Microbial and metabolomic analysis of gingival crevicular fluid in general chronic periodontitis patients: lessons for a predictive, preventive, and personalized medical approach

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.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

Relationship between vaginal and oral microbiome in patients of human papillomavirus (HPV) infection and cervical cancer

BACKGROUND

The aim of this study was to assess the microbial variations and biomarkers in the vaginal and oral environments of patients with human papillomavirus (HPV) and cervical cancer (CC) and to develop novel prediction models.

MATERIALS AND METHODS

This study included 164 samples collected from both the vaginal tract and oral subgingival plaque of 82 women. The participants were divided into four distinct groups based on their vaginal and oral samples: the control group (Z/KZ, n = 22), abortion group (AB/KAB, n = 17), HPV-infected group (HP/KHP, n = 21), and cervical cancer group (CC/KCC, n = 22). Microbiota analysis was conducted using full-length 16S rDNA gene sequencing with the PacBio platform.

RESULTS

The vaginal bacterial community in the Z and AB groups exhibited a relatively simple structure predominantly dominated by Lactobacillus. However, CC group shows high abundances of anaerobic bacteria and alpha diversity. Biomarkers such as Bacteroides, Mycoplasma, Bacillus, Dialister, Porphyromonas, Anaerococcus, and Prevotella were identified as indicators of CC. Correlations were established between elevated blood C-reactive protein (CRP) levels and local/systemic inflammation, pregnancy, childbirth, and abortion, which contribute to unevenness in the vaginal microenvironment. The altered microbial diversity in the CC group was confirmed by amino acid metabolism. Oral microbial diversity exhibited an inverse pattern to that of the vaginal microbiome, indicating a unique relationship. The microbial diversity of the KCC group was significantly lower than that of the KZ group, indicating a link between oral health and cancer development. Several microbes, including Fusobacterium, Campylobacter, Capnocytophaga, Veillonella, Streptococcus, Lachnoanaerobaculum, Propionibacterium, Prevotella, Lactobacillus, and Neisseria, were identified as CC biomarkers. Moreover, periodontal pathogens were associated with blood CRP levels and oral hygiene conditions. Elevated oral microbial amino acid metabolism in the CC group was closely linked to the presence of pathogens. Positive correlations indicated a synergistic relationship between vaginal and oral bacteria.

CONCLUSION

HPV infection and CC impact both the vaginal and oral microenvironments, affecting systemic metabolism and the synergy between bacteria. This suggests that the use of oral flora markers is a potential screening tool for the diagnosis of CC.

[Relationship between short-chain fatty acids in the gingival crevicular fluid and periodontitis of stage Ⅲ or Ⅳ]

OBJECTIVE

To analyze the concentration of formic acid, propionic acid and butyric acid in gingival crevicular fluid (GCF) of patients with stages Ⅲ and Ⅳ periodontitis, and their relationship with periodontitis.

METHODS

The study enrolled 37 systemically healthy patients with periodontitis and 19 healthy controls who visited Department of Periodontology, Peking University School and Hospital of Stomatology from February 2008 to May 2011. Their GCFs were collected from the mesial-buccal site of one molar or incisor in each quadrant. Periodontal clinical parameters, including plaque index(PLI), probing depth(PD), bleeding index(BI), and attachment loss(AL). Concentrations of formic acid, propionic acid and butyric acid in the supernatant of the GCFs were analyzed by high-performance capillary electrophoresis (HPCE). The prediction ability of formic acid, propionic acid and butyric acid with the risk of periodontitis and the differences between grade B and grade C periodontitis were analyzed.

RESULTS

In this study, 32 patients with stage Ⅲ and 5 patients with stage Ⅳ were enrolled, including 9 patients with grade B and 28 patients with grade C. Clinical periodontal variables in the patients with periodontitis were significantly higher than those in the control group (P<0.001). Formic acid was significantly lower in periodontitis than that in the control group [5.37 (3.39, 8.49) mmol/L vs. 12.29 (8.35, 16.57) mmol/L, P<0.001]. Propionic acid and butyric acid in periodontitis were significantly higher than those in the control group: Propionic acid, 10.23 (4.28, 14.90) mmol/L vs. 2.71 (0.00, 4.25) mmol/L, P < 0.001; butyric acid, 2.63 (0.47, 3.81) mmol/L vs. 0.00 (0.00, 0.24) mmol/L, P<0.001. There was no significant difference in formic acid, propionic acid and butyric acid concentrations between grade B and grade C periodontitis (P>0.05). Propionic acid and butyric acid in the deep pocket were significantly higher than in the shallow pocket, while the concentration of formic acid decreased with the increase of PD. Propionic acid (OR=1.51, 95%CI: 1.29-1.75) and butyric acid (OR=3.72, 95%CI: 1.93-7.17) were risk factors for periodontitis, while formic acid (OR=0.87, 95%CI: 0.81-0.93) might be a protective factor for periodontitis. Propionic acid (AUC=0.852, 95%CI: 0.805－0.900), butyric acid (AUC=0.889, 95%CI: 0.841－0.937), f (formic acid, AUC=0.844, 95%CI: 0.793－0.895) demonstrated a good predictive capacity for the risk of periodontitis.

