Biomarkers For Periodontitis Research Articles

Identification of potential diagnostic biomarkers associated with periodontitis by comprehensive bioinformatics analysis

Periodontitis is a chronic inflammatory disease that affects the tissues surrounding the teeth, including the gums and the bones supporting the teeth. Early detection and intervention are crucial for effective management of periodontitis. Our study aims to identify a diagnostic biomarker for periodontitis and explore the pathways associated with the occurrence and development of periodontitis. The expression of gingival tissue from periodontitis and healthy control were downloaded from the Gene Expression Omnibus. The weighted gene co-expression network analysis (WGCNA) were used to analyze module genes associated with periodontitis and DESeq2 were performed to identify differently expressed genes (DEGs) between periodontitis and healthy control. Then the candidate genes were obtained by intersecting the genes from interest modules and DEGs. Functional enrichment analysis was performed using gene ontology and kyoto encyclopedia of gene and genomes, followed by the protein–protein interaction (PPI) network analysis. The hub genes were identified by the cytoCNA plugin in Cytoscape. Finally, immunohistochemical staining of the hub genes was performed to validate the findings. WGCNA analysis found that the expression of the MEblack module was significantly higher in individuals with periodontitis compared to those in the healthy control group. A total of 888 DEGs, including 750 upregulated and 138 downregulated genes, were identified. Finally, 427 candidate genes were identified potentially associated with periodontitis after intersecting the DEGs and the black module genes. Several critical signaling pathways were identified associated with periodontitis by functional enrichment analysis, including cytokine–cytokine receptor interaction, neutrophil extracellular trap formation, Staphylococcus aureus infection, and Interleukin-17 signaling pathway. The PPI network analysis revealed that C-X-C motif chemokine ligand 5 (CXCL5) and C-X-C motif chemokine ligand 6 (CXCL6) could play an important role in the process of periodontitis. The gene expression level of CXCL5 and CXCL6 detected using immunohistochemical verified the findings. In conclusion, we found that CXCL5 and CXCL6 are closely associated with the occurrence of periodontitis. Our present pilot study suggests that CXCL5 and CXCL6 have the potential to be used as a diagnostic biomarker of periodontitis.

Expression dynamics of microRNA-223/Ras-associated binding protein 12 axis in Stage III/Grade B periodontal disease: A case-control analysis.

The intricate interplay between periodontal polymicrobial flora and an altered immune response is the central cause of periodontal disease. Multiple cell death methods and their interactions, along with the associated signaling pathways, significantly impact the initiation and advancement of periodontitis. Our speculation revolves around the role of the miR-223/Ras-associated binding protein (RAB12) signaling axis in regulating autophagy-induced pyroptosis, contributing to the pathophysiology of periodontitis. Thus, this study aimed to investigate miR-223 and RAB12 expression patterns in Stage III/Grade B periodontal disease. The study included 50 healthy individuals and 50 patients diagnosed with Stage III/Grade B periodontal disease. Clinical parameters were cataloged for each participant. miRNA-223 underwent an in silico analysis to identify its potential target genes. Gingival crevicular fluid (GCF) samples were collected from the subjects for real-time polymerase chain reaction to evaluate the expression of both miR-223 and the RAB12 gene. The miRTargetLink2.0 analysis highlighted the RAB12 gene as a prime target for miR-223. In periodontal disease patients, miR-223 and RAB12 gene expressions significantly increased (15.21 and 34.70-fold changes, respectively; P < 0.05). Receiver operating characteristic analysis suggested that miR-223 is a potential biomarker for periodontal disease, with 76% diagnostic accuracy and an area under the curve of 0.777 (P < 0.01). MicroRNA-223 and its target gene RAB12 exhibit high expression levels in GCF samples from individuals with periodontal disease. This suggests modulation of autophagy and the signaling mechanism for pyroptotic cell death in periodontal tissues during pathogenesis. Consequently, the miR-223/RAB12 axis might represent a plausible link for periodontal disease.

