Emerging horizons of salivary diagnostics for periodontal disease

Investigating the role of salivary Interleukin-40 levels in diagnosing periodontal diseases

BACKGROUND

The present study aimed to analyze IL-40, IL-1β, and MMP-8 levels in periodontitis as well as gingivitis and periodontal health, and to explore potential correlations between these biomarkers and standard clinical parameters.

MATERIALS AND METHODS

We collected saliva samples from 120 systemically healthy, non-smoking individuals aged between 18 and 63 years. These individuals were divided into three groups: healthy controls [S], gingivitis [G], and stage III grade B periodontitis [P]. IL-40, IL-1β, and MMP-8 levels in saliva samples were analyzed by ELISA.

RESULTS

We observed significantly elevated salivary IL-40 levels in the G group compared to the S group (p = 0.003). We found significantly higher salivary IL-1β levels in the P group compared to both the S and G groups (p = 0.000). Salivary MMP-8 levels were significantly higher in the P group than in the S group (p = 0.016).

CONCLUSION

Our study suggests that IL-40 and IL-1β may serve as effective salivary biomarkers for diagnosing gingivitis, while MMP-8 and IL-1β may be effective for distinguishing periodontitis. Based on our study's findings, it can be stated that IL-40 may serve as a new and effective biomarker for distinguishing individuals with gingivitis from healthy ones.

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.

Genomic Medicine in Canine Periodontal Disease: A Systematic Review

Genomic medicine has become a growing reality; however, it is still taking its first steps in veterinary medicine. Through this approach, it will be possible to trace the genetic profile of a given individual and thus know their susceptibility to certain diseases, namely periodontal disease. This condition is one of the most frequently diagnosed in companion animal clinics, especially in dogs. Due to the limited existing information and the lack of comprehensive studies, the objective of the present study was to systematically review the existing scientific literature regarding genomic medicine in canine periodontal disease and determine which genes have already been studied and their probable potential. This study followed the recommendations of the PRISMA 2020 methodology. Canine periodontal disease allied to genomic medicine were the subjects of this systematic review. Only six articles met all of the inclusion criteria, and these were analyzed in detail. These studies described genetic variations in the following genes: interleukin-6, interleukin-10, interleukin-1, lactotransferrin, toll-like receptor 9, and receptor activator of nuclear factor-kappa B. Only in two of them, namely interleukin-1 and toll-like receptor 9 genes, may the identified genetic variations explain the susceptibility that certain individuals have to the development of periodontal disease. It is necessary to expand the studies on the existing polymorphic variations in genes and their relationship with the development of periodontal disease. Only then will it be possible to fully understand the biological mechanisms that are involved in this disease and that determine the susceptibility to its development.

The Role of Salivary C-Reactive Protein in Diagnosis of Neonatal Sepsis

Background

Neonatal sepsis remains one of the leading causes of morbidity and mortality among neonates.

Objective

to evaluate the role of salivary C-reactive protein (CRP) as a diagnostic marker in neonatal sepsis.

Methods

This case-control study was carried out on 90 neonates including 45 neonates with symptoms and signs suggestive of neonatal sepsis and 45 healthy neonates as controls. All neonates were subjected to full history taking, thorough clinical examination, laboratory investigations including complete blood count (CBC), serum CRP, blood culture, and salivary CRP.

Results

Septic neonates showed significantly higher salivary CRP compared to controls; it was significantly associated with positive serum CRP, blood culture, and hematological scoring system (HSS). It was significantly higher in neonates who died compared to those who survived. Twenty one cases with positive salivary CRP showed significant positive correlations with the length of the neonatal intensive care unit stay, total white blood cell (WBC) count, mean platelet volume (MPV), neutrophils to lymphocytes ratio (NLR), serum CRP, HSS, significant negative correlations with gestational age, birth weight, APGAR at 1, 5 min, and platelet count. Receiver operating characteristic (ROC) curve for salivary CRP showed that at cut-off value of 0.135 mg/L, the sensitivity was (81.8%) and the specificity was (75.6%) for detecting neonatal sepsis.

Conclusion

Salivary CRP was significantly higher in the septic group, and was associated with positive serum CRP. Hence, salivary CRP could be considered a novel non-invasive biomarker for diagnosing neonatal sepsis.

