Salivary biomarker combination prediction model for the diagnosis of periodontitis in a Taiwanese population

Recent advances in nanomaterial-based biosensor for periodontitis detection

Periodontitis, a chronic inflammatory condition caused by bacteria, often causes gradual destruction of the components that support teeth, such as the alveolar bone, cementum, periodontal ligament, and gingiva. This ultimately results in teeth becoming loose and eventually falling out. Timely identification has a crucial role in preventing and controlling its progression. Clinical measures are used to diagnose periodontitis. However, now, there is a hunt for alternative diagnostic and monitoring methods due to the progress of technology. Various biomarkers have been assessed using multiple bodily fluids as sample sources. Furthermore, conventional periodontal categorization factors do not provide significant insights into the present disease activity, severity and amount of tissue damage, future development, and responsiveness to treatment. In recent times, there has been a growing utilization of nanoparticle (NP)-based detection strategies to create quick and efficient detection assays. Every single one of these platforms leverages the distinct characteristics of NPs to identify periodontitis. Plasmonic NPs include metal NPs, quantum dots (QDs), carbon base NPs, and nanozymes, exceptionally potent light absorbers and scatterers. These find application in labeling, surface-enhanced spectroscopy, and color-changing sensors. Fluorescent NPs function as photostable and sensitive instruments capable of labeling various biological targets. This article presents a comprehensive summary of the latest developments in the effective utilization of various NPs to detect periodontitis.

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.

Accuracy of periodontitis diagnosis obtained using multiple molecular biomarkers in oral fluids: A systematic review and meta-analysis

AIM

To determine the accuracy of biomarker combinations in gingival crevicular fluid (GCF) and saliva through meta-analysis to diagnose periodontitis in systemically healthy subjects.

METHODS

Studies on combining two or more biomarkers providing a binary classification table, sensitivity/specificity values or group sizes in subjects diagnosed with periodontitis were included. The search was performed in August 2022 through PUBMED, EMBASE, Cochrane, LILACS, SCOPUS and Web of Science. The methodological quality of the articles selected was evaluated using the QUADAS-2 checklist. Hierarchical summary receiver operating characteristic modelling was employed to perform the meta-analyses (CRD42020175021).

RESULTS

Twenty-one combinations in GCF and 47 in saliva were evaluated. Meta-analyses were possible for six salivary combinations (median sensitivity/specificity values): IL-6 with MMP-8 (86.2%/80.5%); IL-1β with IL-6 (83.0%/83.7%); IL-1β with MMP-8 (82.7%/80.8%); MIP-1α with MMP-8 (71.0%/75.6%); IL-1β, IL-6 and MMP-8 (81.8%/84.3%); and IL-1β, IL-6, MIP-1α and MMP-8 (76.6%/79.7%).

CONCLUSIONS

Two-biomarker combinations in oral fluids show high diagnostic accuracy for periodontitis, which is not substantially improved by incorporating more biomarkers. In saliva, the dual combinations of IL-1β, IL-6 and MMP-8 have an excellent ability to detect periodontitis and a good capacity to detect non-periodontitis. Because of the limited number of biomarker combinations evaluated, further research is required to corroborate these observations.

Saliva - a new opportunity for fluid biopsy

Saliva is a complex biological fluid with a variety of biomolecules, such as DNA, RNA, proteins, metabolites and microbiota, which can be used for the screening and diagnosis of many diseases. In addition, saliva has the characteristics of simple collection, non-invasive and convenient storage, which gives it the potential to replace blood as a new main body of fluid biopsy, and it is an excellent biological diagnostic fluid. This review integrates recent studies and summarizes the research contents of salivaomics and the research progress of saliva in early diagnosis of oral and systemic diseases. This review aims to explore the value and prospect of saliva diagnosis in clinical application.

Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis

Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN). Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis. Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model's accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function. Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.

