Prediction of Periodontal Inflammation via Metabolic Profiling of Saliva

Amino acid metabolomics and machine learning-driven assessment of future liver remnant growth after hepatectomy in livers of various backgrounds

Accurate assessment of future liver remnant growth after partial hepatectomy (PH) in patients with different liver backgrounds is a pressing clinical issue. Amino acid (AA) metabolism plays a crucial role in liver regeneration. In this study, we combined metabolomics and machine learning (ML) to develop a generalized future liver remnant assessment model for multiple liver backgrounds. The liver index was calculated at 0, 6, 24, 48, 72 and 168 h after 70 % PH in healthy mice and mice with nonalcoholic steatohepatitis or liver fibrosis. The serum levels of 39 amino acids (AAs) were measured using UPLC-MS/MS. The dataset was randomly divided into training and testing sets at a 2:1 ratio, and orthogonal partial least squares regression (OPLS) and minimally biased variable selection in R (MUVR) were used to select a metabolite signature of AAs. To assess liver remnant growth, nine ML models were built, and evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The post-Pareto technique for order preference by similarity to the ideal solution (TOPSIS) was employed for ranking the ML algorithms, and a stacking technique was utilized to establish consensus among the superior algorithms. Compared with those of OPLS, the signature AAs set identified by MUVR (Thr, Arg, EtN, Phe, Asa, 3MHis, Abu, Asp, Tyr, Leu, Ser, and bAib) are more concise. Post-Pareto TOPSIS ranking demonstrated that the majority of ML algorithm in combinations with MUVR outperformed those with OPLS. The established SVM-KNN consensus model performed best, with an R2 of 0.79, an MAE of 0.0029, and an RMSE of 0.0035 for the testing set. This study identified a metabolite signature of 12 AAs and constructed an SVM-KNN consensus model to assess future liver remnant growth after PH in mice with different liver backgrounds. Our preclinical study is anticipated to establish an alternative and generalized assessment method for liver regeneration.

Metabolic profiling of peri-implant crevicular fluid in peri-implantitis

OBJECTS

This study aims to explore the etiology of peri-implantitis by comparing the metabolic profiles in peri-implant crevicular fluid (PICF) from patients with healthy implants (PH) and those with peri-implantitis (PI).

MATERIALS AND METHODS

Fifty-six patients were enrolled in this cross-sectional study. PICF samples were collected and analyzed using both non-targeted and targeted metabolomics approaches. The relationship between metabolites and clinical indices including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL) was examined. Additionally, submucosal microbiota was collected and analyzed using 16S rRNA gene sequencing to elucidate the association between the metabolites and microbial communities.

RESULTS

Significant differences in metabolic profiles were observed between the PH and PI groups, with 179 distinct metabolites identified. In the PI group, specific amino acids and fatty acids were significantly elevated compared to the PH group. Organic acids including succinic acid, fructose-6-phosphate, and glucose-6-phosphate were markedly higher in the PI group, showing positive correlations with mean PD, BOP, and MBL. Metabolites that increased in the PI group positively correlated with the presence of Porphyromonas and Treponema and negatively with Streptococcus and Haemophilus.

CONCLUSIONS

This study establishes a clear association between metabolic compositions and peri-implant condition, highlighting enhanced metabolite activity in peri-implantitis. These findings open avenues for further research into metabolic mechanisms of peri-implantitis and their potential therapeutic implications.

Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation

Objectives

This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease.

Methods

The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics.

Results

Correlation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average.

Conclusions

Data-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal-external validations.

Combined assessment of lysine and N-acetyl cadaverine levels assist as a potential biomarker of the smoker periodontitis

Periodontitis is an inflammatory condition of supporting structures of teeth leading to attachment and bone loss. Cigarette smoking is the single most important and modifiable risk factor with 5 to 20-fold susceptibility for periodontal diseases. Reverse smoking is a peculiar habit of smoking where the lit end is kept inside the mouth, which is predominant in the northern coastal districts of Andhra Pradesh. Polyamines are biologically active amines involved in tissue regeneration and modulation of inflammation. The study aimed to evaluate polyamines and check their utility as a marker in detection of periodontitis among different groups. Total polyamine levels showed significant increase in reverse smokers with periodontitis when compared to the other groups. Qualitative analysis by thin layer chromatography showed three polyamine bands with varying intensity among the different groups. Mass spectrometric and NMR analyses of the three bands identified them as N1, N8-diacetyl spermidine, N-acetyl cadaverine and lysine. Most significantly elevated levels of lysine was observed in the smoker and reverse smoker periodontitis groups when compared to healthy and non-smoker periodontitis groups. The significantly elevated levels of N-acetyl cadaverine could be responsible for the more destruction of periodontium in the reverse smoker group. Antioxidant potential decreased significantly in different smoker periodontitis groups. The present study suggests that the quantitative analysis of salivary polyamines, lysine and N-acetyl cadaverine can aid as an easy noninvasive diagnostic method for assessing the periodontal status, especially in smokers.

Pyroptotic macrophages induce disruption of glutamate metabolism in periodontal ligament stem cells contributing to their compromised osteogenic potential

Macrophage pyroptosis is of key importance to host defence against pathogen infections and may participate in the progression and recovery of periodontitis. However, the role of pyroptotic macrophages in regulating periodontal ligament stem cells (PDLSCs), the main cell source for periodontium renewal, remains unclear. First, we found that macrophage pyroptosis were enriched in gingiva tissues from periodontitis patients compared with those of healthy people through immunofluorescence. Then the effects of pyroptotic macrophages on the PDLSC osteogenic differentiation were investigated in a conditioned medium (CM)-based coculture system in vitro. CM derived from pyroptotic macrophages inhibited the osteogenic differentiation-related gene and protein levels, ALP activity and mineralized nodule formation of PDLSCs. The osteogenic inhibition of CM was alleviated when pyroptosis was inhibited by VX765. Further, untargeted metabolomics showed that glutamate limitation may be the underlying mechanism. However, exogenous glutamate supplementation aggravated the CM-inhibited osteogenic differentiation of PDLSCs. Moreover, CM increased extracellular glutamate and decreased intracellular glutamate levels of PDLSCs, and enhanced the gene and protein expression levels of system xc - (a cystine/glutamate antiporter). After adding cystine to CM-based incubation, the compromised osteogenic potency of PDLSCs was rescued. Our data suggest that macrophage pyroptosis is related to the inflammatory lesions of periodontitis. Either pharmacological inhibition of macrophage pyroptosis or nutritional supplements to PDLSCs, can rescue the compromised osteogenic potency caused by pyroptotic macrophages.

Salivary Metabolites Produced by Oral Microbes in Oral Diseases and Oral Squamous Cell Carcinoma: A Review

In recent years, salivary metabolome studies have provided new biological information and salivary biomarkers to diagnose different diseases at early stages. The saliva in the oral cavity is influenced by many factors that are reflected in the salivary metabolite profile. Oral microbes can alter the salivary metabolite profile and may express oral inflammation or oral diseases. The released microbial metabolites in the saliva represent the altered biochemical pathways in the oral cavity. This review highlights the oral microbial profile and microbial metabolites released in saliva and its use as a diagnostic biofluid for different oral diseases. The importance of salivary metabolites produced by oral microbes as risk factors for oral diseases and their possible relationship in oral carcinogenesis is discussed.

Salivary metabolomic biomarkers for non-invasive lung cancer detection

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

The polymicrobial pathogenicity of Porphyromonas gingivalis

Accumulating microbiome data and mechanistic studies in vitro and in vivo have refined our understanding of the oral microbiota as a functionally integrated polymicrobial community. Emergent properties of these communities are driven to a large extent by interspecies communication which can be based on physical association, secreted small molecules or nutritional exchange. Porphyromonas gingivalis is a consensus periodontal pathogen; however, virulence is only expressed in the context of a polymicrobial community. Multivalent fimbriae mediate attachment to other oral species which can initiate a distinct transcriptional program in both constituents of the binding pair. P. gingivalis also responds to small molecules and nutritional cues produced by partner organisms. Physiological interdependence forms the basis of complex networks of cooperating organisms which begin to resemble an organismal entity exhibiting a spectrum of pathogenic potential.

Comparisons of different extraction methods and solvents for saliva samples

INTRODUCTION

Changes in the categories and concentrations of salivary metabolites may be closely related to oral, intestinal or systemic diseases. To study salivary metabolites, the first analytical step is to extract them from saliva samples as much as possible, while reducing interferences to a minimum. Frequently used extraction methods are protein precipitation (PPT), liquid-liquid extraction (LLE) and solid-phase extraction (SPE), with various organic solvents. The types and quantities of metabolites extracted with different methods may vary greatly, but few studies have systematically evaluated them.