Cell-free DNA as a Novel Diagnostic Biomarker for Periodontitis

Periodontitis, a common inflammatory disease affecting the periodontal tissue, poses diagnostic and management challenges. Conventional methods are not capable of assessing disease activity or identifying individuals who are prone to progression. Biomarkers, measurable substances that indicate the presence, severity, or progression of disease, offer a promising approach to diagnosing periodontitis. Recent discoveries in molecular diagnostics have prompted the search for novel biomarkers, including cell-free DNA (cfDNA). This review article gives an insight into current knowledge regarding the use of cfDNA as a diagnostic marker in periodontal disease diagnosis.

Advances in periodontal biomarkers.

Due to technologic advancements, periodontology has witnessed a boost in biomarker research over the past three decades. Indeed, with the aid of omics, our understanding of the healthy periodontium, pathogenesis of periodontal diseases, and healing after periodontal treatment has improved significantly. Yet, the traditional methods, periodontal probing and radiographies, remain the most common methods to diagnose periodontal disease and monitor treatment. Although these approaches can produce reliable diagnostic outcomes, they generally detect disease only after significant tissue degradation thus making treatment outcome highly uncertain. Accordingly, laboratories worldwide have collaborated with clinicians to design accurate, rapid and cost-effective biomarkers for periodontal disease diagnosis. Despite these efforts, biomarkers that can be widely used in early disease diagnosis and for treatment outcome prediction are far from daily use. The aim of this chapter is to give a general overview on periodontal health and diseases, and review recent advancements in periodontal biomarker research. A second aim will discuss the strengths and limitations of translating periodontal biomarker research to clinical practice. Genetic biomarkers of periodontitis are not discussed as the available confirmatory data is scarce.

The salivary microbiome as a diagnostic biomarker of periodontitis: a 16S multi-batch study before and after the removal of batch effects.

Microbiome-based clinical applications that improve diagnosis related to oral health are of great interest to precision dentistry. Predictive studies on the salivary microbiome are scarce and of low methodological quality (low sample sizes, lack of biological heterogeneity, and absence of a validation process). None of them evaluates the impact of confounding factors as batch effects (BEs). This is the first 16S multi-batch study to analyze the salivary microbiome at the amplicon sequence variant (ASV) level in terms of differential abundance and machine learning models. This is done in periodontally healthy and periodontitis patients before and after removing BEs. Saliva was collected from 124 patients (50 healthy, 74 periodontitis) in our setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq. In parallel, searches were conducted on four databases to identify previous Illumina V3-V4 sequencing studies on the salivary microbiome. Investigations that met predefined criteria were included in the analysis, and the own and external sequences were processed using the same bioinformatics protocol. The statistical analysis was performed in the R-Bioconductor environment. The elimination of BEs reduced the number of ASVs with differential abundance between the groups by approximately one-third (Before=265; After=190). Before removing BEs, the model constructed using all study samples (796) comprised 16 ASVs (0.16%) and had an area under the curve (AUC) of 0.944, sensitivity of 90.73%, and specificity of 87.16%. The model built using two-thirds of the specimens (training=531) comprised 35 ASVs (0.36%) and had an AUC of 0.955, sensitivity of 86.54%, and specificity of 90.06% after being validated in the remaining one-third (test=265). After removing BEs, the models required more ASVs (all samples=200-2.03%; training=100-1.01%) to obtain slightly lower AUC (all=0.935; test=0.947), lower sensitivity (all=81.79%; test=78.85%), and similar specificity (all=91.51%; test=90.68%). The removal of BEs controls false positive ASVs in the differential abundance analysis. However, their elimination implies a significantly larger number of predictor taxa to achieve optimal performance, creating less robust classifiers. As all the provided models can accurately discriminate health from periodontitis, implying good/excellent sensitivities/specificities, the salivary microbiome demonstrates potential clinical applicability as a precision diagnostic tool for periodontitis.