Next-Generation Examination, Diagnosis, and Personalized Medicine in Periodontal Disease

Periodontal disease, a major cause of tooth loss, is an infectious disease caused by bacteria with the additional aspect of being a noncommunicable disease closely related to lifestyle. Tissue destruction based on chronic inflammation is influenced by host and environmental factors. The treatment of periodontal disease varies according to the condition of each individual patient. Although guidelines provide standardized treatment, optimization is difficult because of the wide range of treatment options and variations in the ideas and skills of the treating practitioner. The new medical concepts of “precision medicine” and “personalized medicine” can provide more predictive treatment than conventional methods by stratifying patients in detail and prescribing treatment methods accordingly. This requires a new diagnostic system that integrates information on individual patient backgrounds (biomarkers, genetics, environment, and lifestyle) with conventional medical examination information. Currently, various biomarkers and other new examination indices are being investigated, and studies on periodontal disease-related genes and the complexity of oral bacteria are underway. This review discusses the possibilities and future challenges of precision periodontics and describes the new generation of laboratory methods and advanced periodontal disease treatment approaches as the basis for this new field.

Validity of a combination of periodontal pathogens and salivary biomarkers as predictors of periodontitis

OBJECTIVE

Chronic periodontitis is caused by multiple risk factors. To predict chronic periodontitis in older people, we evaluated the association between a combination of major periodontal pathogens and salivary biomarkers and the presence of periodontitis.

METHODS

Stimulated saliva samples were collected to analyze the prevalence of Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Prevotella intermedia, as well as four biomarkers: interleukin (IL)-1β, IL-6, tumor necrosis factor-α (TNF-α), and prostaglandin E2 (PGE2). A total of 201 Japanese patients were recruited. Oral examinations ware performed to determine chronic periodontitis as measured by Community Periodontal Index. The sociodemographic and behavioral characteristics were also obtained, and the parameters were adjusted as potential confounders to employ statistical models.

RESULTS

The odds ratio (OR) for the presence of P. gingivalis and the third tertile level of IL-1β as compared with the absence of P. gingivalis and the lowest tertile of IL-1β was highest in individuals with periodontitis (OR = 13.98; 95% confidence interval [CI] 3.87-50.52) with the best level (0.79) of area under the curve (AUC) based on the receiver operating characteristic curve. The OR for the presence of P. gingivalis and the third tertile of PGE2 was 7.76 (CI 1.89-31.91) with an AUC of 0.78. The coexistence of more than two periodontal bacteria and the third tertile of PGE2 was also strongly associated with chronic periodontitis (OR = 9.23, 95% CI 2.38-35.79) with an AUC of 0.76.

CONCLUSIONS

The combined information of the presence of P. gingivalis in stimulated saliva, and higher levels of salivary IL-1β may play a vital role in the detection and prediction of chronic periodontitis in older adults.

Identification of nine signature proteins involved in periodontitis by integrated analysis of TMT proteomics and transcriptomics

Recently, there are many researches on signature molecules of periodontitis derived from different periodontal tissues to determine the disease occurrence and development, and deepen the understanding of this complex disease. Among them, a variety of omics techniques have been utilized to analyze periodontitis pathology and progression. However, few accurate signature molecules are known and available. Herein, we aimed to screened and identified signature molecules suitable for distinguishing periodontitis patients using machine learning models by integrated analysis of TMT proteomics and transcriptomics with the purpose of finding novel prediction or diagnosis targets. Differential protein profiles, functional enrichment analysis, and protein–protein interaction network analysis were conducted based on TMT proteomics of 15 gingival tissues from healthy and periodontitis patients. DEPs correlating with periodontitis were screened using LASSO regression. We constructed a new diagnostic model using an artificial neural network (ANN) and verified its efficacy based on periodontitis transcriptomics datasets (GSE10334 and GSE16134). Western blotting validated expression levels of hub DEPs. TMT proteomics revealed 5658 proteins and 115 DEPs, and the 115 DEPs are closely related to inflammation and immune activity. Nine hub DEPs were screened by LASSO, and the ANN model distinguished healthy from periodontitis patients. The model showed satisfactory classification ability for both training (AUC=0.972) and validation (AUC=0.881) cohorts by ROC analysis. Expression levels of the 9 hub DEPs were validated and consistent with TMT proteomics quantitation. Our work reveals that nine hub DEPs in gingival tissues are closely related to the occurrence and progression of periodontitis and are potential signature molecules involved in periodontitis.