CONCLUSION

The concentration of formic acid decrease in the GCF of periodontitis patients, which is a protective factor for periodontitis, its reciprocal have good predictive capacity. However, propionic acid and butyric acid increase, which are risk factors for periodontitis and have good predictive capacity. The concentration of formic acid, propionic acid, and butyric acid vary with probing depth, but there is no significant difference between grade B and grade C periodontitis.

Profiles of subgingival microbiomes and gingival crevicular metabolic signatures in patients with amnestic mild cognitive impairment and Alzheimer's disease

BACKGROUND

The relationship between periodontitis and Alzheimer's disease (AD) has attracted more attention recently, whereas profiles of subgingival microbiomes and gingival crevicular fluid (GCF) metabolic signatures in AD patients have rarely been characterized; thus, little evidence exists to support the oral-brain axis hypothesis. Therefore, our study aimed to characterize both the microbial community of subgingival plaque and the metabolomic profiles of GCF in patients with AD and amnestic mild cognitive impairment (aMCI) for the first time.

METHODS

This was a cross-sectional study. Clinical examinations were performed on all participants. The microbial community of subgingival plaque and the metabolomic profiles of GCF were characterized using the 16S ribosomal RNA (rRNA) gene high-throughput sequencing and liquid chromatography linked to tandem mass spectrometry (LC-MS/MS) analysis, respectively.

RESULTS

Thirty-two patients with AD, 32 patients with aMCI, and 32 cognitively normal people were enrolled. The severity of periodontitis was significantly increased in AD patients compared with aMCI patients and cognitively normal people. The 16S rRNA gene sequencing results showed that the relative abundances of 16 species in subgingival plaque were significantly correlated with cognitive function, and LC-MS/MS analysis identified a total of 165 differentially abundant metabolites in GCF. Moreover, multiomics Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO) analysis revealed that 19 differentially abundant metabolites were significantly correlated with Veillonella parvula, Dialister pneumosintes, Leptotrichia buccalis, Pseudoleptotrichia goodfellowii, and Actinomyces massiliensis, in which galactinol, sn-glycerol 3-phosphoethanolamine, D-mannitol, 1 h-indole-1-pentanoic acid, 3-(1-naphthalenylcarbonyl)- and L-iditol yielded satisfactory accuracy for the predictive diagnosis of AD progression.

CONCLUSIONS

This is the first combined subgingival microbiome and GCF metabolome study in patients with AD and aMCI, which revealed that periodontal microbial dysbiosis and metabolic disorders may be involved in the etiology and progression of AD, and the differential abundance of the microbiota and metabolites may be useful as potential markers for AD in the future.

The interplay between gingival crevicular fluid microbiome and metabolomic profile in intensively treated people with type 1 diabetes - a combined metagenomic/metabolomic approach cross-sectional study

Aims

This study aimed to assess the gingival crevicular fluid (GCF) microbiome and metabolome of adults with type 1 diabetes (T1D) treated with continuous subcutaneous insulin infusion (CSII).

Methods

In this cross-sectional study, the GCF of adults with T1D treated with CSII and non-diabetic controls were sampled, and metagenomic/metabolomic analyses were performed.

Results

In total, 65 participants with T1D and 45 healthy controls with a mean age of 27.05 ± 5.95 years were investigated. There were 22 cases of mild gingivitis (G) in the T1D group. There were no differences considering the Shannon and Chao indices and β-diversity between people with T1D and G, with T1D without G, and healthy controls. Differential taxa were identified, which were mainly enriched in people with T1D and G. Acetic acid concentration was higher in people with T1D, regardless of the presence of G, than in healthy controls. Propionic acid was higher in people with T1D and G than in healthy controls. Isobutyric and isovaleric acid levels were higher in individuals with T1D and G than in the other two subgroups. The concentration of valeric acid was lower and that of caproic acid was higher in people with T1D (regardless of gingival status) than in healthy controls.

Conclusions

The identification of early changes in periodontal tissues by targeting the microbiome and metabolome could potentially enable effective prevention and initial treatment of periodontal disease in people with T1D.

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

NAGS, CPS1, and SLC25A13 (Citrin) at the Crossroads of Arginine and Pyrimidines Metabolism in Tumor Cells