OBJECTIVES

This study aimed to select the most suitable methods and solvents for the extraction of saliva according to different analytical targets.

METHODS

An untargeted metabolomics approach based on liquid chromatography-mass spectrometry was applied to obtain the raw data. The numbers of metabolites, repeatability of the data and intensities of mass spectrometry signals were used as evaluation criteria.

RESULTS

PPT resulted in the highest coverage. Among the PPT solvents, acetonitrile displayed the best repeatability and the highest coverage, while acetone resulted in the best signal intensities for the extracted compounds. LLE with the mixture of chloroform and methanol was the most suitable for the extraction of small hydrophobic compounds.

CONCLUSION

PPT with acetonitrile or acetone was recommended for untargeted analysis, while LLE with the mixture of chloroform and methanol was recommended for small hydrophobic compounds.

Manipulating the diseased oral microbiome: the power of probiotics and prebiotics

Dental caries and periodontal disease are amongst the most prevalent global disorders. Their aetiology is rooted in microbial activity within the oral cavity, through the generation of detrimental metabolites and the instigation of potentially adverse host immune responses. Due to the increasing threat of antimicrobial resistance, alternative approaches to readdress the balance are necessary. Advances in sequencing technologies have established relationships between disease and oral dysbiosis, and commercial enterprises seek to identify probiotic and prebiotic formulations to tackle preventable oral disorders through colonisation with, or promotion of, beneficial microbes. It is the metabolic characteristics and immunomodulatory capabilities of resident species which underlie health status. Research emphasis on the metabolic environment of the oral cavity has elucidated relationships between commensal and pathogenic organisms, for example, the sequential metabolism of fermentable carbohydrates deemed central to acid production in cariogenicity. Therefore, a focus on the preservation of an ecological homeostasis in the oral environment may be the most appropriate approach to health conservation. In this review we discuss an ecological approach to the maintenance of a healthy oral environment and debate the potential use of probiotic and prebiotic supplementation, specifically targeted at sustaining oral niches to preserve the delicately balanced microbiome.

Revealing oxidative degradation of lipids and screening potential markers of four vegetable oils during thermal processing by pseudotargeted oxidative lipidomics

The oxidative degradation of lipids in vegetable oils during thermal processing may present a risk to human health. However, not much is known about the evolution of lipids and their non-volatile derivatives in vegetable oils under different thermal processing conditions. In the present study, a pseudotargeted oxidative lipidomics approach was developed and the evolution of lipids and their non-volatile derivatives in palm oil, rapeseed oil, soybean oil, and flaxseed oil under different thermal processing conditions was investigated. The results showed that thermal processing resulted in the oxidative degradation of TGs in vegetable oils, which generated oxTGs, DGs, and FFAs, as well as TGs with smaller molecular weights. The lower the fatty acid saturation, the more severe the oxidative degradation of vegetable oils and thermal processing at high temperatures should be avoided if possible. From the accumulation of oxTGs concentrations, the hazards during thermal processing at high temperatures were, in descending order, soybean oil, rapeseed oil, flaxseed oil, and palm oil. The non-volatile potential markers were screened in palm oil, rapeseed oil, soybean oil, and flaxseed oil for 1, 7, 5, and 2 markers related to thermal processing time, respectively. The study provided suggestions for the consumption of vegetable oils from multiple perspectives and identified markers for monitored oxidative degradation of vegetable oils.

Dysbiosis of oral microbiome persists after dental treatment-induced remission of periodontal disease and dental caries

IMPORTANCE

We characterized the oral conditions, salivary microbiome, and metabolome after dental treatment by investigating the state after treatment completion and transition to self-care. Dental treatment improved oral health conditions, resulting in oral disease remission; however, the imbalanced state of the salivary microbiome continued even after remission. Although the results of this study are preliminary, owing to the small number of participants in each group when compared to larger cohort studies, they indicate that the risk of disease may remain higher than that of healthy participants, thereby demonstrating the importance of removing dental plaque containing disease-related bacteria using appropriate care even after treatment completion. We also identified bacterial species with relative abundances that differed from those of healthy participants even after remission of symptoms, which may indicate that the maturation of certain bacterial species must be controlled to improve the oral microbiome and reduce the risk of disease recurrence.

Mass spectrometry-based metabolic profiling for identification of biomarkers related to footpad dermatitis in ducks

1. A new assessment method for duck footpad dermatitis (FPD) evaluation was developed, combining visual and histological characters using the images and sections of 400 ducks' feet at 340 d of age. All ducks were graded as G0 (healthy), G1 (mild), G2 (moderate) and G3 (severe) according to the degree of FPD.2. To reveal the potential biomarkers in serum related to duck FPD, non-targeted metabolomics and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to explore differential metabolites in each group.3. There were 57, 91 and 210 annotated differential metabolites in groups G1, G2 and G3 compared with G0, which meant that the severity of FPD increased in line with the number of metabolites. Four metabolites, L-phenylalanine, L-arginine, L-leucine and L-lysine, were considered potential biomarkers related to FPD.4. KEGG enrichment analysis showed that the FPD was mainly involved in glycolysis, the tricarboxylic acid (TCA) cycle, the pentose phosphate pathway and amino acid metabolism. These are related to production metabolism and can affect the physiological activities of ducks, which might explain the decrease in production performance.

Identification of potential salivary biomarkers for Sjögren's syndrome with an untargeted metabolomic approach

INTRODUCTION

Despite the rise of metabolomics over the past years, and particularly salivary metabolomics, little research on Sjögren's syndrome (SS) biomarkers has focused on the salivary metabolome.

OBJECTIVES

This study aims to identify metabolites that could be used as biomarkers for SS.

METHODS

Using the software called XCMS online, the salivary metabolic profiles obtained with liquid chromatography coupled to high-resolution mass spectrometry for 18 female SS patients were compared to those obtained for 22 age-matched female healthy controls.

RESULTS AND CONCLUSION

A total of 91 metabolites showed differential expression in SS patients. A putative identification was proposed with the use of a database for 37 of these metabolites and, of these, 16 identifications were confirmed. Given the identified metabolites, some important metabolic pathways, such as amino acid metabolism, purine metabolism, or even the citric acid cycle seem to be affected. Through the analyses of the ROC (receiver operating characteristic) curves, three metabolites, namely alanine, isovaleric acid, and succinic acid, showed both good sensitivity (respectively 1.000, 1.000, and 0.750) and specificity (respectively 0.692, 0.615, and 0.692) for identifying SS and could then be interesting biomarkers for a potential salivary diagnosis test.

Comparison of the Metabolic Profile between Unstimulated and Stimulated Saliva Samples from Pregnant Women with/without Obesity and Periodontitis

This study aimed to compare the metabolic profile of unstimulated (US) and stimulated (SS) saliva samples from pregnant women with/without obesity and periodontitis. Ninety-six pregnant women were divided into: obesity + periodontitis (OP = 20); obesity/no periodontitis (OWP = 27); normal BMI + periodontitis (NP = 20); and normal BMI/no periodontitis (NWP = 29). US and SS samples were collected by expectoration and chewing of sterilized parafilm gum, respectively, and samples were individually analyzed by Proton Nuclear Magnetic Resonance (1H-NMR). Univariate (t test and correlations) and multivariate (Principal Component Analysis-PCA, and Partial Least Square-Discriminant Analysis-PLS-DA with Variance Importance Projection-VIP scores) and Metabolite Set Enrichment Analysis were done (p < 0.05). Metabolites commonly found in all groups in elevated concentration in US samples were 5-Aminopentoate, Acetic acid, Butyric acid, Propionic acid, Pyruvic acid, and Succinic acid. They were mainly related to the butyrate metabolism, citric acid cycle, amino sugar metabolism, fatty acids biosynthesis, pyruvate metabolism, glutamate metabolism, and Warburg effect. Metabolites commonly found in all groups that were in elevated concentration in SS samples were Citrulline, Fumaric acid, Histidine, N-acetyl glutamine, N-acetylneuraminic acid, para-hydroxyphenylacetic acid, Proline, Tyrosine. Although some differences were found between unstimulated and stimulated saliva samples from pregnant women with/without obesity and periodontitis, stimulated saliva collection seems adequate, demonstrating similar metabolic pathways to unstimulated saliva samples when groups are compared.