Salivary Biomarkers in Periodontitis Patients: A Pilot Study

Background and Objective. Periodontal disease is a chronic inflammatory condition, involving the destruction of both soft and hard tissues in the periodontal region. Advances in diagnostic research are moving toward methods whereby the periodontal risk can be identified and quantified by objective measures using specific inflammatory markers that can be correlated to the severity of the disease. The purpose of this study was to evaluate salivary concentrations of IL-1β, IL-6, MMP-8, and IL-10 in healthy and periodontitis patients and to assess the association between these biomarkers levels and clinical parameters in a Moroccan population. Materials and Methods. Forty subjects were included: 10 periodontally healthy subjects and 30 periodontitis patients. Periodontal examination and saliva sampling were performed in all patients. Levels of salivary cytokines including IL-1β, IL-6, MMP-8, and IL-10 were evaluated by a sandwich ELISA test kit. Data were analyzed by SPSS for Windows. Results. Regarding individual biomarkers, IL-1β, IL-6, and MMP-8 levels were significantly higher in periodontitis patients ( $p\le 0.001$ , $p<\text{0.}05$ , respectively). The concentration of these proteins in saliva showed a significant association with gingival index and pocket depth measurements and may reflect the clinical status of healthy and diseased periodontium. However, no significant differences were observed for the IL-10 component. Conclusion. IL-1β and IL-6 concentrations were statistically higher in periodontitis patients and may be used as potential tools in periodontitis diagnosis.

Impact of the Glycemic Level on the Salivary Proteome of Middle-Aged and Elderly People With Type 2 Diabetes Mellitus: An Observational Study

Type 2 diabetes mellitus (T2DM) is an increasing global public health concern, but its impact on the salivary proteome is still unclear. To evaluate the effect of glycemic levels in middle-aged and elderly individuals with T2DM on salivary proteomics, we compared the differences by liquid chromatography tandem mass spectrometry (LC–MS/MS). Unstimulated whole saliva samples from 8 T2DM patients with good glycemic control (G group, HbA1c <6.5%) and 16 patients with poor control (P group, HbA1c ≥6.5%) were analyzed by LC–MS/MS in the data-independent acquisition mode (Clinical register number: ChiCTR1900023582.). After functional annotation, cluster analysis and receiver operating characteristic (ROC) curve analysis were carried out to screen and evaluate candidate proteins. A total of 5,721 proteins were quantified, while 40 proteins differed significantly. In the P group, proteins involved in oxidative stress-related processes were upregulated, whereas proteins related to salivary secretion were downregulated. The combination of thioredoxin domain-containing protein 17, zymogen granule protein 16B, and FAM3 metabolism regulating signaling molecule D yielded an area under the curve of 0.917 which showed a robust ability to distinguish the P and G groups. In conclusion, poorly controlled hyperglycemia may affect salivary proteins through various pathways, including oxidative stress and glandular secretion. Furthermore, the differentially expressed proteins, especially the three proteins with the best differentiation, might serve as an anchor point for the further study of hyperglycemia and oral diseases.

Clinical utility of subgingival plaque-specific bacteria in salivary microbiota for detecting periodontitis

Saliva contains diverse bacteria shed from various oral sites, including subgingival plaque. It is reasonable to focus on the total occupancy of subgingival plaque-specific bacteria (SUBP bacteria), which live in subgingival environments, in the saliva for detecting periodontitis using salivary testing. This study aimed to validate the clinical utility of SUBP bacteria in the salivary microbiota for the detection of periodontitis. We examined stimulated saliva samples collected from 125 subjects who visited three dental clinics. The relative abundances of previously identified 11 SUBP bacteria were determined using 16S ribosomal RNA gene sequencing and a reference-based approach. The prediction performance was evaluated using a receiver operating characteristic (ROC) curve. The SUBP bacteria accounted for 0-15.4% of the salivary microbiota, and the percentage distinguished periodontitis patients with at least 15 sites with probing depth ≥4 mm with a sensitivity of 0.90 (95% confidence interval [CI], 0.81-0.98) and specificity of 0.70 (95% CI, 0.60-0.80) (area under the ROC curve [AUC], 0.87). Among 2,047 combinations of 11 SUBP bacteria, combinations including Streptococcus constellatus, Porphyromonas gingivalis, and Fusobacterium nucleatum subsp. vincentii demonstrated significantly higher AUC values in their detection. These results suggest that examining SUBP bacteria in saliva may be useful for detecting periodontitis patients in mass screening.