Urea cycle enzymes and transporters collectively convert ammonia into urea in the liver. Aberrant overexpression of carbamylphosphate synthetase 1 (CPS1) and SLC25A13 (citrin) genes has been associated with faster proliferation of tumor cells due to metabolic reprogramming that increases the activity of the CAD complex and pyrimidine biosynthesis. N-acetylglutamate (NAG), produced by NAG synthase (NAGS), is an essential activator of CPS1. Although NAGS is expressed in lung cancer derived cell lines, expression of the NAGS gene and its product was not evaluated in tumors with aberrant expression of CPS1 and citrin. We used data mining approaches to identify tumor types that exhibit aberrant overexpression of NAGS, CPS1, and citrin genes, and evaluated factors that may contribute to increased expression of the three genes and their products in tumors. Median expression of NAGS, CPS1, and citrin mRNA was higher in glioblastoma multiforme (GBM), glioma, and stomach adenocarcinoma (STAD) samples compared to the matched normal tissue. Median expression of CPS1 and citrin mRNA was higher in the lung adenocarcinoma (LUAD) sample while expression of NAGS mRNA did not differ. High NAGS expression was associated with an unfavorable outcome in patients with glioblastoma and GBM. Low NAGS expression was associated with an unfavorable outcome in patients with LUAD. Patterns of DNase hypersensitive sites and histone modifications in the upstream regulatory regions of NAGS, CPS1, and citrin genes were similar in liver tissue, lung tissue, and A549 lung adenocarcinoma cells despite different expression levels of the three genes in the liver and lung. Citrin gene copy numbers correlated with its mRNA expression in glioblastoma, GBM, LUAD, and STAD samples. There was little overlap between NAGS, CPS1, and citrin sequence variants found in patients with respective deficiencies, tumor samples, and individuals without known rare genetic diseases. The correlation between NAGS, CPS1, and citrin mRNA expression in the individual glioblastoma, GBM, LUAD, and STAD samples was very weak. These results suggest that the increased cytoplasmic supply of either carbamylphosphate, produced by CPS1, or aspartate may be sufficient to promote tumorigenesis, as well as the need for an alternative explanation of CPS1 activity in the absence of NAGS expression and NAG.

Evaluation of the Oral Bacterial Genome and Metabolites in Patients with Wolfram Syndrome

In Wolfram syndrome (WFS), due to the loss of wolframin function, there is increased ER stress and, as a result, progressive neurodegenerative disorders, accompanied by insulin-dependent diabetes. The aim of the study was to evaluate the oral microbiome and metabolome in WFS patients compared with patients with type 1 diabetes mellitus (T1DM) and controls. The buccal and gingival samples were collected from 12 WFS patients, 29 HbA1c-matched T1DM patients (p = 0.23), and 17 healthy individuals matched by age (p = 0.09) and gender (p = 0.91). The abundance of oral microbiota components was obtained by Illumina sequencing the 16S rRNA gene, and metabolite levels were measured by gas chromatography–mass spectrometry. Streptococcus (22.2%), Veillonella (12.1%), and Haemophilus (10.8%) were the most common bacteria in the WFS patients, while comparisons between groups showed significantly higher abundance of Olsenella, Dialister, Staphylococcus, Campylobacter, and Actinomyces in the WFS group (p < 0.001). An ROC curve (AUC = 0.861) was constructed for the three metabolites that best discriminated WFS from T1DM and controls (acetic acid, benzoic acid, and lactic acid). Selected oral microorganisms and metabolites that distinguish WFS patients from T1DM patients and healthy individuals may suggest their possible role in modulating neurodegeneration and serve as potential biomarkers and indicators of future therapeutic strategies.

Can the Correlation of Periodontopathies with Gastrointestinal Diseases Be Used as Indicators in Severe Colorectal Diseases?

Gastrointestinal problems are among the most common health problems which can acutely affect the healthy population and chronically involve health risks, seriously affecting the quality of life. Identifying the risk of gastrointestinal diseases in the early phase by indirect methods can increase the healing rate and the quality of life.: The proposal of this study is to verify a correlation between gastrointestinal and periodontal problems and the risk of inflammatory gastrointestinal diseases (IBD). The study was conducted on 123 people who were observed to have gastrointestinal and psychological problems. The participants were divided into three groups, depending on each one's diagnosis. The control group (CG) was composed of 37 people who did not fit either irritable bowel syndrome (IBS) according to the ROME IV criteria, nor were inflammatory markers positive for IBD. Group 2 (IBS) was composed of 44 participants diagnosed with IBS according to the ROME IV criteria. Group 3 was composed of 42 participants who were diagnosed with IBD. All study participants underwent anthropometric, micro-Ident, and quality of life tests. A directly proportional relationship of the presence of bacteria with IBD patients with the exception of Capnocytophaga spp. and Actinobacillus actinomycetemcomitans was observed. These two bacteria correlated significantly with IBS. Follow-up of the study participants will help determine whether periodontal disease can be used as an indicator of severe colorectal disease. In addition, this study should be continued especially in the case of IBD more thoroughly to follow and reduce the risk of malignancy.