Immunoaffinity-enriched salivary small extracellular vesicles in periodontitis

Aim: Saliva extracellular vesicles (EVs) serve as a significant reservoir of biomarkers that may be of clinical use in disease diagnosis. Saliva, however, contains EVs of both host- and bacterial- origin. Identifying suitable EVs for disease diagnosis involves enriching host EVs and limiting non-host contamination with effective isolation methods. The objectives of this research were: (1) to evaluate the salivary EVs enrichment in 12 periodontally healthy patients by two different methods: size exclusion chromatography (SEC) and bead-based immunoaffinity capture (EXO-NET®); (2) to analyze the variance expression of inflammatory cytokines in EXO-NET-enriched EVs, comparing individuals with periodontitis (n = 20) to non-periodontitis (n = 12). Methods: Whole unstimulated saliva samples were collected from 12 periodontally healthy and 20 periodontitis patients. EVs were isolated from the 12 non-periodontitis patients using SEC (referred to as SEC-EVs) and EXO-NET (referred to as EXO-NET EVs), after which their total protein content, 37 EV surface markers, and bacterial pathogens expression were compared. Subsequently, the inflammatory cytokines expression levels (interleukin-IL-6, IL-1β, IL-8, and IL-10) in EXO-NET EVs were measured for non-periodontitis and periodontitis. Results: EXO-NET EVs contained more EV-specific protein and substantially higher expression of EV surface markers (CD9, CD81, CD63), but less pathogenic DNA was detected compared to that in SEC-EVs. Additionally, EXO-NET EVs from periodontitis patients contained higher amounts of IL-6 and IL-8, and decreased IL-10, compared to those from non-periodontitis patients. Conclusion: The findings suggest that immunoaffinity capture (EXO-NET) is a dependable method for salivary EVs enrichment, resulting in a higher yield of host EVs with reduced bacterial DNA detection compared to SEC. Furthermore, the research proposes that immunoaffinity capture enriched EVs can function as biomarkers for periodontitis, demonstrated by an increased expression of proinflammatory cytokines from periodontitis patients.

Gingival crevicular fluid levels of apelin correlates with clinical periodontal diagnosis.

Limitations of clinical periodontal measurements have led to the search for reliable biomarkers that can be used in diagnosis and monitoring of periodontal diseases. Considering the relationship of adipokines with periodontal disease, diabetes, and obesity, apelin may be a biomarker for periodontal diseases due to its modulating effects on inflammation. The present study was conducted to determine gingival crevicular fluid (GCF) apelin levels in systemically healthy individuals and to evaluate the potential of apelin as a biomarker for periodontal diagnosis. Ten individuals with clinically healthy periodontal tissues, 10 patients diagnosed with gingivitis, and 10 patients with periodontitis were included in the present study. Whole mouth clinical periodontal measurements were recorded and GCF samples were obtained from the buccal approximal regions of single-rooted teeth with features that would represent clinical periodontal diagnosis. Apelin level in the samples was determined by ELISA. Clinical and biochemical findings were statistically analyzed. Possible relationship between the variables was evaluated with Pearson correlation analysis. Apelin level in the gingivitis group was higher than that in the clinically healthy group (p = 0.000) and lower than that in the periodontitis group (p = 0.000). A positive correlation was found between GCF apelin concentration and plaque score, bleeding on probing, and probing depth (p = 0.000). Within the limits of this study, it can be suggested that GCF apelin concentration may be a biomarker that can distinguish between healthy periodontal tissues, gingivitis, and periodontitis patients. Apelin concentration in the gingival crevicular fluid may aid in the diagnosis of periodontal disease.

The evaluation of salivary leucine-rich alpha-2 glycoprotein (LRG) and C-reactive protein (CRP) in humans with periodontal health or periodontal disease.