Evaluation of the Relationship between Salivary Lipids, Proteins and Total Antioxidant Capacity with Gingival Health Status in Type-1 Diabetic Children

STATEMENT OF THE PROBLEM

Alteration in salivary composition and its effect on the oral cavity in diabetic child patients remains equivocal.

PURPOSE

This study was performed to assess the relationship between salivary factors and gingival status in children with type-1 diabetes mellitus (DM).

MATERIAL AND METHOD

In this cross-sectional study, 120 subjects aged 6-16 years (60 well-controlled and poorly-controlled diabetics and 60 healthy individuals) were examined to determine the gingival index (GI) and plaque index (PI). The unstimulated saliva samples were collected to measure the salivary triglyceride, cholesterol, albumin, α-amylase, total protein levels by the laboratory kits. Total antioxidant capacity and the free radicals scavenger index were measured by the Ferric Reducing Ability Of Plasma (FRAP) and 1,1-Diphenyl-2-picryl-hydrazyl (DPPH) assays, respectively. Data were analyzed by parametric and non-parametric, Pearson correlation, and t tests at a 5% error level.

RESULTS

GI of diabetics was significantly higher than that of healthy individuals (1.51± 0.71 and 0.9±0.81, respectively, p< 0.001). No significant difference was found between the PI of diabetics compared to healthy volunteers (1.59±0.69, 1.63±0.74, respectively). The levels of salivary triglyceride and cholesterol, albumin and total proteins in healthy subjects were significantly higher than that in people with DM (p< 0.001). A significantly more salivary α-amylase activity was found in diabetics compared to non-diabetics (p< 0.001). No significant differences were found between diabetic and non-diabetic subjects in terms of DPPH (95.5, 95.9%, respectively) and FRAP (9.77±0.13, 9.78±0.12 (µmol/mL), respectively).

CONCLUSION

More gingival inflammation and salivary α-amylase activity and lower level of salivary lipids, albumin, and total proteins were found in diabetic patients, but there was no association between the level of lipids, proteins, and the total antioxidant capacity of saliva with periodontal health indicators in patients with DM and healthy individuals.

Evaluation of salivary biomarkers for the diagnosis of periodontitis

Background

Salivary interleukin (IL)-1β, matrix metalloproteinase (MMP)-8, pyridinoline cross-linked carboxyterminal telopeptide of type I collagen (ICTP) and Porphyromonas gingivalis (Pg) are related to periodontitis. This study aimed to investigate the diagnostic potential of these biomarkers and to build a prediction panel for diagnosing periodontal disease.

Methods

A total of 80 participants were enrolled in a cross-sectional study and divided into healthy (n = 25), gingivitis (n = 24), and periodontitis (n = 31) groups based on their periodontal exam results. A full mouth periodontal examination was performed and unstimulated saliva was collected. Salivary IL-1β, MMP-8, ICTP, and Pg were assessed using enzyme-linked immunosorbent assay (ELISA) and quantitative real time PCR (qPCR). Their potentials for diagnosing periodontal disease were analyzed and combined prediction panels of periodontal disease were evaluated.