Applications of Mass Spectrometry in Dentistry

Mass Spectrometry (MS) is one of the fastest-developing methods in analytical instrumentation. As a highly sensitive, universal detector, it can identify known and unknown compounds, which can indeed be found in a minimal concentration. This review aims to highlight the significant milestones in MS applications in dentistry during recent decades. MS can be applied in three different fields of dentistry: (1) in research of dental materials and chemical agents, (2) in laboratory analysis of biospecimens, and (3) as a real-time diagnostic tool in service of oral surgery and pathology. MS applications on materials and agents may focus on numerous aspects, such as their clinical behavior, possible toxicity, or antimicrobial properties. MS is also a valuable, non-invasive tool for biomarkers' detection in saliva and has found great application in -omics technologies as it achieves efficient structure-finding in metabolites. As metabolites are located beyond the central dogma, this technique can provide a complete understanding of cellular functions. Thus, it is possible to determine the biological profile in normal and pathological conditions, detect various oral or systematic diseases and conditions, and predict their course. Lastly, some promising advances concerning the surgical approach to potentially oral malignant or malignant disorders exist. This breakthrough method provides a comprehensive approach to dental materials research and biomarker discovery in dental and craniofacial tissues. The current availability of various 'OMIC' approaches paves the way for individualized dentistry and provides suggestions for clinical applications in the point-of-care hubs.

Microbiome, alveolar bone, and metabolites: Connecting the dots

The oral microbiome (OM) is a diverse and dynamic collection of species, separated from alveolar bone by the oral mucosa. Pathogenic shifts in the OM (dysbiosis) during periodontitis are associated with an inflammatory response in the oral mucosa that drives alveolar bone resorption. Alveolar bone is also affected by metabolic disorders such as osteoporosis. Accumulating evidence has linked another microbial community, the gut microbiome (GM), to systemic bone metabolism and osteoporosis. Underlying this connection is the biologic activity of metabolites, byproducts of host and bacterial activity. Limited evidence also suggests that metabolites in the oral cavity signal between the OM and immune system, influencing both alveolar bone homeostasis and pathologic bone destruction in periodontitis. While the oral cavity and gut are connected through the gastrointestinal tract, dissimilar roles for known metabolites between these two niches exemplify the difficulty in translating knowledge on gut-derived metabolites and bone metabolism to alveolar bone. Integrated metabolomic, transcriptomic, and metagenomic approaches hold promise for resolving these challenges and identifying novel metabolites which impact alveolar bone health. Further interrogation through mechanistic testing in pre-clinical models and carefully controlled clinical studies have potential to lead toward translation of these discoveries into meaningful therapies.

The key players of dysbiosis in Noma disease; A systematic review of etiological studies

Noma is a rapidly progressing periodontal disease with up to 90% mortality in developing countries. Poor, immunocompromised and severely malnourished children (2 to 6 years old) are mostly affected by Noma. Prevention and effective management of Noma is hindered by the lack of sufficient cohesive studies on the microbial etiology of the disease. Research efforts have not provided a comprehensive unified story of the disease. Bridging the gap between existing studies gives an insight on the disease pathogenesis. This current systematic review of etiological studies focuses on the key players of dysbiosis in Noma disease. This review was performed in accordance with the Preferred Reporting Items for Systemic review and Meta-Analyses (PRISMA) statement. Web of Science, MEDLINE via PubMed, Cochrane Library, Scopus, and Science Direct were searched electronically for clinical trials which applied culture dependent or molecular techniques to identify oral microbiota from Noma patients. Trials which involved periodontal diseases except Noma were excluded. After screening 275 articles, 153 full-texts articles were assessed for eligibility of which eight full text articles were selected for data extraction and analysis. The results show that 308 samples from 169 Noma participants (6 months to 15 years old) have been used in clinical trials. There was some variance in the microbiome identified due to the use of 3 different types of samples (crevicular fluid, subgingival plaque, and swabbed pus) and the ambiguity of the stage or advancement of Noma in the studies. Other limitations of the studies included in this review were: the absence of age-matched controls in some studies; the constraints of colony morphology as a tool in distinguishing between virulent fusobacterium genus at the species level; the difficulty in culturing spirochaetes in the laboratory; the choice of primers in DNA amplification; and the selection of probe sets in gene sequencing. This systematic review highlights spirochaetes and P. intermedia as putative trigger organisms in Noma dysbiosis, shows that F. nucleatum promotes biofilms formation in late stages of the disease and suggests that future studies should be longitudinal, with high throughput genome sequencing techniques used with gingival plaque samples from early stages of Noma.

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.

Relationship between microRNA expression and inflammatory factors in patients with both type 2 diabetes mellitus and periodontal disease

OBJECTIVE

To investigate the expression of single-stranded microRNAs (miRNAs) in serum and gingival crevicular fluid of patients with type 2 diabetes mellitus (T2DM) complicated by periodontal disease and its correlation with inflammatory factors.

METHODS

Twenty-six periodontitis patients without diabetes mellitus (periodontal group), 24 patients with T2DM (T2DM group), 22 patients with both T2DM and periodontal disease (comorbid group), and 25 healthy individuals without a history of periodontal disease (healthy group) were recruited respectively. Serum and gingival crevicular fluid specimens were collected to detect the expression levels of miRNAs and inflammatory factors including serum tumor necrosis factor-α (TNF-α), interleukin-10 (IL-10), and transforming growth factor-β (TGF-β), and their correlations were also investigated.