The purpose of the present research is to evaluate the salivary levels of leucine-rich alpha-2 glycoprotein (LRG) and C-reactive protein (CRP) in periodontal health and disease (gingivitis and stage III periodontitis) and also to compare the discriminative efficiencies of both biomarkers in periodontal disease. LRG is a new acute-phase protein whose functions are still being investigated. LRG and CRP are both biomarkers that are increased by inflammation. No clinical study has yet investigated the comparison of the level of LRG and CRP in periodontal health, gingivitis and periodontitis in saliva samples. A total of 60 individuals, including 20 periodontally healthy (control group/group C), 20 with gingivitis (group G), and 20 with Stage III periodontitis (group P), who were systemically healthy and non-smokers, participated in this study. Periodontal charts were used for recording clinical periodontal parameters and saliva LRG and CRP levels were measured by ELISA. Analyzing the area under the curve (AUC) was performed by the receiver-operating characteristics curve. Salivary levels of LRG and CRP were significantly higher in disease groups than in group C (p < .05). Positive statistically significant correlations were observed between both biomarkers and clinical parameters (p < .05). There was also a strong positive correlation between two biomarkers (p < .05). In distinguishing periodontal disease from periodontal health, LRG (AUC = 0.833) and CRP (AUC = 0.826) were found to have similar accuracy (p = .923). LRG and CRP may be useful and similarly effective biomarkers in the diagnosis of periodontal diseases based on the findings of this study.

MicroRNAs in Gingival Crevicular Fluid: An Observational Case-Control Study of Differential Expression in Periodontitis.

microRNAs (miRNAs) present in the gingival crevicular fluid (GCF) of patients with chronic periodontitis may serve as biomarkers of periodontal disease. The aim of this study was to perform a miRNA-sequencing study of all miRNAs present in GCF, comparing miRNA expression level profiles between advanced chronic periodontitis (CP) patients and healthy subjects (HS). GCF samples were collected from the single-rooted teeth of patients with severe CP (n = 11) and of HS (n = 12). miRNAs were isolated from GCF using an miRNeasy Serum/Plasma kit(Qiagen GmbH, Hilden, Germany). Reverse transcription polymerase chain reaction (qRT-PCR) was used to determine the expression levels of miRNA candidates involved in periodontal pathogenesis. Of all the sequenced miRNAs, miR-199, miR-146a, miR-30a, and miR-338 were identified as best representing the CP patient samples. The validation study identified miR-199 as the most powerful biomarker used to define periodontitis. Upon sequencing all known miRNAs in GCF for the first time, we uncovered several potential biomarkers to define periodontitis. Identifying miRNAS in the GCF using high-throughput approaches will clarify the role of these molecules in periodontitis and provide biomarkers with potential applications.

MicroRNA‐155 (miR-155) as an accurate biomarker of periodontal status and coronary heart disease severity: a case–control study

BackgroundIncreasing evidence supports associations between periodontal disease and coronary heart disease (CHD). This case–control study evaluated whether inflammatory regulator, microRNA-155 (miR-155), could be utilised as a biomarker of periodontitis and/or CHD.MethodsOf 120 participants, 30 patients had clinically healthy periodontium (controls, C), 30 patients had generalized periodontitis (P), 30 patients had CHD and clinically healthy periodontium (AS-C); and 30 patients had CHD with generalized periodontitis (AS-P). Patient demographic and periodontal characteristics (plaque index, bleeding on probing, probing pocket depth and clinical attachment loss), were collected. Patient whole blood and saliva levels of miR-155 and pro-inflammatory cytokine (interleukin-1β), were quantified by quantitative real time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA). One-way ANOVA with post-hoc Tukey test was used to determine differences among the four groups. Chi Square test was used for participant gender comparisons. Pearson correlation tests and multiple linear regression analyses were used to assess associations between the demographic and clinical variables analysed, versus IL-1β and miR-155 levels. miR-155 and IL-1β accuracy in differentiating healthy versus other patient groups were analysed using receiver operating characteristic (ROC) curves, by calculating area under the curve (AUC) values and sensitivity and specificity cut-off points using Youden’s index. Statistical tests of sensitivity and specificity were conducted using the McNemar test.ResultsWhole blood miR-155 levels were elevated in periodontitis/non-periodontitis patients with CHD (AS-P, AS-C), and periodontitis patients alone (P) (p < 0.001). Receiver operating characteristic (ROC) and area under the curve (AUC) analyses confirmed miR-155 accuracy in discriminating P, AS-C and AS-P groups (AUC 0.6861–0.9944, p < 0.0001–0.05), coupled with high sensitivity (76.7–100.0%), specificity (53.3–96.7%) and cut-off points (> 0.955- > 2.915 a.u.; p < 0.0001). miR-155 levels further distinguished between CHD (AS-C, AS-P) and periodontitis (P) patients (AUC ≥ 0.8378, sensitivity ≥ 88.7%, specificity ≥ 73.3%, cut-off > 2.82 a.u; p < 0.0001), and between AS-C and AS-P patients (AUC 0.7578, sensitivity 80.0%, specificity 50.0%, cut-off > 7.065 a.u; p < 0.001). Subsequent analyses identified positive correlations between miR-155 and the various patient demographics, salivary interleukin-1β and periodontal parameters assessed.ConclusionsThis study advocates miR-155 as an accurate diagnostic/prognostic biomarker of periodontitis and/or CHD severity, thereby improving detection and treatment for both conditions.