Results

As a single marker, IL-1β showed the best diagnostic value of the four markers evaluated and exhibited an area under the curve (AUC) value of 0.88 with 90% sensitivity and 76% specificity for discriminating periodontitis subjects from healthy subjects, an AUC value of 0.80 with 83% sensitivity and 76% specificity for discriminating gingivitis subjects from healthy subjects and an AUC value of 0.66 with 68% sensitivity and 64% specificity for differentiating periodontitis subjects from gingivitis subjects. The combination of IL-1β, ICTP, and Pg exhibited the highest efficacy for discriminating periodontitis subjects from healthy subjects (AUC = 0.94) and gingivitis subjects (AUC = 0.77). The combination of IL-1β and MMP-8 exhibited the best ability to discriminate gingivitis from healthy subjects (AUC = 0.84).

Conclusions

Salivary IL-1β, MMP-8, ICTP, and Pg showed significant effectiveness for diagnosing periodontal disease. The combination of IL-1β, ICTP, and Pg can be used to discriminate periodontitis subjects from healthy subjects and gingivitis subjects, and the combination of IL-1β and MMP-8 can be used to discriminate gingivitis subjects from healthy subjects.

Cytokines in saliva as biomarkers of oral and systemic oncological or infectious diseases: A systematic review

Recent evidence suggests that salivary cytokines provide information about both oral conditions and systemic diseases. This review summarizes evidence for the use of salivary cytokines as biomarkers for oral and systemic diseases. We included studies in adults and children with a focus on the latter, due to the importance of non-invasive diagnostic methods in the paediatric age group. A systematic review was performed using Medline and Web of Science covering the period of January 1996 to December 2019 according to the preferred reporting items for systematic reviews. Thirty-four studies were included in the final analysis, for a total of 2407 patients and healthy controls. Pro-inflammatory cytokines including interleukin (IL)-1β, IL-2, IL-6 and tumor necrosis factor (TNF)-α were associated with the severity of oral mucosal tissue damage in patients with cancer, and IL-1β may be an early marker of graft-versus-host disease. Salivary interferon-γ levels were correlated with oral complications and the presence of the underlying disease in HIV-infected individuals, and salivary cytokine patterns may be useful for diagnosing tuberculosis. In summary, current data illustrate that salivary cytokines are associated with oral inflammation, making them potential biomarkers for disease diagnosis and treatment efficacy. Because of the simplicity of saliva collection, this method may be useful in pediatric studies and in resource-limited settings.

Predictive Periodontitis: The Most Promising Salivary Biomarkers for Early Diagnosis of Periodontitis

The primary cause of tooth loss in the industrialized world is periodontitis, a bacterial anaerobic infection whose pathogenesis is characterized by composite immune response. At present, the diagnose of periodontitis is made by a complete status check of the patient’s periodontal health; full-mouth plaque score, full-mouth bleeding score, probing depth, clinical attachment level, bleeding on probing, recessions, mobility, and migration are evaluated in order to provides a clear picture of the periodontal conditions of a single patient. Chair-side diagnostic tests based on whole saliva could be routinely used by periodontists for a very early diagnosis of periodontitis, monitoring, prognosis, and management of periodontal patients by biomarker detection, whose diagnostic validity is related to sensitivity and specificity. Recent paper reviews and meta-analyses have focused on five promising host derived biomarkers as candidate for early diagnosis of periodontitis: MMP-8 (Metalloproteinase-8), MIP-1α (Macrophage inflammatory protein-1 alpha), IL-1 β (Interleukin-1 beta), IL-6 (Interleukin-6), and HB (Hemoglobin), and their combinations. Chair-side Lab-on-a-chip (LOC) technology may soon become an important part of efforts to detect such biomarkers in saliva medium to improve worldwide periodontal health in developed nations as well as in underserved communities and poor countries. Their applications in preventive and predictive medicine is now fundamental, and is aimed at the early detection of risk factors or the presence or evolution of the disease, and in personalized medicine, which aims to identify tailor-made treatments for individual patients. The aim of the present paper is to be informative about host derived periodontal biomarkers and, in particular, we intend to report information about the most important immune response derived biomarkers and Hemoglobin as candidates to be routinely utilized in order to obtain a chair-side early diagnosis of periodontal disease.