RESULTS

Eleven miRNAs were detected in the gingival crevicular tissue of all subjects. The expression of miR-223 and miR-200b in serum and gingival crevicular fluid was elevated higher in the comorbid group than in the other three groups (P<0.05), and their expressions in gingival crevicular fluid contributed to the differential diagnosis of periodontal disease from diabetes mellitus comorbid with periodontal disease (P<0.05). Gingival crevicular fluid miR-223 expression was positively associated with TNF-α, clinical attachment loss (CAL), and probing pocket depth (PPD) in the periodontal group, while negatively associated with IL-10 (P<0.05), and so was gingival crevicular fluid miR-200b expression with TNF-α, CAL, and PPD (P<0.05). In the comorbid group, gingival crevicular fluid miR-223 expression showed a positive correlation with TNF-α, CAL, and PPD (P<0.05), and so did gingival crevicular fluid miR-200b expression with TNF-α, CAL, and PPD (P<0.05).

CONCLUSIONS

Our findings indicate a close link between the levels of miR-223 and miR-200b in serum and gingival crevicular fluid and susceptibility to T2DM as well as the pathogenesis of periodontal disease.

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.

Systems Biology in Periodontitis

Systems biology is a promising scientific discipline that allows an integrated investigation of host factors, microbial composition, biomarkers, immune response and inflammatory mediators in many conditions such as chronic diseases, cancer, neurological disorders, and periodontitis. This concept utilizes genetic decoding, bioinformatic, flux-balance analysis in a comprehensive approach. The aim of this review is to better understand the current literature on systems biology and identify a clear applicability of it to periodontitis. We will mostly focus on the association between this condition and topics such as genomics, transcriptomics, proteomics, metabolomics, as well as contextualize delivery systems for periodontitis treatment, biomarker detection in oral fluids and associated systemic conditions.

The Role of Epigenetic and Biological Biomarkers in the Diagnosis of Periodontal Disease: A Systematic Review Approach

The aim of this systemic review was to collate and analyze existing data from published literature sources to identify the current understanding of the role of epigenetic and biological biomarkers in periodontal disease and diagnostics. A comprehensive searching strategy was undertaken in Embase, Medline, The Dentistry and Oral Sciences and CINAHL databases. Grey literature searching strategies were also employed. Articles published in the English language between 2017−2020 were included. A total of 1014 studies were returned of which 15 studies were included. All included articles were cross-sectional, case−control studies. Relevant data were extracted according to various demographic and methodological factors including cohort size, oral biofluid sampled, number of examiners, smoking status and reported outcomes. A measure of the biomarker levels and corresponding significance were documented where possible. This review identified that exRNA has the greatest diagnostic potential, with four biomarkers (SPRR1A, lnc-TET3-2:1, FAM25A, CRCT1) displaying sensitivity of >71% and specificity of 100% in the assessed samples (p < 0.001) for gingivitis. This work also identifies the need for a unified approach to future research to draw meaningful comparison. Further investigations are warranted to definitively validate exRNA data and for the development of an exRNA-specific point-of-care diagnostic test.

SMDI: An Index for Measuring Subgingival Microbial Dysbiosis

An intuitive, clinically relevant index of microbial dysbiosis as a summary statistic of subgingival microbiome profiles is needed. Here, we describe a subgingival microbial dysbiosis index (SMDI) based on machine learning analysis of published periodontitis/health 16S microbiome data. The raw sequencing data, split into training and test sets, were quality filtered, taxonomically assigned to the species level, and centered log-ratio transformed. The training data set was subject to random forest analysis to identify discriminating species (DS) between periodontitis and health. DS lists, compiled by various "Gini" importance score cutoffs, were used to compute the SMDI for samples in the training and test data sets as the mean centered log-ratio abundance of periodontitis-associated species subtracted by that of health-associated ones. Diagnostic accuracy was assessed with receiver operating characteristic analysis. An SMDI based on 49 DS provided the highest accuracy with areas under the curve of 0.96 and 0.92 in the training and test data sets, respectively, and ranged from -6 (most normobiotic) to 5 (most dysbiotic) with a value around zero discriminating most of the periodontitis and healthy samples. The top periodontitis-associated DS were Treponema denticola, Mogibacterium timidum, Fretibacterium spp., and Tannerella forsythia, while Actinomyces naeslundii and Streptococcus sanguinis were the top health-associated DS. The index was highly reproducible by hypervariable region. Applying the index to additional test data sets in which nitrate had been used to modulate the microbiome demonstrated that nitrate has dysbiosis-lowering properties in vitro and in vivo. Finally, 3 genera (Treponema, Fretibacterium, and Actinomyces) were identified that could be used for calculation of a simplified SMDI with comparable accuracy. In conclusion, we have developed a nonbiased, reproducible, and easy-to-interpret index that can be used to identify patients/sites at risk of periodontitis, to assess the microbial response to treatment, and, importantly, as a quantitative tool in microbiome modulation studies.