Microbial metabolites in the pathogenesis of periodontal diseases: a narrative review

The purpose of this narrative review is to highlight the importance of microbial metabolites in the pathogenesis of periodontal diseases. These diseases, involving gingivitis and periodontitis are inflammatory conditions initiated and maintained by the polymicrobial dental plaque/biofilm. Gingivitis is a reversible inflammatory condition while periodontitis involves also irreversible destruction of the periodontal tissues including the alveolar bone. The inflammatory response of the host is a natural reaction to the formation of plaque and the continuous release of metabolic waste products. The microorganisms grow in a nutritious and shielded niche in the periodontal pocket, protected from natural cleaning forces such as saliva. It is a paradox that the consequences of the enhanced inflammatory reaction also enable more slow-growing, fastidious, anaerobic bacteria, with often complex metabolic pathways, to colonize and thrive. Based on complex food chains, nutrient networks and bacterial interactions, a diverse microbial community is formed and established in the gingival pocket. This microbiota is dominated by anaerobic, often motile, Gram-negatives with proteolytic metabolism. Although this alternation in bacterial composition often is considered pathologic, it is a natural development that is promoted by ecological factors and not necessarily a true "dysbiosis". Normal commensals are adapting to the gingival crevice when tooth cleaning procedures are absent. The proteolytic metabolism is highly complex and involves a number of metabolic pathways with production of a cascade of metabolites in an unspecific manner. The metabolites involve short chain fatty acids (SCFAs; formic, acetic, propionic, butyric, and valeric acid), amines (indole, scatole, cadaverine, putrescine, spermine, spermidine) and gases (NH₃, CO, NO, H₂S, H₂). A homeostatic condition is often present between the colonizers and the host response, where continuous metabolic fluctuations are balanced by the inflammatory response. While it is well established that the effect of the dental biofilm on the host response and tissue repair is mediated by microbial metabolites, the mechanisms behind the tissue destruction (loss of clinical attachment and bone) are still poorly understood. Studies addressing the functions of the microbiota, the metabolites, and how they interplay with host tissues and cells, are therefore warranted.

Assessment and Correlation of Salivary Ca, Mg, and pH in Smokers and Non-Smokers with Generalized Chronic Periodontitis

Background and Objectives: Diagnostic evaluation with the aid of biomarkers has reached newer heights to assess disease activity. Salivary calcium, magnesium, and pH are one of the biochemical parameters which can be helpful in assessing the progression of periodontal disease. Smokers are at topnotch threat for having oral diseases, predominantly periodontal diseases. The aim of this study was to assess the salivary calcium, magnesium, and pH levels in smokers compared with non-smokers with chronic periodontitis. Materials and Methods: The current study was conducted on 210 individuals affected with generalized chronic periodontitis, with the age group between 25 and 55 years. Based on their smoking habit, an equal number of patients were categorized into two groups; namely, group I consisted of non-smokers and group II consisted of smokers. The clinical parameters that were measured included Plaque Index (PI), Gingival Index (GI), Probing Pocket Depth (PPD), and Clinical Attachment Loss (CAL). The biochemical variables that were evaluated in the current study included salivary calcium, magnesium, and pH using an AVL9180 electrolyte analyzer (Roche, Germany). The gathered data were analyzed with an unpaired t test was using SPSS 20.0. Results: A statistically significant higher PPD (p < 0.01), CAL (p < 0.05), and salivary calcium levels (p < 0.001) were observed in the smokers' compared with their non-smoking counterparts. Among the biochemical parameters, calcium showed a significantly (p < 0.001) higher level in smokers (5.79 ± 1.76) in contrast to non-smokers (3.87 ± 1.03). Additionally, a significant negative correlation (p < 0.05) between calcium and PPD was observed in non-smokers, whereas a non-significant inverse relation (p > 0.05) was seen in smokers. Conclusions: The present study indicates that the salivary calcium level can be a potential biochemical parameter to assess the progression of periodontal disease in smokers and non-smokers. Within the limitations of the current study, the salivary biomarkers appear to have an essential role in the identification and indication of the status of periodontal diseases.

Saliva Metabolomic Profile in Dental Medicine Research: A Narrative Review

Metabolomic research tends to increase in popularity over the years, leading to the identification of new biomarkers related to specific health disorders. Saliva is one of the most newly introduced and systematically developed biofluids in the human body that can serve as an informative substance in the metabolomic profiling armamentarium. This review aims to analyze the current knowledge regarding the human salivary metabolome, its alterations due to physiological, environmental and external factors, as well as the limitations and drawbacks presented in the most recent research conducted, focusing on pre—analytical and analytical workflows. Furthermore, the use of the saliva metabolomic profile as a promising biomarker for several oral pathologies, such as oral cancer and periodontitis will be investigated.

Salivary metabolome indicates a shift in tyrosine metabolism in patients with burning mouth syndrome: a prospective case-control study

ABSTRACT

The pathophysiology of primary burning mouth syndrome (BMS) remains controversial. Targeted analyses or "omics" approach of saliva provide diagnostic or pathophysiological biomarkers. This pilot study's primary objective was to explore the pathophysiology of BMS through a comparative analysis of the salivary metabolome among 26 BMS female cases and 25 age- and sex-matched control subjects. Secondary objectives included comparative analyses of inflammatory cytokines, neuroinflammatory markers, and steroid hormones among cases and control subjects, and among BMS patients according to their clinical characteristics. Salivary metabolome, neuroinflammatory markers, cytokines, and steroids were, respectively, analysed by liquid chromatography coupled with mass spectrometry, ELISA and protease activity assay, and multiparametric Luminex method. Among the 166 detected metabolites, univariate analysis did not find any discriminant metabolite between groups. Supervised multivariate analysis divided patients into 2 groups with an accuracy of 60% but did not allow significant discrimination (permutation test, P = 0.35). Among the metabolites contributing to the model, 3 belonging to the tyrosine pathway ( l -dopa, l -tyrosine, and tyramine) were involved in the discrimination between cases and control subjects, and among BMS patients according to their levels of pain. Among the detectable molecules, levels of cytokines, steroid hormones, and neuroinflammatory markers did not differ between cases and control subjects and were not associated with characteristics of BMS patients. These results do not support the involvement of steroid hormones, inflammatory cytokines, or inflammatory neurogenic mediators in the pathophysiology of pain in BMS, whereas the observed shift in tyrosine metabolism may indicate an adaptative response to chronic pain or an impaired dopaminergic transmission.

Dysbiosis of Oral Microbiota and Metabolite Profiles Associated with Type 2 Diabetes Mellitus

Several previous studies have shown that oral microbial disorders may be closely related to the occurrence and development of type 2 diabetes mellitus (T2DM). However, whether the function of oral microorganisms and their metabolites have changed in patients with T2DM who have not suffered from any oral diseases has not been reported. We performed metagenomic analyses and nontargeted metabolic analysis of saliva and supragingival plaque samples from patients with T2DM who have not suffered any oral diseases and normal controls. We found that periodontal pathogens such as Porphyromonas gingivalis and Prevotella melaninogenica were significantly enriched, while the abundances of dental caries pathogens such as Streptococcus mutans and Streptococcus sobrinus were not significantly different in patients with T2DM compared to those in normal controls. Metabolomic analyses showed that the salivary levels of cadaverine and L-(+)-leucine of patients with T2DM were significantly higher than those of normal controls, while the supragingival plaque levels of N-acetyldopamine and 3,4-dimethylbenzoic acid in patients with T2DM were significantly higher than those in the normal controls. Additionally, we identified the types of oral microorganisms related to the changes in the levels of circulating metabolites, and the oral microorganisms were involved in the dysregulation of harmful metabolites such as cadaverine and n, n-dimethylarginine. Overall, our study first described the changes in the composition of oral microorganisms and their metabolites in patients with T2DM who have not suffered any oral diseases, which will provide a direct basis for finding oral biomarkers for early warning of oral diseases in T2DM. IMPORTANCE The incidence of oral diseases in type 2 diabetic patients might increase, and the severity might also be more serious. At present, the relationship between oral microorganisms and type 2 diabetes mellitus (T2DM) has become a hot topic in systemic health research. However, whether the function of oral microorganisms and their metabolites have changed in patients with T2DM who have not suffered from any oral diseases has not been reported. We found that even if the oral condition of T2DM is healthy, their oral microbes and metabolites have changed, thus increasing the risk of periodontal disease. Our study first described the changes in the composition of oral microorganisms and their metabolites in T2DM who have not suffered any oral diseases and revealed the correlation between oral microorganisms and their metabolites, which will provide a direct basis for finding oral biomarkers for early warning of oral diseases in patients with T2DM.