Periodontal disease and visfatin level: A systematic review and meta-analysis.

Visfatin is considered an inflammatory biomarker in periodontal disease (PD). In this meta-analysis, we aimed to evaluate the relationship between Visfatin biomarker level with PD. In this study, Medline, Scopus, Web of Science, and Google Scholar were searched. We included studies that examined visfatin levels in samples from healthy people and periodontal disease until March 2023. The quality of the selected articles was evaluated using the Newcastle-Ottawa assessment scale. Depending on heterogeneity of studies, random-effects or fixed-effect models were used to pool results and report the standardized mean difference (SMD). After screening the retrieved papers, the related data were extracted. A total of 159 studies were identified, and 16 studies were included in the meta-analysis. In 9 studies, the SMD of visfatin level of gingival crevicular fluid (GCF) in patients with chronic periodontitis (CP) and healthy individuals was 4.32 (p<0.001). In 6 studies, the SMD of salivary visfatin level in patients with CP and healthy individuals was 2.95 (p = 0.004). In addition, in five studies, the SMD of serum visfatin level in patients with CP and healthy individuals was 7.87 (p<0.001). Therefore, Visfatin levels in serum, saliva, and GCF of patients with CP were increased in comparison to healthy individuals. Comparison of visfatin levels in saliva of gingivitis patients and healthy individuals showed a significant increase of visfatin in gingivitis patients (SMD:0.57, P = 0.018), but no significant difference was observed in the mean GCF visfatin level of gingivitis patients and healthy individuals (SMD:2.60, P = 0.090). In addition, the results suggested that there is no difference between gingivitis cases compared to CP patients (SMD:3.59, P = 0.217). Visfatin levels in GCF, serum, and saliva have the potential to be used as a diagnostic biomarker of periodontitis.

A disposable immunosensor for the detection of salivary MMP-8 as biomarker of periodontitis

This work describes the development of a novel voltammetric immunosensor for the detection of salivary MMP-8 at the point-of-care. The electrochemical platform was based on a graphene (GPH) screen-printed electrode (SPE) functionalized by gold-nanospheres (AuNSs) and antibodies against MMP-8 protein (anti-MMP-8). The functionalization with anti-MMP-8 was realized by using 11-mercaptoundecanoic acid (11-MUA), thanks to its ability to give strong sulfur bonds with its –SH end, and to cross-link the –NH2 groups of the antibody molecule with the other –COOH end, using the traditional EDC-NHS method. The voltammetric sensor showed good performances with a linear range of 2.5–300 ng mL−1, a LOD value of 1.0 ± 0.1 ng mL−1 and a sensitivity of 0.05 µA mL cm−2 ng−1. Moreover, the proposed immunosensor was tested in real saliva samples, showing comparable results to those obtained with the conventional ELISA method. The biosensor was single-use and cost-effective and required a small quantity of test medium and a short preparation time, representing a very attractive biosensor for MMP-8 detection in human saliva.

Identification of biomarkers related to prognosis and diagnosis of periodontitis by bioinformatics based on public database.