Fixed Prosthetic Restorations and Periodontal Health: A Narrative Review

Periodontal health plays an important role in the longevity of prosthodontic restorations. The issues of comparative assessment of prosthetic constructions are complicated and not fully understood. The aim of this article is to review and present the current knowledge regarding the various technical, clinical, and molecular aspects of different prosthetic biomaterials and highlight the interactions between periodontal health and prosthetic restorations. Articles on periodontal health and fixed dental prostheses were searched using the keywords “zirconium”, “CAD/CAM”, “dental ceramics”, “metal–ceramics”, “margin fit”, “crown”, “fixed dental prostheses”, “periodontium”, and “margin gap” in PubMed/Medline, Scopus, Google Scholar, and Science Direct. Further search criteria included being published in English, and between January 1981 and September 2021. Then, relevant articles were selected, included, and critically analyzed in this review. The margin of discrepancy results in the enhanced accumulation of dental biofilm, microleakage, hypersensitivity, margin discoloration, increased gingival crevicular fluid flow (GCF), recurrent caries, pulp infection and, lastly, periodontal lesion and bone loss, which can lead to the failure of prosthetic treatment. Before starting prosthetic treatment, the condition of the periodontal tissues should be assessed for their oral hygiene status, and gingival and periodontal conditions. Zirconium-based restorations made from computer-aided design and computer-aided manufacturing (CAD/CAM) technology provide better results, in terms of marginal fit, inflammation reduction, maintenance, and the restoration of periodontal health and oral hygiene, compared to constructions made by conventional methods, and from other alloys. Compared to subgingival margins, supragingival margins offer better oral hygiene, which can be maintained and does not lead to secondary caries or periodontal disease.

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.

Metagenomic and Metatranscriptomic Insight Into Oral Biofilms in Periodontitis and Related Systemic Diseases

The oral microbiome is one of the most complex microbial communities in the human body and is closely related to oral and systemic health. Dental plaque biofilms are the primary etiologic factor of periodontitis, which is a common chronic oral infectious disease. The interdependencies that exist among the resident microbiota constituents in dental biofilms and the interaction between pathogenic microorganisms and the host lead to the occurrence and progression of periodontitis. Therefore, accurately and comprehensively detecting periodontal organisms and dissecting their corresponding functional activity characteristics are crucial for revealing periodontitis pathogenesis. With the development of metagenomics and metatranscriptomics, the composition and structure of microbial communities as well as the overall functional characteristics of the flora can be fully profiled and revealed. In this review, we will critically examine the currently available metagenomic and metatranscriptomic evidence to bridge the gap between microbial dysbiosis and periodontitis and related systemic diseases.

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.

Is There a Causal Link Between Periodontitis and Cardiovascular Disease? A Concise Review of Recent Findings

There is substantial evidence in support of an association between periodontitis and cardiovascular disease. The most important open question related to this association is causality. This article revisits the question of causality by reviewing intervention studies and systematic reviews and meta analyses published in the last 3 years. Where are we now in answering this question? Whilst systematic reviews and epidemiological studies continue to support an association between the diseases, intervention studies fall short in determining causality. There is a dearth of good-quality, blinded randomised control trials with cardiovascular disease outcomes. Most studies use surrogate markers/biomarkers for endpoints, and this is problematic as they may not be reflective of cardiovascular disease status. This review further highlights another issue with surrogate markers/biomarkers: the potential for collider bias. Ethical considerations surrounding nontreatment have led to calls for a well-annotated database containing in-depth dental health data. Finally, a relatively new and important risk factor for cardiovascular disease, clonal haematopoiesis of indeterminate potential, is discussed. Clonal haematopoiesis of indeterminate potential increases cardiovascular risk by more than 40%, and inflammation is a contributing factor. The impact of periodontal disease on this emerging risk factor has yet to be explored. Although the question of causality in the association between periodontal disease and cardiovascular disease remains unanswered, the importance of good oral health in maintaining good heart health is reiterated.

Structure and Function of Oral Microbial Community in Periodontitis Based on Integrated Data

Objective

Microorganisms play a key role in the initiation and progression of periodontal disease. Research studies have focused on seeking specific microorganisms for diagnosing and monitoring the outcome of periodontitis treatment. Large samples may help to discover novel potential biomarkers and capture the common characteristics among different periodontitis patients. This study examines how to screen and merge high-quality periodontitis-related sequence datasets from several similar projects to analyze and mine the potential information comprehensively.

Methods

In all, 943 subgingival samples from nine publications were included based on predetermined screening criteria. A uniform pipeline (QIIME2) was applied to clean the raw sequence datasets and merge them together. Microbial structure, biomarkers, and correlation network were explored between periodontitis and healthy individuals. The microbiota patterns at different periodontal pocket depths were described. Additionally, potential microbial functions and metabolic pathways were predicted using PICRUSt to assess the differences between health and periodontitis.

