Thiago Morelli

Saliva as a diagnostic tool for periodontal disease: current state and future directions.

In periodontics and implant dentistry, traditional clinical criteria are often insufficient for determining sites of active disease, for monitoring quantitatively the response to therapy or for measuring the degree of susceptibility to future disease progression. Saliva as a mirror of oral and systemic health is a valuable source for clinically relevant information because it contains biomarkers specific for the unique physiological aspects of periodontal ⁄ peri-implant disease, and qualitative changes in the composition of these biomarkers could have diagnostic value by identifying patients with enhanced disease susceptibility, identifying sites with active disease, predicting sites that will have active disease in the future and ⁄ or serving as surrogate end points for monitoring the effectiveness of therapy. Although the diagnostic value of saliva has been recognized for some time (50, 51) and potential biomarkers of periodontal ⁄ periimplant disease have been identified in saliva (39, 67, 74), most work carried out to date has failed to provide reliable aids to the clinician. However, the availability of more sophisticated analytic techniques give cause for optimism that saliva will eventually become the tool needed for more precise treatment planning.

Saliva/Pathogen Biomarker Signatures and Periodontal Disease Progression

The purpose of this study was to determine the role of saliva-derived biomarkers and periodontal pathogens during periodontal disease progression (PDP). One hundred human participants were recruited into a 12-month investigation. They were seen bi-monthly for saliva and clinical measures and bi-annually for subtraction radiography, serum and plaque biofilm assessments. Saliva and serum were analyzed with protein arrays for 14 pro-inflammatory and bone turnover markers, while qPCR was used for detection of biofilm. A hierarchical clustering algorithm was used to group study participants based on clinical, microbiological, salivary/serum biomarkers, and PDP. Eighty-three individuals completed the six-month monitoring phase, with 44 exhibiting PDP, while 39 demonstrated stability. Participants assembled into three clusters based on periodontal pathogens, serum and salivary biomarkers. Cluster 1 members displayed high salivary biomarkers and biofilm; 82% of these individuals were undergoing PDP. Cluster 2 members displayed low biofilm and biomarker levels; 78% of these individuals were stable. Cluster 3 members were not discriminated by PDP status; however, cluster stratification followed groups 1 and 2 based on thresholds of salivary biomarkers and biofilm pathogens. The association of cluster membership to PDP was highly significant (p < 0.0002). The use of salivary and biofilm biomarkers offers potential for the identification of PDP or stability (ClinicalTrials.gov number, CT00277745).

Angiogenic Biomarkers and Healing of Living Cellular Constructs

The use of intra-oral soft-tissue-engineered devices has demonstrated potential for oral mucosa regeneration. The aim of this study was to investigate the temporal expression of angiogenic biomarkers during wound healing of soft tissue reconstructive procedures comparing living cellular constructs (LCC) with autogenous free gingival grafts. Forty-four human participants bilaterally lacking sufficient zones of attached keratinized gingiva were randomly assigned to soft tissue surgery plus either LCC or autograft. Wound fluid samples were collected at baseline and weeks 1, 2, 3, and 4 post-operatively and analyzed for a panel of angiogenic biomarkers: angiogenin (ANG), angiostatin (ANT), PDGF-BB, VEGF, FGF-2, IL-8, TIMP-1, TIMP-2, GM-CSF, and IP-10. Results demonstrated a significant increase in expression of ANT, PDGF-BB, VEGF, FGF-2, and IL-8 for the LCC group over the autograft group at the early stages of wound repair. Although angiogenic biomarkers were modestly elevated for the LCC group, no clinical correlation with wound healing was found. This human investigation demonstrates that, during early wound-healing events, expression of angiogenic-related biomarkers is up-regulated in sites treated with LCC compared with autogenous free gingival grafts, which may provide a safe and effective alternative for regenerating intra-oral soft tissues (ClinicalTrials.gov number, NCT01134081).

Development and Applications of Porous Tantalum Trabecular Metal-Enhanced Titanium Dental Implants

BACKGROUND Porous tantalum trabecular metal has recently been incorporated in titanium dental implants as a new form of implant surface enhancement. However, there is little information on the applications of this material in implant dentistry. PURPOSE The purpose of this article is to summarize the contemporary concept on the applications of porous tantalum trabecular metal in implant dentistry. MATERIALS AND METHODS We therefore review the current literature on the basic science and clinical uses of this material. RESULTS Porous tantalum metal is used to improve the contact between osseous structure and dental implants and therefore presumably facilitate osseointegration. Success of porous tantalum metal in orthopedic implants led to the incorporation of porous tantalum metal in the design of root-form endosseous titanium implants. The porous tantalum three-dimensional enhancement of titanium dental implant surface allows for combining bone ongrowth together with bone ingrowth, or osseoincorporation. While little is known about the biological aspect of the porous tantalum in the oral cavity, there seems to be several possible advantages of this implant design. This article reviews the biological aspects of porous tantalum-enhanced titanium dental implants, in particular the effects of anatomical consideration and oral environment to implant designs. CONCLUSIONS We propose here possible clinical situations and applications for this type of dental implant. Advantages and disadvantages of the implants as well as needed future clinical studies are discussed.