Periodontitis is a multifactorial disease that has a negative impact on people's life. However, studies on potential key genes with excellent diagnostic value for periodontitis disease have not been systematically explored. GSE10334 data set was downloaded from the Gene Expression Omnibus database. Following the gene expression profiles were normalized by the Robust multi-array average (RMA) algorithm, the differentially expressed genes were screened and incorporated into Weight gene correlation network analysis to obtain hub genes. Receiver-operating characteristic curve analysis was used to verify the validity and agility of the hub genes-based least absolute shrinkage and selection operator model. Furthermore, we validated the expression of these hub genes by real-time polymerase chain reaction and western blotting. Eight hub genes were identified and had good diagnostic values. Besides, the upregulations of eight hub genes were verified both in protein and mRNA levels in clinical periodontitis gum tissue. We discovered potential biomarkers in periodontitis based on the public database and these biomarkers focused on several immune responses and inflammatory pathways. Thus, this study may provide potential therapeutic targets for early diagnosis and treatment of periodontitis.

Mass Spectrometry-Based Proteomics for Discovering Salivary Biomarkers in Periodontitis: A Systematic Review.

Periodontitis is one of the primary causes of tooth loss, and is also related to various systemic diseases. Early detection of this condition is crucial when it comes to preventing further oral damage and the associated health complications. This study offers a systematic review of the literature published up to April 2023, and aims to clearly explain the role of proteomics in identifying salivary biomarkers for periodontitis. Comprehensive searches were conducted on PubMed and Web of Science to shortlist pertinent studies. The inclusion criterion was those that reported on mass spectrometry-driven proteomic analyses of saliva samples from periodontitis cohorts, while those on gingivitis or other oral diseases were excluded. An assessment for risk of bias was carried out using the Newcastle-Ottawa Scale and Quality Assessment of Diagnostic Accuracy Studies or the NIH quality assessment tool, and a meta-analysis was performed for replicable candidate biomarkers, i.e., consistently reported candidate biomarkers (in specific saliva samples, and periodontitis subgroups, reported in ≥2 independent cohorts/reports) were identified. A Gene Ontology enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery bioinformatics resources, which consistently expressed candidate biomarkers, to explore the predominant pathway wherein salivary biomarkers consistently manifested. Of the 15 studies included, 13 were case-control studies targeting diagnostic biomarkers for periodontitis participants (periodontally healthy/diseased, n = 342/432), while two focused on biomarkers responsive to periodontal treatment (n = 26 participants). The case-control studies were considered to have a low risk of bias, while the periodontitis treatment studies were deemed fair. Summary estimate and confidence/credible interval, etc. determination for the identified putative salivary biomarkers could not be ascertained due to the low number of studies in each case. The results from the included case-control studies identified nine consistently expressed candidate biomarkers (from nine studies with 230/297 periodontally healthy/diseased participants): (i) those that were upregulated: alpha-amylase, serum albumin, complement C3, neutrophil defensin, profilin-1, and S100-P; and (ii) those that were downregulated: carbonic anhydrase 6, immunoglobulin J chain, and lactoferrin. All putative biomarkers exhibited consistent regulation patterns. The implications of the current putative marker proteins identified were reviewed, with a focus on their potential roles in periodontitis diagnosis and pathogenesis, and as putative therapeutic targets. Although in its early stages, mass spectrometry-based salivary periodontal disease biomarker proteomics detection appeared promising. More mass spectrometry-based proteomics studies, with or without the aid of already available clinical biochemical approaches, are warranted to aid the discovery, identification, and validation of periodontal health/disease indicator molecule(s). Protocol registration number: CRD42023447722; supported by RD-02-202410 and GRF17119917.

Estimating the salivary levels of IL-35 in smokers with periodontitis: A cross sectional study