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.

The Voltammetric Detection of Cadaverine Using a Diamine Oxidase and Multi-Walled Carbon Nanotube Functionalised Electrochemical Biosensor

Cadaverine is a biomolecule of major healthcare importance in periodontal disease; however, current detection methods remain inefficient. The development of an enzyme biosensor for the detection of cadaverine may provide a cheap, rapid, point-of-care alternative to traditional measurement techniques. This work developed a screen-printed biosensor (SPE) with a diamine oxidase (DAO) and multi-walled carbon nanotube (MWCNT) functionalised electrode which enabled the detection of cadaverine via cyclic voltammetry and differential pulse voltammetry. The MWCNTs were functionalised with DAO using carbodiimide crosslinking with 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC) and N-Hydroxysuccinimide (NHS), followed by direct covalent conjugation of the enzyme to amide bonds. Cyclic voltammetry results demonstrated a pair of distinct redox peaks for cadaverine with the C-MWCNT/DAO/EDC-NHS/GA SPE and no redox peaks using unmodified SPEs. Differential pulse voltammetry (DPV) was used to isolate the cadaverine oxidation peak and a linear concentration dependence was identified in the range of 3-150 µg/mL. The limit of detection of cadaverine using the C-MWCNT/DAO/EDC-NHS/GA SPE was 0.8 μg/mL, and the biosensor was also found to be effective when tested in artificial saliva which was used as a proof-of-concept model to increase the Technology Readiness Level (TRL) of this device. Thus, the development of a MWCNT based enzymatic biosensor for the voltammetric detection of cadaverine which was also active in the presence of artificial saliva was presented in this study.

A Preliminary Pilot Study: Metabolomic Analysis of Saliva in Oral Candidiasis

Early detection of oral candidiasis is essential. However, most currently available methods are time-consuming and useful only for screening patients. Previous studies on the relationship between oral candidiasis and saliva have focused on saliva volume and not on its components. Therefore, to clarify the effects of oral candidiasis on salivary metabolites, the relationship between salivary components and oral candidiasis was investigated by comparing the salivary metabolites of oral candidiasis patients and those not previously diagnosed with candidiasis. Forty-five participants visiting our university hospital were included and classified into two groups, the Candida group and the control group, based on the Candida detection test results. The unstimulated saliva was collected using the spitting method over 15 min, and the stimulated saliva was collected using the gum-chewing method over 10 min. The saliva volume was measured, and the saliva samples were frozen and analyzed metabolomically. Metabolome analysis revealed 51 metabolites with peak detection rates exceeding 50%. There was no significant difference in age and sex between the Candida and control groups. In the Candida group, five metabolites (tyrosine, choline, phosphoenolpyruvate, histidine, and 6-phosphogluconate) were significantly elevated in the unstimulated, two (octanoic acid and uridine monophosphate(UMP)) were significantly increased, and four (ornithine, butyrate, aminovalerate and aminolevulinate) were significantly decreased in the stimulated saliva. This study suggests the possibility of identifying metabolites specific to patients with oral candidiasis, which could aid prompt diagnosis.

20 abnormal metabolites of Stage IV Grade C periodontitis was discovered by CPSI-MS

Saliva is a noninvasive biofluid that contains the metabolic signature of severe periodontitis (SP, Stage IV and Grade C). Conductive polymer spray ionization mass spectrometry (CPSI-MS) was used to record a wide range of metabolites within a few seconds, making this technique a promising point-of-care method for the early detection of SP (Stage IV and Grade C). Saliva samples from 31 volunteers, consisting of 16 healthy controls (HC) and 15 patients with SP (Stage IV and Grade C), were collected to identify dysregulated metabolites. Twenty metabolites were screened out, including seven amino acids. Moreover, the results showed that amino acid metabolism is closely related to the development of periodontitis. The present study further confirmed that salivary metabolites in the oral cavity were significantly altered after plaque removal. These results suggest that the combination of CPSI-MS is a feasible tool for preclinical screening of SP (Stage IV and Grade C).

Salivary parameters and periodontal inflammation in obstructive sleep apnoea patients

The aim of this cross-sectional study was to objectively assess the salivary flow rate and composition and periodontal inflammation in obstructive sleep apnoea (OSA) patients. The subjects, who underwent whole-night polysomnography or polygraphy, were referred for saliva sampling and periodontal examination. According to the severity of OSA based on the Apnoea Hypopnea Index (AHI) value, the subjects were classified into groups: no OSA (AHI < 5; N = 17), mild to moderate OSA (AHI 5-29.9; N = 109), and severe OSA (AHI > 30; N = 79). Salivary flow rate, pH, salivary electrolytes, and cortisol were measured from collected saliva samples. Periodontal examination included assessment of the number of teeth, dental plaque, bleeding on probing and periodontal measurements: gingival recession, probing pocket depth, clinical attachment level (CAL) and periodontal inflamed surface area (PISA) score. There were no significant differences in salivary flow rate, salivary pH, salivary electrolyte concentrations or electrolyte ratios among the groups classified according to the severity of OSA. However, subjects without OSA had higher salivary cortisol concentrations than OSA groups (p < 0.001). Increased plaque scores were associated with a higher AHI (r = 0.26; p = 0.003). According to the salivary flow rate, subjects with hyposalivation and reduced salivation had higher concentrations of salivary electrolytes and lower salivary pH than subjects with normal salivation. Subjects with hyposalivation had an increased Mg/PO₄ ratio (p < 0.001) and a reduced Ca/Mg ratio (p < 0.001). Furthermore, subjects with severe OSA tended to have higher CALs and plaque volumes. In conclusion, under pathological conditions, such as OSA, multiple interactions might impact salivary flow and electrolyte composition. Complex interrelationships might affect the integrity of oral health, especially considering OSA severity, inflammation, concomitant diseases and medications.

Metabolomic Profiles Associated with Obesity and Periodontitis during Pregnancy: Cross-Sectional Study with Proton Nuclear Magnetic Resonance (1H-NMR)-Based Analysis

This study aimed to elucidate the metabolomic signature associated with obesity and periodontitis during pregnancy in plasma and saliva biofluids. Ninety-eight pregnant women were divided into: with obesity and periodontitis (OP = 20), with obesity but without periodontitis (OWP = 27), with normal BMI but with periodontitis (NP = 21), with normal BMI and without periodontitis (NWP = 30). Saliva and plasma were analyzed by 1H-NMR for metabolites identification. Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA (sPLS-DA), and Variable Importance of Projection (VIP) were performed. ANOVA and Pearson’s correlation were applied (p < 0.05). Plasmatic analysis indicated the levels of glucose (p = 0.041) and phenylalanine (p = 0.015) were positively correlated with periodontal parameters and BMI, respectively. In saliva, periodontitis was mainly associated with high levels of acetic acid (p = 0.024), isovaleric acid, butyric acid, leucine, valine, isoleucine, and propionic acid (p < 0.001). High salivary concentrations of glycine (p = 0.015), succinic acid (p = 0.015), and lactate (p = 0.026) were associated with obesity. Saliva demonstrated a more elucidative difference than plasma, indicating the glucose-alanine cycle, alanine metabolism, valine, leucine and isoleucine degradation, glutamate metabolism, and Warburg effect as the main metabolic pathways.

Periodontal disease metabolomics signatures from different biofluids: a systematic review

BACKGROUND

Periodontitis is resulted from a complex interaction between genetics and epigenetics, microbial factors, and the host response. Metabolomics analyses reflect both the steady-state physiological equilibrium of cells or organisms as well as their dynamic metabolic responses to environmental stimuli.

AIM OF REVIEW

This systematic review of the literature aimed to assess which low molecular weight metabolites are more often found in biological fluids of individuals with periodontitis compared to individuals with gingivitis or periodontal health.

KEY SCIENTIFIC CONCEPTS OF REVIEW

All the included studies employed untargeted analysis. One or more biological fluids were analyzed, including saliva (n = 14), gingival crevicular fluid (n = 6), mouthwash (n = 1), serum (n = 3) and plasma (n = 1). Fifty-six main metabolites related to periodontitis have been identified in at least two independent studies by NMR spectroscopy or MS-based metabolomics. Saliva was the main biological fluid sampled. It is noteworthy that 14 metabolites of the 56 detected were identified as main metabolites in all studies that sampled the saliva. The majority of metabolites found consistently among studies were amino acids, organic acids and derivates: acetate, alanine, butyrate, formate, GABA, lactate, propionate, phenylalanine and valine. They were either up- or down-regulated in the studies or this information was not mentioned. The main metabolic pathway was related to phenylalanine, tyrosine and tryptophan biosynthesis. Metabolites more frequently found in individuals with periodontitis were related to both the host and to microorganism responses. Future studies are needed, and they should follow some methodological standards to facilitate their comparison.