Results

The subgingival microbial communities and functions in subjects with periodontitis were significantly different from those in healthy subjects. Treponema, TG5, Desulfobulbus, Catonella, Bacteroides, Aggregatibacter, Peptostreptococcus, and Eikenella were periodontitis biomarkers, while Veillonella, Corynebacterium, Neisseria, Rothia, Paludibacter, Capnocytophaga, and Kingella were signature of healthy periodontium. With the variation of pocket depth from shallow to deep pocket, the proportion of Spirochaetes, Bacteroidetes, TM7, and Fusobacteria increased, whereas that of Proteobacteria and Actinobacteria decreased. Synergistic relationships were observed among different pathobionts and negative relationships were noted between periodontal pathobionts and healthy microbiota.

Conclusion

This study shows significant differences in the oral microbial community and potential metabolic pathways between the periodontitis and healthy groups. Our integrated analysis provides potential biomarkers and directions for in-depth research. Moreover, a new method for integrating similar sequence data is shown here that can be applied to other microbial-related areas.

Bacteriological Evaluation of Gingival Crevicular Fluid in Teeth Restored Using Fixed Dental Prostheses: An In Vivo Study

The present in vivo study determined the microbiological counts of the gingival crevicular fluid (GCF) among patients with fixed dental prostheses fabricated using three different techniques. A total of 129 subjects were divided into three study groups: first, cobalt-chrome-based, metal-ceramic prostheses fabricated by the conventional method (MC, n = 35); the second group consisted of cobalt-chrome-based, metal-ceramic prostheses fabricated by the computer-aided design and computer-aided manufacturing (CAD/CAM) technique (CC-MC, n = 35); the third group comprised zirconia-based ceramic prostheses fabricated using the CAD/CAM technique (CC-Zr, n = 35). The control consisted of 24 patients using prostheses fabricated with either MC, CC-MC, or CC-Zr. The GCF was obtained from the subjects before treatment, and 6 and 12 months after the prosthetic treatment. Bacteriological and bacterioscopic analysis of the GCF was performed to analyze the patients’ GCF. The data were analyzed using SPSS V20 (IBM Company, Chicago, IL, USA). The number of microorganisms of the gingival crevicular fluid in all groups at 12 months of prosthetic treatment reduced dramatically compared with the data obtained before prosthetic treatment. Inflammatory processes in the periodontium occurred slowly in the case of zirconium oxide-based ceramic constructions due to their biocompatibility with the mucous membranes and tissues of the oral cavity as well as a reduced risk of dental biofilm formation. This should be considered by dentists and prosthodontists when choosing restoration materials for subjects with periodontal pathology.

Periodontal health related-inflammatory and metabolic profiles of patients with end-stage renal disease: potential strategy for predictive, preventive, and personalized medicine

Objectives

To compare the periodontal health related–inflammatory and metabolic differences between patients with end-stage renal disease (ESRD) and healthy controls (HC), and to identify potential biomarkers in gingival crevicular fluid (GCF) and serum of ESRD patients for predictive, preventive, and personalized medicine (PPPM).

Methods

Patients with ESRD (ESRD group; n = 52) and healthy controls (HC group; n = 44) were recruited. Clinical periodontal parameters were recorded. The differential metabolites in the GCF and serum were identified by liquid chromatography/mass spectrometry (LC/MS). Inflammatory markers including interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and C-reactive protein (CRP) were also assessed.

Results

In the ESRD group, IL-8 and CRP were significantly higher in GCF, whereas IL-6 and CRP were significantly higher in serum, compared with HC group (all P < 0.05). In the case of GCF, taurine levels were positively correlated with IL-8 levels in both groups (all P < 0.05). In the case of serum, l-phenylalanine and p-hydroxyphenylacetic acid levels were positively correlated with CRP levels in both groups (all P < 0.05). Significant positive correlations were observed between metabolites (including pseudouridine, l-phenylalanine, and p-hydroxyphenylacetic acid) and IL-6 levels only in ESRD group.

Conclusions

IL-8 and CRP are potential inflammatory makers that reflect the periodontal health of ESRD individual, which may be considered the valuable predictive diagnostics in the agreement with PPPM philosophy. Besides, metabolites of taurine in GCF as well as l-phenylalanine and p-hydroxyphenylacetic acid in serum are possible biomarkers correlated with inflammatory markers. All these biomarkers may also be highly recommended as a novel predictive/diagnostic tool for the assessment of inflammatory status from the perspectives of PPPM in view of susceptible population and individual screening.

Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach

An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the “participatory” medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population.