Genome-wide association study of biologically-informed periodontal complex traits offers novel insights into the genetic basis of periodontal disease

Genome-wide association studies (GWAS) of chronic periodontitis (CP) defined by clinical criteria alone have had modest success to-date. Here, we refine the CP phenotype by supplementing clinical data with biological intermediates of microbial burden (levels of eight periodontal pathogens) and local inflammatory response (gingival crevicular fluid IL-1β) and derive periodontal complex traits (PCTs) via principal component analysis. PCTs were carried forward to GWAS (∼2.5 million markers) to identify PCT-associated loci among 975 European American adult participants of the Dental ARIC study. We sought to validate these findings for CP in the larger ARIC cohort (n = 821 participants with severe CP, 2031—moderate CP, 1914—healthy/mild disease) and an independent German sample including 717 aggressive periodontitis cases and 4210 controls. We identified six PCTs with distinct microbial community/IL-1β structures, although with overlapping clinical presentations. PCT1 was characterized by a uniformly high pathogen load, whereas PCT3 and PCT5 were dominated by Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis, respectively. We detected genome-wide significant signals for PCT1 (CLEC19A, TRA, GGTA2P, TM9SF2, IFI16, RBMS3), PCT4 (HPVC1) and PCT5 (SLC15A4, PKP2, SNRPN). Overall, the highlighted loci included genes associated with immune response and epithelial barrier function. With the exception of associations of BEGAIN with severe and UBE3D with moderate CP, no other loci were associated with CP in ARIC or aggressive periodontitis in the German sample. Although not associated with current clinically determined periodontal disease taxonomies, upon replication and mechanistic validation these candidate loci may highlight dysbiotic microbial community structures and altered inflammatory/immune responses underlying biological sub-types of CP.

Association of Synergistetes and Cyclodipeptides with Periodontitis

The purpose of this study was to evaluate the microbial community (MC) composition as it relates to salivary metabolites and periodontal clinical parameters in a 21-d biofilm-overgrowth model. Subjects (N = 168) were enrolled equally into 5 categories of periodontal status per the biofilm-gingival interface classification. Microbial species within subgingival plaque samples were identified by human microbiome identification microarray. Whole saliva was analyzed by liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry for metabolite identification. Phylum was grouped into MCs according to principal component analysis. Generalized linear and regression models were used to examine the association among MC, species, periodontal clinical parameters, and salivary metabolome. Multiple comparisons were adjusted with the false discovery rate. The study population was distributed into 8 distinct MC profiles, designated MC-1 to MC-8. MC-2 explained 14% of the variance and was dominated by Synergistetes and Spirochaetes. It was the only community structure significantly associated with high probing depth (P = 0.02) and high bleeding on probing (P = 0.008). MC-2 was correlated with traditional periodontal pathogens and several newly identified putative periodontal pathogens: Fretibacterium fastidiosum, Fretibacterium sp. OT360/OT362, Filifactor alocis, Treponema lecithinolyticum, Eubacterium saphenum, Desulfobulbus sp./OT041, and Mogibacterium timidum. Synergistetes phylum was strongly associated with 2 novel metabolites—cyclo (-leu-pro) and cyclo (-phe-pro)—at 21 d of biofilm overgrowth (P = 0.02). In subjects with severe periodontitis (P2 and P3), cyclo (-leu-pro) and cyclo (-phe-pro) were significantly associated with increased changes in probing depth at 21 d of biofilm overgrowth (P ≤ 0.05). The analysis identified a MC dominated by Synergistetes, with classic and putative newly identified pathogens/pathobionts associated with clinical disease. The metabolomic discovery of 2 novel cyclodipeptides that have been reported to serve as quorum-sensing and/or bacteriocidal/bacteriostatic molecules, in association with Synergistetes, suggests a potential role in periodontal biofilm dysbiosis and periodontal disease that warrants further investigation.

Derivation and Validation of the Periodontal and Tooth Profile Classification System for Patient Stratification

BACKGROUND The goal of this study is to use bioinformatics tools to explore identification and definition of distinct periodontal and tooth profile classes (PPCs/TPCs) among a cohort of individuals by using detailed clinical measures at the tooth level, including both periodontal measurements and tooth loss. METHODS Full-mouth clinical periodontal measurements (seven clinical parameters) from 6,793 individuals from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom latent class analysis (LCA) procedure was developed to identify clinically distinct PPCs and TPCs. Three validation cohorts were used: NHANES (2009 to 2010 and 2011 to 2012) and the Piedmont Study population (7,785 individuals). RESULTS The LCA method identified seven distinct periodontal profile classes (PPCs A to G) and seven distinct tooth profile classes (TPCs A to G) ranging from health to severe periodontal disease status. The method enabled identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. The PPC algorithm was applied and confirmed in three distinct cohorts. CONCLUSIONS The findings suggest PPC and TPC using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at an individual and tooth level. These classifications can potentially be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients.