Impact of Matrix Metalloproteinase-9 during Periodontitis and Cardiovascular Diseases

Matrix metalloproteinase-9 (MMP-9) has been shown to play a key role in endothelial function and perhaps pivotal in the correlation between periodontal disease and cardiovascular disease (CVD). For the study, the impact of MMP-9 of periodontitis and CVD on serum and saliva concentrations was analyzed. For the study patients with periodontitis (n = 31), CVD (n = 31), periodontitis + CVD (n = 31), and healthy patients (n = 31) were enrolled. Clinical and demographic characteristics as well as serum and salivary MMP-9 were evaluated. MMP-9 concentrations in serum and saliva were statistically elevated in patients with CVD (p < 0.01) and in patients with periodontitis plus CVD (p < 0.001) compared to patients with periodontitis and healthy subjects. Multivariate regression analysis showed that c-reactive protein (hs-CRP) was the only significant predictor for MMP-9 serum (p < 0.001), whereas hs-CRP (p < 0.001) and total cholesterol (p = 0.029) were the statistically significant salivary MMP-9 predictors. This study evidenced that patients with CVD and periodontitis + CVD presented elevated MMP-9 concentrations in serum and saliva compared to patients with periodontitis and healthy subjects. Furthermore, hs-CRP was a negative predictor of serum and salivary MMP-9.

Salivary Cytokines as Potential Diagnostic Biomarkers for Systemic Lupus Erythematosus Disease

Systemic lupus erythematosus (SLE) is a complex autoimmune inflammatory disease characterized by an unknown etiology and a highly variable clinical presentation. This clinical heterogeneity might be explained by dysregulation of tolerance to self and apoptotic mechanisms, overproduction of autoantibodies, and abnormal cytokine levels. Cytokine imbalance levels have been associated with disease activity and severity in SLE patients. In the last years, salivary cytokines related to SLE have gained significant attention and researchers have begun to focus on the identification of cytokines in the saliva of SLE patients using it as a diagnostic fluid for the inflammatory process underlying SLE. This review highlights and summarizes recent studies revealing the cytokines that have been identified in the saliva of individuals with SLE. Data reported and discussed in this report may provide useful additional information to better understand the mechanisms associated with the disease.

Development of an miRNA-Array-Based Diagnostic Signature for Periodontitis

Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis.

Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number

Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified “healthy” and “moderate or severe” periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.

A review of T helper 17 cell-related cytokines in serum and saliva in periodontitis

Periodontitis is a chronic inflammatory disease with a complex underlying immunopathology. Cytokines, as molecular mediators of inflammation, play a role in all stages of disease progression. T helper 17 (Th17) cells are thought to play a role in periodontitis. Th17 cell development and maintenance requires a pro-inflammatory cytokine milieu, with many of the cytokines implicated in the pathogenesis of periodontitis. Serum and saliva are easily accessible biofluids which can represent the systemic and local environment to promote the development of Th17 cells. Here we review human clinical studies that investigate IL-1β, IL-4, IL-6, IL-10, IL-17A, IL-17F, IL-21, IL-22, IL-23, IL-25, IL-31, IL-33, IFN-γ, sCD40L and TNF-α in serum and saliva in periodontitis. We highlight their putative role in the pathogenesis of periodontitis and place them within a wider context of animal and other clinical studies.

Diagnostic Models for Screening of Periodontitis with Inflammatory Mediators and Microbial Profiles in Saliva

