrix molecules, including SPP1, BGLAP, and COL9A1, in patients with peri-implantitis were reduced, while the inflammation marker interleukin 8 (IL8) was highly expressed. RUNX2, one of the transcription factors of mature osteoblasts, was also decreased in peri-implantitis. Finally, the human telomerase reverse transcriptase immortalized cell line from peri-implantitis exhibited a more fibro-osteoblastic character than did the healthy control.
| S-EPMC4438728 | biostudies-literature