Integrated analysis of the salivary microbiome and metabolome in chronic and aggressive periodontitis: A pilot study

This pilot study was designed to identify the salivary microbial community and metabolic characteristics in patients with generalized periodontitis. A total of 36 saliva samples were collected from 13 patients with aggressive periodontitis (AgP), 13 patients with chronic periodontitis (ChP), and 10 subjects with periodontal health (PH). The microbiome was evaluated using 16S rRNA gene high-throughput sequencing, and the metabolome was accessed using gas chromatography-mass spectrometry. The correlation between microbiomes and metabolomics was analyzed by Spearman’s correlation method. Our results revealed that the salivary microbial community and metabolite composition differed significantly between patients with periodontitis and healthy controls. Striking differences were found in the composition of salivary metabolites between AgP and ChP. The genera Treponema, Peptococcus, Catonella, Desulfobulbus, Peptostreptococcaceae_[XI] ([G-2], [G-3] [G-4], [G-6], and [G-9]), Bacteroidetes_[G-5], TM7_[G-5], Dialister, Eikenella, Fretibacterium, and Filifactor were present in higher levels in patients with periodontitis than in the healthy participants. The biochemical pathways that were significantly different between ChP and AgP included pyrimidine metabolism; alanine, aspartate, and glutamate metabolism; beta-alanine metabolism; citrate cycle; and arginine and proline metabolism. The differential metabolites between ChP and AgP groups, such as urea, beta-alanine, 3-aminoisobutyric acid, and thymine, showed the most significant correlations with the genera. These differential microorganisms and metabolites may be used as potential biomarkers to monitor the occurrence and development of periodontitis through the utilization of non-invasive and convenient saliva samples. This study reveals the integration of salivary microbial data and metabolomic data, which provides a foundation to further explore the potential mechanism of periodontitis.

Salivary biomarkers as key to monitor personalized oral healthcare and precision dentistry: A scoping review

Personalized Oral Healthcare has recently become the new trend word in medicine and dentistry. In this context, saliva diagnostics using various biomarkers seem to be the gateway to personalized dental diagnostics and therapy. But the terminology is not (yet) uniformly defined, furthermore it is unclear to what extent which salivary markers play a relevant role in the therapeutic decision making. In this Scoping Review, an electronic search was conducted in PubMed and Web of Science databases using medical subject headings (MESH terms) “saliva”, “biomarker”, “personality/persons”, and “dentistry”. Only human studies were included, in which repeated salivary measurements were performed to analyze monitoring effects with at least ten patients per group. PRISMA-ScR and Tricco guidelines were followed: (i) to examine what salivary biomarkers have been explored in terms of personalized oral healthcare and precision dentistry, (ii) to investigate the clinical relevance for oral health and its correlation to systemic health, and (iii) to summarize an outlook for future developments based on these results. Out of 899 studies, a total of 57 were included for data extraction in this Scoping Review, mainly focusing on periodontal therapy and patient monitoring. Salivary biomarkers have shown the potential to change the field of dentistry in all dental disciplines as a key for personalized workflows. The increasing interest in dental research is obvious, demonstrated by the growing number of publications in recent years. At this time, however, the predominant discipline is periodontology, which allows biomarker-based monitoring of the disease prevention and progression. The studies included showed heterogeneous methods using manifolds biomarkers. Therefore, no uniformly accepted concept can be presented today. Further clinical research with well-defined outcomes including standardized procedures is necessary.

Fusobacterium nucleatum Metabolically Integrates Commensals and Pathogens in Oral Biofilms

Fusobacterium nucleatum is a common constituent of the oral microbiota in both periodontal health and disease. Previously, we discovered ornithine cross-feeding between F. nucleatum and Streptococcus gordonii, where S. gordonii secretes ornithine via an arginine-ornithine antiporter (ArcD), which in turn supports the growth and biofilm development of F. nucleatum; however, broader metabolic aspects of F. nucleatum within polymicrobial communities and their impact on periodontal pathogenesis have not been addressed. Here, we show that when cocultured with S. gordonii, F. nucleatum increased amino acid availability to enhance the production of butyrate and putrescine, a polyamine produced by ornithine decarboxylation. Coculture with Veillonella parvula, another common inhabitant of the oral microbiota, also increased lysine availability, promoting cadaverine production by F. nucleatum. We confirmed that ArcD-dependent S. gordonii-excreted ornithine induces synergistic putrescine production, and mass spectrometry imaging revealed that this metabolic capability creates a putrescine-rich microenvironment on the surface of F. nucleatum biofilms. We further demonstrated that polyamines caused significant changes in the biofilm phenotype of a periodontal pathogen, Porphyromonas gingivalis, with putrescine accelerating the biofilm life cycle of maturation and dispersal. This phenomenon was also observed with putrescine derived from S. gordonii-F. nucleatum coculture. Lastly, analysis of plaque samples revealed cooccurrence of P. gingivalis with genetic modules for putrescine production by S. gordonii and F. nucleatum. Overall, our results highlight the ability of F. nucleatum to induce synergistic polyamine production within multispecies consortia and provide insight into how the trophic web in oral biofilm ecosystems can eventually shape disease-associated communities.

IMPORTANCE Periodontitis is caused by a pathogenic shift in subgingival biofilm ecosystems, which is accompanied by alterations in microbiome composition and function, including changes in the metabolic activity of the biofilm, which comprises multiple commensals and pathogens. While Fusobacterium nucleatum is a common constituent of the supra- and subgingival biofilms, its metabolic integration within polymicrobial communities and the impact on periodontal pathogenesis are poorly understood. Here, we report that amino acids supplied by other commensal bacteria induce polyamine production by F. nucleatum, creating polyamine-rich microenvironments. Polyamines reportedly have diverse functions in bacterial physiology and possible involvement in periodontal pathogenesis. We show that the F. nucleatum-integrated trophic network yielding putrescine from arginine through ornithine accelerates the biofilm life cycle of Porphyromonas gingivalis, a periodontal pathogen, from the planktonic state through biofilm formation to dispersal. This work provides insight into how cooperative metabolism within oral biofilms can tip the balance toward periodontitis.

Metabolomics Research in Periodontal Disease by Mass Spectrometry

Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

Serum Metabonomics Reveals Risk Factors in Different Periods of Cerebral Infarction in Humans

Studies of key metabolite variations and their biological mechanisms in cerebral infarction (CI) have increased our understanding of the pathophysiology of the disease. However, how metabolite variations in different periods of CI influence these biological processes and whether key metabolites from different periods may better predict disease progression are still unknown. We performed a systematic investigation using the metabonomics method. Various metabolites in different pathways were investigated by serum metabolic profiling of 143 patients diagnosed with CI and 59 healthy controls. Phe-Phe, carnitine C18:1, palmitic acid, cis-8,11,14-eicosatrienoic acid, palmitoleic acid, 1-linoleoyl-rac-glycerol, MAG 18:1, MAG 20:3, phosphoric acid, 5α-dihydrotestosterone, Ca, K, and GGT were the major components in the early period of CI. GCDCA, glycocholate, PC 36:5, LPC 18:2, and PA showed obvious changes in the intermediate time. In contrast, trans-vaccenic acid, linolenic acid, linoleic acid, all-cis-4,7,10,13,16-docosapentaenoic acid, arachidonic acid, DHA, FFA 18:1, FFA 18:2, FFA 18:3, FFA 20:4, FFA 22:6, PC 34:1, PC 36:3, PC 38:4, ALP, and Crea displayed changes in the later time. More importantly, we found that phenylalanine metabolism, medium-chain acylcarnitines, long-chain acylcarnitines, choline, DHEA, LPC 18:0, LPC 18:1, FFA 18:0, FFA 22:4, TG, ALB, IDBIL, and DBIL played vital roles in the development of different periods of CI. Increased phenylacetyl-L-glutamine was detected and may be a biomarker for CI. It was of great significance that we identified key metabolic pathways and risk metabolites in different periods of CI different from those previously reported. Specific data are detailed in the Conclusion section. In addition, we also explored metabolite differences of CI patients complicated with high blood glucose compared with healthy controls. Further work in this area may inform personalized treatment approaches in clinical practice for CI by experimentally elucidating the pathophysiological mechanisms.