This study aims to investigate and assess salivary biomarkers and microbial profiles as a means of diagnosing periodontitis. A total of 121 subjects were included: 28 periodontally healthy subjects, 24 with Stage I periodontitis, 24 with Stage II, 23 with Stage III, and 22 with Stage IV. Salivary proteins (including active matrix metalloproteinase-8 (MMP-8), pro-MMP-8, total MMP-8, C-reactive protein, secretory immunoglobulin A) and planktonic bacteria (including Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Fusobacterium nucleatum, Prevotella intermedia, Porphyromonas nigrescens, Parvimonas micra, Campylobacter rectus, Eubacterium nodatum, Eikenella corrodens, Streptococcus mutans, Staphylococcus aureus, Enterococcus faecalis, and Actinomyces viscosus) were measured from salivary samples. The performance of the diagnostic models was assessed by receiver operating characteristics (ROCs) and area under the ROC curve (AUC) analysis. The diagnostic models were constructed based on the subjects’ proteins and/or microbial profiles, resulting in two potential diagnosis models that achieved better diagnostic powers, with an AUC value > 0.750 for the diagnosis of Stages II, III, and IV periodontitis (Model PA-I; AUC: 0.796, sensitivity: 0.754, specificity: 0.712) and for the diagnosis of Stages III and IV periodontitis (Model PA-II; AUC: 0.796, sensitivity: 0.756, specificity: 0.868). This study can contribute to screening for periodontitis based on salivary biomarkers.

Salivary Biomarkers and Their Application in the Diagnosis and Monitoring of the Most Common Oral Pathologies

Saliva is a highly versatile biological fluid that is easy to gather in a non-invasive manner—and the results of its analysis complement clinical and histopathological findings in the diagnosis of multiple diseases. The objective of this review was to offer an update on the contribution of salivary biomarkers to the diagnosis and prognosis of diseases of the oral cavity, including oral lichen planus, periodontitis, Sjögren’s syndrome, oral leukoplakia, peri-implantitis, and medication-related osteonecrosis of the jaw. Salivary biomarkers such as interleukins, growth factors, enzymes, and other biomolecules have proven useful in the diagnosis and follow-up of these diseases, facilitating the early evaluation of malignization risk and the monitoring of disease progression and response to treatment. However, further studies are required to identify new biomarkers and verify their reported role in the diagnosis and/or prognosis of oral diseases.

Periodontitis and oral cancer - current concepts of the etiopathogenesis

Gingival tissues are attacked by oral pathogens which can induce inflammatory reactions. The immune-inflammatory responses play essential roles in the patient susceptibility to periodontal diseases. There is a wealth of evidence indicating a link between chronic inflammation and risk of malignant transformation of the affected oral epithelium. Periodontitis is associated with an increased risk of developing chronic systemic conditions including autoimmune diseases and different types of cancers. Besides, some risk factors such as smoking, alcohol consumption and human papilloma virus have been found to be associated with both periodontitis and oral cancer. This review article aimed to study the current concepts in pathogenesis of chronic periodontitis and oral cancer by reviewing the related articles.

Therapeutic significance of β-glucuronidase activity and its inhibitors: A review

The emergence of disease and dearth of effective pharmacological agents on most therapeutic fronts, constitutes a major threat to global public health and man's existence. Consequently, this has created an exigency in the search for new drugs with improved clinical utility or means of potentiating available ones. To this end, accumulating empirical evidence supports molecular target therapy as a plausible egress and, β-glucuronidase (βGLU) - a lysosomal acid hydrolase responsible for the catalytic deconjugation of β-d-glucuronides has emerged as a viable molecular target for several therapeutic applications. The enzyme's activity level in body fluids is also deemed a potential biomarker for the diagnosis of some pathological conditions. Moreover, due to its role in colon carcinogenesis and certain drug-induced dose-limiting toxicities, the development of potent inhibitors of βGLU in human intestinal microbiota has aroused increased attention over the years. Nevertheless, although our literature survey revealed both natural products and synthetic scaffolds as potential inhibitors of the enzyme, only few of these have found clinical utility, albeit with moderate to poor pharmacokinetic profile. Hence, in this review we present a compendium of exploits in the present millennium directed towards the inhibition of βGLU. The aim is to proffer a platform on which new scaffolds can be modelled for improved βGLU inhibitory potency and the development of new therapeutic agents in consequential.

Diagnostic sensitivity and specificity of host-derived salivary biomarkers in periodontal disease amongst adults: Systematic review

OBJECTIVE

To systematically assess the diagnostic value of host-derived salivary biomarkers based on their reported sensitivity and specificity in relation to clinical parameters of periodontal disease diagnosis in adults.