Changes in salivary matrix metalloproteinase-3, -8, and -9 concentrations after 6 weeks of non-surgical periodontal therapy

Background

Studies using salivary inflammatory biomarkers for diagnosing and monitoring the progression of periodontal disease have garnered increased attention in recent years. The present study aimed to identify changes in clinical parameters and concentrations of salivary matrix metalloproteinases (MMPs) following 6 weeks of non-surgical periodontal therapy (NSPT).

Methods

A 6-week NSPT program was applied to 51 adults aged ≥ 20 years. The program involved scaling, root planing, and professional toothbrushing for healthy participants and those with periodontal disease. Patients with periodontal disease underwent professional toothbrushing during all three visits. Periodontal pocket depth (PD) and gingival bleeding were assessed at week 0, week 3, and week 6, and saliva samples were collected to measure the concentrations of MMP-3, -8, and -9.

Results

All clinical parameters were improved in the periodontal disease groups following the NSPT course. Compared with healthy participants, the patients with periodontal disease showed increased concentrations of salivary MMP-3, -8, and -9. During the 6-week program, patients with periodontal disease also showed significant reductions in PD and gingival bleeding during the third week; no significant reduction was found during the sixth week. Significant reductions in the concentrations of salivary MMP-3, -8, and -9 were also noted in the periodontal disease group at week 3. The sensitivity and specificity of MMP-3 for predicting periodontitis were 81.8% and 55.5%, respectively.

Conclusion

The present study found that NSPT resulted in reductions of salivary MMP-3, -8, and -9, and identified the potential of MMP-3 as a biomarker in the diagnosis of periodontal disease. These findings may serve as foundational data for future studies into the development of diagnostic kits for periodontal disease.

The emerging role of small extracellular vesicles in saliva and gingival crevicular fluid as diagnostics for periodontitis

Periodontitis is a highly prevalent multifactorial chronic inflammatory disease associated with a destructive host immune-inflammatory response to microbial dysbiosis. Current clinical diagnosis is reliant on measuring past periodontal tissue loss, with a lack of molecular biomarkers to accurately diagnose periodontitis activity in 'real-time'. Thus, discovery of new classes of diagnostic biomarkers is of critical importance in periodontology. Small extracellular vesicles (<200 nm in diameter; sEVs) from oral biofluids (saliva and gingival crevicular fluid-GCF) are lipid-encapsulated bilayered vesicles and have recently emerged as a potential source of biomarkers for periodontal disease (gingivitis and periodontitis), due to the cargo of protein, genetic material and lipids derived from their parent cells. There is limited information on the isolation and characterisation methods of saliva/GCF-sEVs or the characterisation of sEVs cargo as biomarkers for periodontitis. In this review, we detail the composition of sEVs and summarise their isolation and characterisation from saliva and GCF. The potential role of saliva and GCF-derived sEVs in periodontitis diagnosis is also explored. It is proposed that sEVs cargo, including protein, microRNA, message RNA and DNA methylation, are potential biomarkers for periodontitis with good diagnostic power (area under the curve-AUC > 0.9).

Saliva as a Source of Biomarkers for Periodontitis and Periimplantitis

Saliva has the potential to be used as a diagnostic and monitoring tool for various diseases if biomarkers of an adequate sensitivity and specificity could be identified. Several reviews and even meta-analyses have been performed in recent years, which have found some candidate biomarkers for periodontitis, like macrophage inflammatory protein-1 alpha, interleukin-1ß, interleukin-6, matrix metalloproteinase-8, or hemoglobin. However, none of those are currently in use to replace conventional periodontal diagnostics with a periodontal probe. For periimplantitis, to date, heterogeneity of different study protocols and implant types did not permit to discover clear biomarkers, which were able to distinguish between healthy and diseased implants. Few proinflammatory cytokines, similar to periodontitis, have been characterized as adjunct tools to clinical diagnosis. The additional determination of antimicrobial peptides, bone turnover markers, and bacteria could help to enhance sensitivity and specificity in a combined model for periodontitis and periimplantitis. Furthermore, proteomic approaches might be preferred over single biomarker determinations. A global consensus is also needed to harmonize salivary sampling methods as well as procedures of biomarker analysis to ensure future comparability.

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.