Novel impacts of saliva with regard to oral health

The maintenance of balanced oral homeostasis depends on saliva. A readily available and molecularly rich source of biological fluid, saliva fulfills many functions in the oral cavity, including lubrication, pH buffering, and tooth mineralization. Saliva composition and flow can be modulated by different factors, including circadian rhythm, diet, age, drugs, and disease. Recent events have revealed that saliva plays a central role in the dissemination and detection of the SARS-CoV-2 coronavirus. A working knowledge of saliva function and physiology is essential for dental health professionals.

Salivary Microbial Dysbiosis Is Associated With Peri-Implantitis: A Case-Control Study in a Brazilian Population

Background and Objectives

The aim of this study was to examine the salivary microbiome in healthy peri-implant sites and those with peri-implantitis.

Methods

Saliva samples were collected from 21 participants with healthy peri-implant sites and 21 participants with peri-implantitis. The V4 hypervariable region of the 16S rRNA gene was sequenced using the Ion Torrent PGM System (Ion 318™ Chip v2 400). The NGS analysis and composition of the salivary microbiome were determined by taxonomy assignment. Downstream bioinformatic analyses were performed in QIIME (v 1.9.1).

Results

Clinical differences according to peri-implant condition status were found. Alpha diversity metrics revealed that the bacterial communities of participants with healthy peri-implant sites tended to have a richer microbial composition than individuals with peri-implantitis. In terms of beta diversity, bleeding on probing (BoP) may influence the microbial diversity. However, no clear partitioning was noted between the salivary microbiome of volunteers with healthy peri-implant sites or volunteers with peri-implantitis. The highest relative abundance of Stenotrophomonas, Enterococcus and Leuconostoc genus, and Faecalibacterium prausnitzii, Haemophilus parainfluenzae, Prevotella copri, Bacteroides vulgatus, and Bacteroides stercoris bacterial species was found in participants with peri-implantitis when compared with those with healthy peri-implant sites.

Conclusion

Differences in salivary microbiome composition were observed between patients with healthy peri-implant sites and those with peri-implantitis. BoP could affect the diversity (beta diversity) of the salivary microbiome.

Metabolomic Evaluation of Chronic Periodontal Disease in Older Adults

Periodontal disease is an infectious inflammatory disease related to the destruction of supporting tissues of the teeth, leading to a functional loss of the teeth. Inflammatory molecules present in the exudate are catalyzed and form different metabolites that can be identified and quantified. Thus, we evaluated the inflammatory exudate present in crevicular fluid to identify metabolic biological markers for diagnosing chronic periodontal disease in older adults. Research participants were selected from long-term institutions in Brazil. Participants were individuals aged 65 years or older, healthy, or with chronic periodontal disease. Gas chromatography/mass spectrometry was used to evaluate potential biomarkers in 120 crevicular fluid samples. We identified 969 metabolites in the individuals. Of these, 15 metabolites showed a variable importance with projection score > 1 and were associated with periodontal disease. Further analysis showed that among the 15 metabolites, two (5-aminovaleric acid and serine, 3TMS derivative) were found at higher concentrations in the crevicular fluid, indicating their potential diagnostic power for periodontal disease in older adults. Our findings indicated that some metabolites are present at high concentrations in the crevicular fluid in older adults with periodontal disease and can be used as biomarkers of periodontal disease.

Potential of Prebiotic D-Tagatose for Prevention of Oral Disease

Recent studies have shown phenotypic and metabolic heterogeneity in related species including Streptococcus oralis, a typical oral commensal bacterium, Streptococcus mutans, a cariogenic bacterium, and Streptococcus gordonii, which functions as an accessory pathogen in periodontopathic biofilm. In this study, metabolites characteristically contained in the saliva of individuals with good oral hygiene were determined, after which the effects of an identified prebiotic candidate, D-tagatose, on phenotype, gene expression, and metabolic profiles of those three key bacterial species were investigated. Examinations of the saliva metabolome of 18 systemically healthy volunteers identified salivary D-tagatose as associated with lower dental biofilm abundance in the oral cavity (Spearman’s correlation coefficient; r = -0.603, p = 0.008), then the effects of D-tagatose on oral streptococci were analyzed in vitro. In chemically defined medium (CDM) containing D-tagatose as the sole carbohydrate source, S. mutans and S. gordonii each showed negligible biofilm formation, whereas significant biofilms were formed in cultures of S. oralis. Furthermore, even in the presence of glucose, S. mutans and S. gordonii showed growth suppression and decreases in the final viable cell count in a D-tagatose concentration-dependent manner. In contrast, no inhibitory effects of D-tagatose on the growth of S. oralis were observed. To investigate species-specific inhibition by D-tagatose, the metabolomic profiles of D-tagatose-treated S. mutans, S. gordonii, and S. oralis cells were examined. The intracellular amounts of pyruvate-derived amino acids in S. mutans and S. gordonii, but not in S. oralis, such as branched-chain amino acids and alanine, tended to decrease in the presence of D-tagatose. This phenomenon indicates that D-tagatose inhibits growth of those bacteria by affecting glycolysis and its downstream metabolism. In conclusion, the present study provides evidence that D-tagatose is abundant in saliva of individuals with good oral health. Additionally, experimental results demonstrated that D-tagatose selectively inhibits growth of the oral pathogens S. mutans and S. gordonii. In contrast, the oral commensal S. oralis seemed to be negligibly affected, thus highlighting the potential of administration of D-tagatose as an oral prebiotic for its ability to manipulate the metabolism of those targeted oral streptococci.

Saliva and Plasma Reflect Metabolism Altered by Diabetes and Periodontitis

Periodontitis is an inflammatory disorder caused by disintegration of the balance between the periodontal microbiome and host response. While growing evidence suggests links between periodontitis and various metabolic disorders including type 2 diabetes (T2D), non-alcoholic liver disease, and cardiovascular disease (CVD), which often coexist in individuals with abdominal obesity, factors linking periodontal inflammation to common metabolic alterations remain to be fully elucidated. More detailed characterization of metabolomic profiles associated with multiple oral and cardiometabolic traits may provide better understanding of the complexity of oral-systemic crosstalk and its underlying mechanism. We performed comprehensive profiling of plasma and salivary metabolomes using untargeted gas chromatography/mass spectrometry to investigate multivariate covariation with clinical markers of oral and systemic health in 31 T2D patients with metabolic comorbidities and 30 control subjects. Orthogonal partial least squares (OPLS) results enabled more accurate characterization of associations among 11 oral and 25 systemic clinical outcomes, and 143 salivary and 78 plasma metabolites. In particular, metabolites that reflect cardiometabolic changes were identified in both plasma and saliva, with plasma and salivary ratios of (mannose + allose):1,5-anhydroglucitol achieving areas under the curve of 0.99 and 0.92, respectively, for T2D diagnosis. Additionally, OPLS analysis of periodontal inflamed surface area (PISA) as the numerical response variable revealed shared and unique responses of metabolomic and clinical markers to PISA between healthy and T2D groups. When combined with linear regression models, we found a significant correlation between PISA and multiple metabolites in both groups, including threonate, cadaverine and hydrocinnamate in saliva, as well as lactate and pentadecanoic acid in plasma, of which plasma lactate showed a predominant trend in the healthy group. Unique metabolites associated with PISA in the T2D group included plasma phosphate and salivary malate, while those in the healthy group included plasma gluconate and salivary adenosine. Remarkably, higher PISA was correlated with altered hepatic lipid metabolism in both groups, including higher levels of triglycerides, aspartate aminotransferase and alanine aminotransferase, leading to increased risk of cardiometabolic disease based on a score summarizing levels of CVD-related biomarkers. These findings revealed the potential utility of saliva for evaluating the risk of metabolic disorders without need for a blood test, and provide evidence that disrupted liver lipid metabolism may underlie the link between periodontitis and cardiometabolic disease.