MATERIALS AND METHODS

Comprehensive search of PubMed, Nature, Cochrane and OVID (Embase, MEDLINE [R] and PsycINFO) was conducted up to 1 August 2018, using key terms relevant to the research questions and Cochrane methodology, supplemented by a grey literature search. The revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS- 2) tool was used to assess the methodological quality of all included studies.

RESULTS

Seven studies were included in the review. Macrophage inflammatory protein-1αlpha (MIP-1α), interleukin-1beta (IL-1β), interleukin-6 (IL-6) and matrix metalloproteinase-8 (MMP-8) were identified as diagnostically acceptable biomarkers for periodontal disease. Overall, the combination of IL-6 and MMP-8 showed best diagnostic performance. Also, a combination of the four key biomarkers (IL-1β, IL-6, MMP-8 and MIP-1α) showed promising results for distinction between gingivitis and periodontitis, as well as for periodontitis compared with gingival health. Results are interpreted with caution due to limitations in the number of studies included and their quality.

CONCLUSION

Certain salivary biomarkers can potentially be useful in combination and singularly for the diagnosis of periodontal disease. However, further methodically robust research is required to validate these biomarkers.

Accuracy of single molecular biomarkers in saliva for the diagnosis of periodontitis: A systematic review and meta-analysis

AIM

To analyse, using a meta-analytical approach, the diagnostic accuracy of single molecular biomarkers in saliva for the detection of periodontitis in systemically healthy subjects.

MATERIALS AND METHODS

Articles on molecular biomarkers in saliva providing a binary contingency table (or sensitivity and specificity values and group sample sizes) in individuals with clinically diagnosed periodontitis were considered eligible. Searches for candidate articles were conducted in six electronic databases. The methodological quality was assessed through the tool Quality Assessment of Diagnostic Studies. Meta-analyses were performed using the Hierarchical Summary Receiver Operating Characteristic model.

RESULTS

Meta-analysis was possible for 5 of the 32 biomarkers studied. The highest values of sensitivity for the diagnosis of periodontitis were obtained for IL1beta (78.7%), followed by MMP8 (72.5%), IL6 and haemoglobin (72.0% for both molecules); the lowest sensitivity value was for MMP9 (70.3%). In terms of specificity estimates, MMP9 had the best result (81.5%), followed by IL1beta (78.0%) and haemoglobin (75.2%); MMP8 had the lowest specificity (70.5%).

CONCLUSIONS

MMP8, MMP9, IL1beta, IL6 and Hb were salivary biomarkers with good capability to detect periodontitis in systemically healthy subjects. MMP8 and IL1beta are the most researched biomarkers in the field, both showing clinically fair effectiveness for the diagnosis of periodontitis.

Ability of S100 proteins and matrix metalloproteinase-9 to identify periodontitis in a ligature-induced periodontitis dog model

AIMS

The present study aimed to monitor the levels of selected salivary biomarkers during the development and treatment of periodontitis and to evaluate their ability to identify periodontitis in dogs.

MATERIALS AND METHODS

A total of 15 beagle dogs were divided into a control group (no ligature), group 1 (ligature on six teeth), and group 2 (ligature on 12 teeth). The experimental periods consisted of 8 weeks of periodontitis induction and 4 weeks of treatment. Clinical measurements and the sampling of saliva were performed every 4 weeks. The levels of S100A8, S100A9, S100A8/A9, and matrix metalloproteinase (MMP)-9 were measured by enzyme-linked immunosorbent assay.

RESULTS

All experimental animals and two control animals developed periodontitis, which was successfully treated. All salivary biomarkers were significantly increased in periodontitis with high diagnostic power (c-index ≥ 0.944) and were able to identify animals with periodontitis on a single tooth. Whereas the levels of salivary S100A8/A9 recovered to levels in health, those of S100A8, S100A9, and MMP-9 in periodontitis stability remained significantly higher than in health.

CONCLUSION

Salivary S100A8, S100A9, S100A8/A9, and MMP-9 may be used for the screening of periodontitis in dogs, but with caution of other conditions that can affect their levels in saliva.