In search of appropriate measures of periodontal status: The periodontal profile phenotype (P3) system

BACKGROUND This paper focuses on Periodontal Profile Class (PPC), a component of the Periodontal Profile Phenotype (P3 ) System that may be more representative of the periodontitis phenotype than current case definitions of periodontitis. Data illustrate the unique aspects of the PPC compared with other commonly used periodontal classification indices. METHODS Latent Class Analysis (LCA) identified discrete classes of individuals grouped by tooth-level clinical parameters. The analysis defined seven distinct periodontal profile classes (PPC A through G) and seven distinct tooth profile classes (TPC A through G). This LCA classification was an entirely data-derived agnostic process without any preconceived presumptions of what constituted disease. RESULTS Comparing the PPC with the Centers for Disease Control/American Academy of Periodontology (CDC/AAP) and European indices, the PPC is unique in that it contains four disease classes not traditionally used. Less than half of individuals classified as Healthy by both the CDC/AAP and European indices were Healthy using the PPC. About 25% of those classified as Severe by CDC/AAP and European indices were PPC-Severe. The remainder spread out over the High Gingival Index, Posterior Disease, Tooth Loss, and Severe Tooth Loss phenotypes. CONCLUSIONS The PPC classification provides a significant departure from the traditional clinical case status indices that have been used, but has resulted in clinical phenotypes that are quite familiar to most clinicians who take notice of the distribution of missing teeth, areas of recession, diminished periodontal support, and other aspects of the dentition while conducting a periodontal examination. The mutually exclusive categories provided by the PPC system provide periodontal clinical summaries that can be an important component of precision dentistry.

The Novel ASIC2 Locus Is Associated with Severe Gingival Inflammation

An increasing body of evidence suggests a significant genetic regulation of inflammatory response mechanisms; however, little is known regarding the genetic determinants of severe gingival inflammation (GI). We conducted a genome-wide association study of severe GI among 4,077 European American adults, participants in the Dental Atherosclerosis Risk in Communities cohort. The severe GI trait was defined dichotomously with the 90th percentile of gingival index ≥2 extent score. Genotyping was performed with the Affymetrix 6.0 array platform, and an imputed set of 2.5 million markers, based on HapMap Phase II CEU build 36, was interrogated. Genetic models were based on logistic regression and controlled for ancestry (10 principal components), sex, age, and examination center. One locus on chromosome 17 met genome-wide statistical significance criteria—lead single-nucleotide polymorphism: rs11652874 (minor allele frequency = 0.06, intronic to ASIC2 [acid-sensing ionic channel 2, formerly named ACCN1]; odds ratio = 2.1, 95% confidence interval = 1.6 to 2.7, P = 3.9 × 10-8). This association persisted among subjects with severe periodontitis and was robust to adjustment for microbial plaque index. Moreover, the minor (G) allele was associated with higher levels of severe GI in stratified analyses among subsets of participants with high load of either “red” or “orange” complex pathogens, although this association was not statistically significant. While these results will require replication in independent samples and confirmation by mechanistic studies, this locus appears as a promising candidate for severe GI. Our findings suggest that genetic variation in ASIC2 is significantly associated with severe GI and that the association is plaque independent. Knowledge Transfer Statement: Persistent gingival inflammation reflected by bleeding usually precedes ongoing attachment loss or periodontal disease progression. Our findings suggest that genetic variation in ASIC2 that is associated with severe gingival inflammation might be used as a genetic marker to identify people at higher risk for periodontal disease. Ongoing studies to uncover the mechanistic link between ASIC2 and gingival inflammation could lead to novel therapeutic interventions.

An experimental murine model to study periodontitis

Periodontal disease (PD) is a common dental disease associated with the interaction between dysbiotic oral microbiota and host immunity. It is a prevalent disease, resulting in loss of gingival tissue, periodontal ligament, cementum and alveolar bone. PD is a major form of tooth loss in the adult population. Experimental animal models have enabled the study of PD pathogenesis and are used to test new therapeutic approaches for treating the disease. The ligature-induced periodontitis model has several advantages as compared with other models, including rapid disease induction, predictable bone loss and the capacity to study periodontal tissue and alveolar bone regeneration because the model is established within the periodontal apparatus. Although mice are the most convenient and versatile animal models used in research, ligature-induced periodontitis has been more frequently used in large animals. This is mostly due to the technical challenges involved in consistently placing ligatures around murine teeth. To reduce the technical challenge associated with the traditional ligature model, we previously developed a simplified method to easily install a bacterially retentive ligature between two molars for inducing periodontitis. In this protocol, we provide detailed instructions for placement of the ligature and demonstrate how the model can be used to evaluate gingival tissue inflammation and alveolar bone loss over a period of 18 d after ligature placement. This model can also be used on germ-free mice to investigate the role of human oral bacteria in periodontitis in vivo. In conclusion, this protocol enables the mechanistic study of the pathogenesis of periodontitis in vivo.In this protocol, a ligature is placed between mouse teeth. This induces gingival tissue inflammation and alveolar bone loss, resulting in a mouse model of periodontitis.