Salivary Metabolomics for Diagnosis and Monitoring Diseases: Challenges and Possibilities

Saliva is a useful biological fluid and a valuable source of biological information. Saliva contains many of the same components that can be found in blood or serum, but the components of interest tend to be at a lower concentration in saliva, and their analysis demands more sensitive techniques. Metabolomics is starting to emerge as a viable method for assessing the salivary metabolites which are generated by the biochemical processes in elucidating the pathways underlying different oral and systemic diseases. In oral diseases, salivary metabolomics has concentrated on periodontitis and oral cancer. Salivary metabolites of systemic diseases have been investigated mostly in the early diagnosis of different cancer, but also neurodegenerative diseases. This mini-review article aims to highlight the challenges and possibilities of salivary metabolomics from a clinical viewpoint. Furthermore, applications of the salivary metabolic profile in diagnosis and prognosis, monitoring the treatment success, and planning of personalized treatment of oral and systemic diseases are discussed.

Evaluation of the ability of the trypsin-like peptidase activity assay to detect severe periodontitis

OBJECTIVES

N-benzoyl-DL-arginine peptidase (trypsin-like peptidase) is specifically produced by certain strains of periodontitis-associated bacteria. We aimed to examine the effectiveness of an objectively quantified trypsin-like peptidase activity assay (TLP-AA) for detecting severe periodontitis.

METHODS

The study population included 347 adults (108 men and 239 women; average age, 43.3 years) who underwent a full-mouth periodontal examination. Specimens for the TLP-AA were obtained using tongue swabs. Using a color reader, the TLP-AA results were obtained as a* values, with higher positive a* values indicating an increased intense enzymatic activity. The predictive validity of the TLP-AA results for severe periodontitis was assessed using receiver operating characteristic curve analysis and the periodontitis case definition provided by the Centers for Disease Control and Prevention/American Academy of Periodontology as the gold standard. Furthermore, multivariable logistic regression analyses were performed to predict severe periodontitis using the TLP-AA results and health characteristics, as the exposure variables.

RESULTS

Severe periodontitis was observed in 5.2% of the participants. TLP-AA had high diagnostic accuracy for severe periodontitis, with an area under the curve of 0.83 (95% confidence interval [CI]: 0.75-0.92). The cut-off score for the a* value that best differentiated individuals with severe periodontitis was 0.09, with a sensitivity of 83% and specificity of 77%. Multivariable logistic regression analyses revealed that the TLP-AA results were significantly associated with severe periodontitis after adjusting for health characteristics (adjusted odds ratios: 1.90 [95% CI: 1.37-2.62] for the a* value).

CONCLUSIONS

Objectively quantified TLP-AA results are potentially useful for detecting severe periodontitis in epidemiological surveillance.

Proteomics, lipidomics, metabolomics and 16S DNA sequencing of dental plaque from patients with diabetes and periodontal disease

Oral microbiome influences human health, specifically prediabetes and type 2 diabetes (Pre-DM/DM) and periodontal diseases (PDs), through complex microbial interactions. To explore these relations, we performed 16S rDNA sequencing, metabolomics, lipidomics, and proteomics analyses on supragingival dental plaque collected from individuals with Pre-DM/DM (n = 39), Pre-DM/DM and PD (n = 37), PD alone (n = 11), or neither (n = 10). We identified on average 2790 operational taxonomic units and 2025 microbial and host proteins per sample and quantified 110 metabolites and 415 lipids. Plaque samples from Pre-DM/DM patients contained higher abundance of Fusobacterium and Tannerella than plaques from metabolically healthy patients. Phosphatidylcholines, plasmenyl phosphatidylcholines, ceramides containing non-OH fatty acids, and host proteins related to actin filament rearrangement were elevated in plaques from PD versus non-PD samples. Cross-omic correlation analysis enabled the detection of a strong association between Lautropia and monomethyl phosphatidylethanolamine (PE-NMe), which is striking because synthesis of PE-NMe is uncommon in oral bacteria. Lipidomics analysis of in vitro cultures of Lautropia mirabilis confirmed the synthesis of PE-NMe by the bacteria. This comprehensive analysis revealed a novel microbial metabolic pathway and significant associations of host-derived proteins with PD.

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Patients with periodontal disease or diabetes have unique microbial dysbiosis.

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Proteomics and 16S data provide complementary information about microbial diversity.

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Cross-omic correlation reveals host signatures associated with periodontal disease.

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Multi-omic data lead to finding about microbially synthesized lipids.

Salivary metabolites associated with a 5-year tooth loss identified in a population-based setting

BACKGROUND

Periodontitis is among the most common chronic diseases worldwide, and it is one of the main reasons for tooth loss. Comprehensive profiling of the metabolite content of the saliva can enable the identification of novel pathways associated with periodontitis and highlight non-invasive markers to facilitate time and cost-effective screening efforts for the presence of periodontitis and the prediction of tooth loss.

METHODS

We first investigated cross-sectional associations of 13 oral health variables with saliva levels of 562 metabolites, measured by untargeted mass spectrometry among a sub-sample (n = 938) of the Study of Health in Pomerania (SHIP-2) using linear regression models adjusting for common confounders. We took forward any candidate metabolite associated with at least two oral variables, to test for an association with a 5-year tooth loss over and above baseline oral health status using negative binomial regression models.

RESULTS

We identified 84 saliva metabolites that were associated with at least one oral variable cross-sectionally, for a subset of which we observed robust replication in an independent study. Out of 34 metabolites associated with more than two oral variables, baseline saliva levels of nine metabolites were positively associated with a 5-year tooth loss. Across all analyses, the metabolites 2-pyrrolidineacetic acid and butyrylputrescine were the most consistent candidate metabolites, likely reflecting oral dysbiosis. Other candidate metabolites likely reflected tissue destruction and cell proliferation.

CONCLUSIONS

Untargeted metabolic profiling of saliva replicated metabolic signatures of periodontal status and revealed novel metabolites associated with periodontitis and future tooth loss.

Analysis of Salivary Metabolites by Nuclear Magnetic Resonance Before and After Oral Mucosa Cleaning of Infants in the Pre-dental Period

The aim of the present study was to verify if a protocol for cleaning the oral cavity of infants in the pre-dental period can reduce extrinsic salivary metabolites observed through Nuclear Magnetic Resonance (NMR). A cross-sectional clinical study with a convenience sample was conducted, and infants were recruited at the UFRJ Pediatric Dentistry Clinic. Participants who had used antibiotics and/or antifungals up to 3 months before and whose legal guardians did not consent or sign the Informed Consent Form were excluded. An anamnesis was performed with the guardians and the participants' intraoral clinical examination. Initial collection of unstimulated total saliva was performed using an automatic pipette with sterile plastic tips in the buccal floor region, at least 1 h after the last feeding. Subsequently, the infants' oral mucosa was cleaned with gauze moistened with filtered water, and after 5 min, a new collection was performed, using the same methodology. The obtained samples were immediately transferred on ice to the laboratory, centrifuged (10,000 g), and stored at −80°C. The NMR analyses were performed using a 500-MHz spectrometer Bruker, Germany); evaluations were done via the 1H and 1H-1H TOCSY spectra for metabolite signaling. Eleven pre-dental infants were evaluated, with a mean age of 3.8 months, including six girls (55%). Of these, nine participants (82%) were exclusively breastfed. The higher presence of components such as lactose, glucose, sugars, acetate, alanine, and lactate were observed in the samples before oral mucosa cleaning. Regarding the type of diet, more lactose was observed in the saliva of patients who were exclusively breastfed than those that received mixed feeding. We conclude that the oral mucosa cleaning of infants in the pre-dental period tends to reduce the concentration of extrinsic components from the diet, such as lactose, in the salivary metabolomic profile analyzed by NMR.

Resolving the Contradictory Functions of Lysine Decarboxylase and Butyrate in Periodontal and Intestinal Diseases

Periodontal disease is a common, bacterially mediated health problem worldwide. Mastication (chewing) repeatedly traumatizes the gingiva and periodontium, causing traces of inflammatory exudate, gingival crevicular fluid (GCF), to appear in crevices between the teeth and gingiva. Inadequate tooth cleaning causes a dentally adherent microbial biofilm composed of commensal salivary bacteria to appear around these crevices where many bacteria grow better on GCF than in saliva. We reported that lysine decarboxylase (Ldc) from Eikenella corrodens depletes the GCF of lysine by converting it to cadaverine and carbon dioxide. Lysine is an amino acid essential for the integrity and continuous renewal of dentally attached epithelium acting as a barrier to microbial products. Unless removed regularly by oral hygiene, bacterial products invade the lysine-deprived dental attachment where they stimulate inflammation that enhances GCF exudation. Cadaverine increases and supports the development of a butyrate-producing microbiome that utilizes the increased GCF substrates to slowly destroy the periodontium (dysbiosis). A long-standing paradox is that acid-induced Ldc and butyrate production support a commensal (probiotic) microbiome in the intestine. Here, we describe how the different physiologies of the respective tissues explain how the different Ldc and butyrate functions impact the progression and control of these two chronic diseases.