BackgroundA refined network and integrated host immune response to bacteria are formed by anti-inflammatory cytokines and the cells that they are associated to IL-35 has been recognized as having strong suppressive activity in chronic inflammatory diseases, together with IL-10 and TGF-β. It is believed that inflammatory diseases like periodontitis trigger the inducible Treg population to express IL-35, expanding regulatory responses by increasing infection. AimThe objective is to estimate and compare the salivary IL-35 levels in Periodontally healthy subjects, smokers and non-smokers with Periodontitis in order to educate on the consequences of periodontal health among the diseased patients. Materials and MethodsTotally 42 subjects were included and they were categorized into Group 1 (n = 14) as Periodontally healthy subjects, Group 2 (n = 14) as systemically healthy non-Smokers with periodontitis and Group 3 (n = 14) as systemically healthy smokers with periodontitis. Each subject was assessed for clinical parameters such as Plaque index, Gingival index, Probing depth, clinical attachment. A polypropylene tube was used to collect unstimulated saliva and centrifuged it at 800 × g for 10 min. Supernatants were collected and stored at −80◦C. A commercially available enzyme-linked immunosorbent assay kit was used to analyse levels of human salivary IL-35. ResultsThe average age of the subjects in Group 1, Group 2 and Group 3 were 50.53, 52.93 and 52.07 years respectively. All three groups showed a statistically significant difference in clinical parameters including Plaque index, Gingival index, Probing depth and clinical attachment. The salivary IL-35 level was found to be elevated in non-smokers who have periodontitis compared to smokers with periodontitis and healthy individuals. Despite this, the salivary IL-35 levels were found to be statistically significant among three groups at P < 0.001. ConclusionThe salivary levels of IL-35 were found to increase in Periodontitis patients with/without smoking, along with increased clinical parameters. IL-35 is considered a influential biomarker for periodontal disease.

Cystatin SN (CST1) as a Novel Salivary Biomarker of Periodontitis.

Identification of biomarkers could help in assessing periodontal health status and monitoring treatment outcomes. Therefore, the aim of this cross-sectional study was to identify potential innovative salivary biomarkers for the diagnosis of periodontitis using an untargeted proteomic approach. Forty-five healthy non-smoker participants diagnosed as having periodontally healthy conditions (H), severe periodontitis (P), and healthy but reduced periodontium after active periodontal treatment (T) were consecutively enrolled (15 per each group) in the study. A higher number of spots were identified in the proteome of unstimulated whole saliva collected from H and T subjects compared with P group, mainly within the range of 8-40 kDa. Protein spots of interest were analysed by MALDI-TOF-MS, allowing the identification of cystatin SN (CST1) isoform, as confirmed by Western blot. CST1 was markedly expressed in the H group, while it was absent in most P samples (p < 0.001). Interestingly, a distinct CST1 expression was observed in saliva from T patients. CST1 was negatively correlated with the percentage of pathological sites (p < 0.001) and was effective in discriminating active periodontitis from healthy periodontal status (whether H or T). Therefore, salivary CST1 may be a promising non-invasive biomarker for periodontal disease diagnosis and monitoring.

The role of salivary galectin-3 and galectin-9 levels in plaque-induced gingivitis and periodontitis

BackgroundThis study aimed to compare the salivary galectin-3 and galectin-9 levels in periodontitis, gingivitis, and periodontally healthy patients. MethodsThis study included 75 non-smokers who were systemically healthy. The clinical periodontal parameters of each participant were recorded. Individuals with periodontal health, gingivitis, and Stage II or Stage III Grade B periodontitis were allocated to the corresponding study groups (n = 25 each). Saliva samples were obtained from all individuals after they abstained from drinking and eating 1 h before sample collection. The galectin-3 and galectin-9 levels in the saliva were analyzed using enzyme-linked immunosorbent assay. One-way analysis of variance, student's t-test, Spearman correlation, and logistic regression were used for statistical analyses. ResultsThe galectin-3 and galectin-9 levels were significantly higher in the periodontitis and gingivitis groups than in the healthy group (p < 0.001). The highest galectin-3 and galectin-9 levels were observed in the gingivitis group (p < 0.05). Overall, the galectin-3 levels were significantly higher than the galectin-9 levels in all the groups (p < 0.001). ConclusionsThe salivary galectin-3 and galectin-9 levels were high in patients with periodontitis and gingivitis, suggesting that they could be potential biomarkers for periodontal diseases.

Does smoking influence tryptophan metabolism in periodontal inflammation? A cross-sectional study.