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.

Macrophage activity is associated with gingival inflammation: Soluble CD163 in an experimental gingivitis study

AIM

This study aimed to investigate the association between gingival inflammation and levels of soluble CD163 (sCD163), a macrophage-specific marker associated to inflammation, in young adults participating in an experimental gingivitis study.

METHODS

Forty-two university students volunteered to participate in the study, which comprised three phases: a two-week Hygiene Phase (clinical examination and professional cleaning); a three-week Induction Phase (absence of oral hygiene); and a two-week Resolution Phase (reestablishment of oral hygiene). Clinical recordings of plaque (Modified Quigley and Hein Plaque Index) and gingival inflammation (Modified Gingival Index) were collected weekly during the Induction Phase, and after two weeks during the Resolution Phase. Levels of sCD163 from gingival crevicular fluid (GCF) were collected during Induction and Resolution Phases and measured by ELISA. Group-based-trajectory-modeling (GBTM) was used to model patterns of sCD163 throughout the Induction Phase. Mixed-effects multilevel models were used to estimate the effect of gingival inflammation on sCD163 over time.

RESULTS

Levels of sCD163 increased steadily over time, however, sCD163 showed a lagged response to gingival inflammation. GBTM analysis identified two groups for sCD163: one with a "linear" trajectory of sCD163 over the Induction Phase (n = 35), and another with a "quadratic" (n = 7) increase of sCD163 at the end of the Induction Phase. Stratified analysis by the sCD163 groups revealed that "linear" sCD163 growth was associated with both GCF volume and gingival inflammation but lagged in time, while a "quadratic" growth was associated with gingival inflammation and time.

CONCLUSIONS

Macrophage activity is associated with gingival inflammation and can be detected at early stages of gingivitis. However, while in most participants a "linear" trajectory of sCD163 over the development of gingival inflammation was observed, among few individuals an exacerbated increase of sCD163 levels in GCF was noticed particularly at the end of the Induction Phase.

Nanomaterials for Tissue Engineering In Dentistry

The tissue engineering (TE) of dental oral tissue is facing significant changes in clinical treatments in dentistry. TE is based on a stem cell, signaling molecule, and scaffold triad that must be known and calibrated with attention to specific sectors in dentistry. This review article shows a summary of micro- and nanomorphological characteristics of dental tissues, of stem cells available in the oral region, of signaling molecules usable in TE, and of scaffolds available to guide partial or total reconstruction of hard, soft, periodontal, and bone tissues. Some scaffoldless techniques used in TE are also presented. Then actual and future roles of nanotechnologies about TE in dentistry are presented.

Nanotechnology in dentistry: prevention, diagnosis, and therapy

Nanotechnology has rapidly expanded into all areas of science; it offers significant alternative ways to solve scientific and medical questions and problems. In dentistry, nanotechnology has been exploited in the development of restorative materials with some significant success. This review discusses nanointerfaces that could compromise the longevity of dental restorations, and how nanotechnolgy has been employed to modify them for providing long-term successful restorations. It also focuses on some challenging areas in dentistry, eg, oral biofilm and cancers, and how nanotechnology overcomes these challenges. The recent advances in nanodentistry and innovations in oral health-related diagnostic, preventive, and therapeutic methods required to maintain and obtain perfect oral health, have been discussed. The recent advances in nanotechnology could hold promise in bringing a paradigm shift in dental field. Although there are numerous complex therapies being developed to treat many diseases, their clinical use requires careful consideration of the expense of synthesis and implementation.

Detecting infection in neonates: promises and challenges of a salivary approach

Premature newborns present unique challenges for the caregiver. Their clinical fragility and immature immune system places them at increased risk for bacterial and viral infections. Current clinical standard of care mandates invasive phlebotomy to assess an infant for an infection. However, serial blood draws can lead to blood transfusions and the infliction of noxious stimuli to this vulnerable population. Salivary screening for common neonatal morbidities, such as infections, could vastly improve the care for these infants and positively affect their long-term clinical outcomes. Recent technological advancements have improved our ability to detect thousands of proteins and/or microbes from a single salivary sample, making noninvasive assessment in neonates a possibility. This article reviews the clinical applications and challenges associated with integrating salivary analysis for infectious surveillance into the neonatal population.