Untargeted and targeted gingival metabolome in rodents reveal metabolic links between high-fat diet-induced obesity and periodontitis

AIM

To characterize gingival metabolome in high-fat diet (HFD)-induced obesity in mice with/without periodontitis.

METHODS

HFD-induced obesity mouse model was established by 16-week feeding, and a lean control group was fed with low-fat diet (n = 21/group). Both models were induced for periodontitis on the left sides by molar ligation for 10 days, whereas the right sides were used as controls. Gingival metabolome and arginine metabolism were analysed by non-targeted/targeted liquid chromatography-mass spectrometry.

RESULTS

Of 2247 reference features, presence of periodontitis altered 165 in lean versus 885 in HFD mice; and HFD altered 525 in absence versus 1435 in presence of periodontitis. Compared with healthy condition, periodontitis and HFD had distinct effects on gingival metabolome. Metabolomic impacts of periodontitis were generally greater in HFD mice versus lean controls. K-medoids clustering showed that HFD amplified the impacts of periodontitis on gingival metabolome in both intensity and extensity. Ten metabolic pathways were enriched, including 2 specific to periodontitis, 5 specific to HFD and 3 shared ones. Targeted validation on arginine metabolism confirmed the additive effects between HFD and periodontitis.

CONCLUSION

The obese population consuming excessive HFD display amplified metabolic response to periodontitis, presenting a metabolic susceptibility to exacerbated periodontal destruction.

Emerging intraoral biosensors

Biomedical devices that involved continuous and real-time health-care monitoring have drawn much attention in modern medicine, of which skin electronics and implantable devices are widely investigated. Skin electronics are characterized for their non-invasive access to the physiological signals, and implantable devices are superior at the diagnosis and therapy integration. Despite the significant progress achieved, many gaps remain to be explored to provide a more comprehensive overview of human health. As the connecting point of the outer environment and human systems, the oral cavity contains many unique biomarkers that are absent in skin or inner organs, and hence, this could become a promising alternative locus for designing health-care monitoring devices. In this review, we outline the status of the oral cavity during the communication of the environment and human systems and compare the intraoral devices with skin electronics and implantable devices from the biophysical and biochemical aspects. We further summarize the established diagnosis database and technologies that could be adopted to design intraoral biosensors. Finally, the challenges and potential opportunities for intraoral biosensors are discussed. Intraoral biosensors could become an important complement for existing biomedical devices to constitute a more reliable health-care monitoring system.

Polyamine biomarkers as indicators of human disease

The significant increase of periodontitis, chronic kidney disease (CKD), Alzheimer's disease and cancer can be attributed to an ageing population. Each disease produces a range of biomarkers that can be indicative of disease onset and progression. Biomarkers are defined as cellular (intra/extracellular components and whole cells), biochemical (metabolites, ions and toxins) or molecular (nucleic acids, proteins and lipids) alterations which are measurable in biological media such as human tissues, cells or fluids. An interesting group of biomarkers that merit further investigation are the polyamines. Polyamines are a group of molecules consisting of cadaverine, putrescine, spermine and spermidine and have been implicated in the development of a range of systemic diseases, in part due to their production in periodontitis. Cadaverine and putrescine within the periodontal environment have demonstrated cell signalling interfering abilities, by way of leukocyte migration disruption. The polyamines spermine and spermidine in tumour cells have been shown to inhibit cellular apoptosis, effectively prolonging tumorigenesis and continuation of cancer within the host. Polyamine degradation products such as acrolein have been shown to exacerbate renal damage in CKD patients. Thus, the use of such molecules has merit to be utilized in the early indication of such diseases in patients.

Salivary metabolomics for the diagnosis of periodontal diseases: a systematic review with methodological quality assessment

INTRODUCTION

Early diagnosis of periodontitis by means of a rapid, accurate and non-invasive method is highly desirable to reduce the individual and epidemiological burden of this largely prevalent disease.

OBJECTIVES

The aims of the present systematic review were to examine potential salivary metabolic biomarkers and pathways associated to periodontitis, and to assess the accuracy of salivary untargeted metabolomics for the diagnosis of periodontal diseases.

METHODS

Relevant studies identified from MEDLINE (PubMed), Embase and Scopus databases were systematically examined for analytical protocols, metabolic biomarkers and results from the multivariate analysis (MVA). Pathway analysis was performed using the MetaboAnalyst online software and quality assessment by means of a modified version of the QUADOMICS tool.

RESULTS

Twelve studies met the inclusion criteria, with sample sizes ranging from 19 to 130 subjects. Compared to periodontally healthy individuals, valine, phenylalanine, isoleucine, tyrosine and butyrate were found upregulated in periodontitis patients in most studies; while lactate, pyruvate and N-acetyl groups were the most significantly expressed in healthy individuals. Metabolic pathways that resulted dysregulated are mainly implicated in inflammation, oxidative stress, immune activation and bacterial energetic metabolism. The findings from MVA revealed that periodontitis is characterized by a specific metabolic signature in saliva, with coefficients of determination ranging from 0.52 to 0.99.

CONCLUSIONS

This systematic review summarizes candidate metabolic biomarkers and pathways related to periodontitis, which may provide opportunities for the validation of diagnostic or predictive models and the discovery of novel targets for monitoring and treating such a disease (PROSPERO CRD42020188482).

Changes in the Salivary Metabolic Profile of Generalized Periodontitis Patients after Non-surgical Periodontal Therapy: A Metabolomic Analysis Using Nuclear Magnetic Resonance Spectroscopy

Pattern analysis of the salivary metabolic profile has been proven accurate in discriminating between generalized periodontitis (GP) patients and healthy individuals (HI), as this disease modifies the salivary concentrations of specific metabolites. Due to the scarcity of data from previous studies, this study aimed to evaluate if non-surgical periodontal therapy (NST) could affect the metabolomic profile in GP patients’ saliva and if it compares to that of HI. Unstimulated salivary samples were collected from 11 HI and 12 GP patients before and 3 months after NST. Nuclear Magnetic Resonance (NMR) spectroscopy, followed by a supervised multivariate statistical approach on entire saliva spectra and partial least square (PLS) discriminant analysis, were performed to obtain metabolic profiles. In the GP group, periodontal treatment improved all clinical parameters, but not all the diseased sites were eradicated. PLS revealed an accuracy of 100% in distinguishing between metabolic profiles of GP patients before and after NST. Orthogonal projection to latent structure was able to discriminate between the three groups of subjects with an accuracy of 85.6%. However, the post-NST metabolic profile of GP patients could not be completely assimilated to that of HI. Although NST may produce significant changes in the metabolic profile, GP patients maintained a distinctive fingerprint compared to HI.

Gingival fibroblasts dynamically reprogram cellular metabolism during infection of Porphyromonas gingivalis

OBJECTIVE

The purpose of the present study was to explore the sequential changes in the cellular metabolism in gingival fibroblasts (GFs) in response toPorphyromonas gingvalis (P. gingivalis) ATCC33277 infection.

DESIGN

GFs were treated withP. gingivalis at the MOI of 50 for 4, 24 and 48 h to mimic the early, medium, and late phase in the bacterial infection. LDH assay and cell counting kit-8 were utilized to explore cell death and proliferation. Real-time PCR was utilized to explore the gene transcription of pro-inflammatory genes. The relative levels of biomolecules in GFs were measured by gas chromatography-mass spectrometry. Principal component analysis and orthogonal partial least-squares-discriminant analysis were performed to visualize the metabolic difference among experimental groups. In addition, pathway analysis was conducted regarding differential metabolites in GFs.

RESULTS

P. gingivalis infection triggered significant gene transcription of IL-1β, IL 6, MCP 1, and MMP 1 in GFs. In addition, P. gingivalis stimulated cell proliferation of GFs at MOI of 10, 50 and 250. Moreover, P. gingivalis triggered significant cell death at higher MOI. 69, 173 and 148 metabolites were qualitatively detected at 4, 24 and 48 h after P. gingivalis infection respectively in GFs, showing a sequential change of different phase. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that ATP-binding cassette transporters, glutathione, purine and pyrimidine metabolism was significantly altered in different phase.

CONCLUSIONS

Human GFs may sequentially rewire metabolomics to shape the inflammatory responses and support the proliferation of host cells during P. gingivalis infection.