The aim of this study was to identify the effects of smoking and periodontal inflammation on tryptophan-kynurenine metabolism as well as the correlation between these findings and clinical periodontal parameters. It has been shown that the tryptophan amino acid's primary catabolic pathway, the kynurenine pathway (KP), may serve as a key biomarker for periodontal disease. Although there are studies investigating the effect of smoking on KYN-TRP metabolism, the effect of smoking on periodontal disease through KP has not been revealed so far. The salivary and serum samples were gathered from 24 nonsmoker (NS-P) stage III, grade B generalized periodontitis and 22 smoker (S-P) stage III, grade C generalized periodontitis patients, in addition to 24 nonsmoker (NS-C) and 24 smoker (S-C) periodontally healthy control individuals. Saliva and serum IL-6, kynurenine (KYN), and tryptophan (TRP) values, and KYN/TRP ratio were analyzed by liquid chromatography-mass spectrometry. Clinical periodontal measurements were recorded. Salivary TRP values were significantly higher in both periodontitis groups than control groups (p < .05). Salivary KYN values were highest in NS-P group (p < .05). Salivary KYN values did not differ significantly between periodontitis groups (p = .84). Salivary KYN/TRP ratio was significantly lower in NS-P group compared to other groups (p < .001). Serum TRP value is higher in S-P group than other groups; however, significant difference was found in S-C group (p < .05). Serum KYN values were significantly lower in smokers than nonsmokers. Serum KYN/TRP ratio is higher in NS-P group. NS-P group has the highest salivary IL-6 levels, NS-C group has the lowest values (p < .05). Our results point out that smoking exacerbates inflammation in the periodontium and increases TRP destruction and decreases IDO activity by suppressing KP in serum. As a result, kynurenine and its metabolites may be significant biomarkers in the link between smoking and periodontal disease.

Association between gingival bleeding and hematuria as biomarkers of periodontitis and renal disease: a review.

Gingival bleeding is a common complaint and symptom in patients with periodontitis. In clinics, gingival bleeding is regarded as an important sign of gingival inflammation, which is also of great significance in predicting the activity of periodontitis. Existing research has indicated that periodontitis has an impact on distant sites, such as the kidney. Hematuria is the principal feature of glomerular disease, which can reflect the degree and condition of glomerular inflammation. Previous studies have revealed an association between periodontal diseases with renal diseases, so a study is necessary to discuss their representative signs of them. For the moment, there are no reports that are concerned about the correlation between gingival bleeding with hematuria. The main point of this text is to review the potential association between gingival bleeding with hematuria, reveal their underlying mechanisms, and provide instructions for the therapy of periodontitis and glomerular diseases.

Complement components C3b and C4b as potential reliable site-specific diagnostic biomarkers for periodontitis.

This study aimed to investigate the correlation between the expression levels of C3b and C4b in human gingival tissue (GT) and gingival crevicular fluid (GCF) and disease severity in human periodontitis and to determine whether C3b and C4b are significant site-specific complementary diagnostic markers for periodontitis. A variety of biomarkers that have potential for informing diagnoses of periodontitis have been proposed. The complement components C3b and C4b were found to be positively correlated with disease severity. The therapeutic effect of targeting C3b and C4b on inflammatory bone loss in experimental periodontitis models has been studied. However, studies on the diagnostic potential of the gingival C3b and C4b expression levels for periodontitis are scarce. The expression levels of C3b and C4b in the GT and GCF were investigated via immunohistochemistry and enzyme-linked immunosorbent assay, respectively. The correlation between the expression levels of C3b and C4b and disease severity with probing depth as well as the clinical attachment level were determined. To evaluate the diagnostic accuracy of the C3b and C4b expression levels at the periodontitis sites, the receiver operating characteristic (ROC) curve, cut-off point, area under the ROC curve, sensitivity, and specificity were analyzed. The expression levels of C3b and C4b in human GT and GCF were significantly positively correlated with periodontitis severity. The expression levels of combined C3b + C4b in the GT can significantly differentiate the disease status at the tissue level (p < .0001). Similarly, the expression levels of C3b + C4b in GCF can statistically distinguish periodontitis sites from healthy ones (p < .0001). Locally deposited C3b and C4b were positively correlated with periodontitis severity and recognized as site-specific diagnostic biomarkers for clinicopathological features in periodontitis. The association between the C3b and C4b network and periodontitis may be further understood and provide a basis for the development of novel screening as well as diagnostic and therapeutic strategies for periodontitis.