Metabolomics of gingival crevicular fluid to identify biomarkers for periodontitis: A systematic review with meta-analysis

The present systematic review aimed to examine periodontitis-specific biomarkers in the gingival crevicular fluid (GCF) that could have a diagnostic relevance, and to provide a qualitative assessment of the current literature. Metabolites are reliable indicators of pathophysiological statuses, and their quantification in the GCF can provide an outlook of the changes associated with periodontitis and have diagnostic value. Relevant studies identified from PubMed, Embase, Cochrane Library, and Scopus databases were examined to answer the following PECO question: "In systemically healthy individuals, can concentration of specific metabolites in the GCF be used to discriminate subjects with healthy periodontium (H) or gingivitis from patients with periodontitis (P) and which is the diagnostic accuracy?" Quality of included studies was rated using a modified version of the QUADOMICS tool. Meta-analysis was conducted whenever possible. After the screening of 1,554 titles, 15 studies were selected, with sample size ranging from 30 to 93 subjects. Eleven studies performed targeted metabolomics analysis and provided data for 10 metabolites. Among the most consistent markers, malondialdehyde levels were found higher in the P group compared with H group (SMD = 2.86; 95% CI: 1.64, 4.08). Also, a significant increase of 8-hydroxy-deoxyguanosine, 4-hydroxynonenal, and neopterin was detected in periodontally diseased sites, while glutathione showed an inverse trend. When considering data from untargeted metabolomic analysis in four studies, more than 40 metabolites were found significantly discriminant, mainly related to amino acids and lipids degradation pathways. Notably, only one study reported measures of diagnostic accuracy. Several metabolites were differentially expressed in GCF of subjects across different periodontal conditions, having a major potential for investigating periodontal pathophysiology and for site-specific diagnosis. Oxidative stress-related molecules, such as malondialdehyde and 8-hydroxy-deoxyguanosine, were the most consistently associated to periodontitis (PROSPERO CRD42020188482).

Gingival Crevicular Fluid (GCF): A Diagnostic Tool for the Detection of Periodontal Health and Diseases

The methodologies applicable for the evaluation of periodontal associated diseases are constantly evolving to provide quick, realistic, and scientifically proven results. Trends in the past followed a clinical evaluation of periodontal tissues and radiographic-based reports that formed the foundation for detection of diseases involving the structures supporting the teeth. As the confines and limitations of conventional strategies became obvious over the passage of time, hand in hand variety of techniques have evolved and experimentally justified. These improvisations are based on an improved understanding of the periodontal-pathogenic cascade. Periodontal pathogenesis and a paradigm shift from disease understanding to disease prevention and treatment entail few prerequisites that demand the objectivity of diagnostics procedure that includes sensitivity and specificity along with an explanation of the intensity of the disease, Gingival crevicular fluid an oral bio-fluid resides in the close proximity with gingival tissues have been widely used to understand and differentiate the periodontal health and diseased status. The biomarkers present in the GCF can be a reliable tool to detect the minute changes seen in the disease processes. The GCF consists of various host and bacterial-derived products as well as biomarkers which in turn can be evaluated for the diagnosis, prognosis as well as management of the periodontal disease. Thus, the review aims at describing GCF as a potential oral biofluid helpful in differentiating periodontal health and disease status.

Label-free analysis of gingival crevicular fluid (GCF) by surface enhanced Raman scattering (SERS)

Gingival crevicular fluid (GCF) is an interesting biofluid reflecting the physiological and pathological states of a single dental element. Due to this unique feature, in recent years, metabolomic analysis of GCF has gained attention as a biometric tool for the diagnosis and therapy of periodontal disease. Traditional methods are, however, too slow, cumbersome and expensive for a health-care routine. Surface enhanced Raman scattering (SERS) can offer rapid and label-free detailed molecular fingerprints that can be used for biofluid analysis. Here we report the first SERS characterization of GCF using an easy and quick sample preparation. The dominant features in the SERS spectrum of GCF are ascribed to very few metabolites, in particular to uric acid, hypoxanthine, glutathione and ergothioneine. Additionally, we succeeded in differentiating between the SERS signal of GCF collected from healthy volunteers and the one collected from patients with periodontal disease.

Natural Killer-Like B Cells Secreting Interleukin-18 Induces a Proinflammatory Response in Periodontitis

Natural killer-like B (NKB) cells, which are newly identified immune subsets, reveal a critical immunoregulatory property in the eradication of microbial infection via the secretion of interleukin (IL)-18. For the first time, this study investigated the role of NKB cells in secreting IL-18 in the pathogenesis of periodontitis. In this study, NKB cells' percentage and IL-18 concentration in peripheral blood and periodontium in periodontitis patients was measured using flow cytometry and ELISA. The role of IL-18 in regulating periodontal inflammation was examined in a Porphyromonas gingivalis (P. gingivalis)-induced periodontitis murine model. Peripheral and periodontal-infiltrating CD3^-CD19⁺NKp46⁺ NKB cells, which were the main source of IL-18, were elevated and correlated with attachment loss in periodontitis patients. In vitro IL-18 stimulation promoted proinflammatory cytokine production in periodontal ligament cells. P. gingivalis infection induced elevation of IL-18 receptor in periodontium in a periodontitis murine model. IL-18 neutralization not only suppressed P. gingivalis-induced alveolar bone resorption, but also inhibited recruitment of antigen-non-specific inflammatory cells into the periodontium, probably via dampening expressions of cytokines, chemokines, and matrix metalloproteinases. NKB cells secreting IL-18 appeared to be an important mediator in the inflammatory response following intraoral P. gingivalis infection. These findings might be relevant to the development of immunotherapies for periodontitis.

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.