Background: MiRNAs have emerged as minimally invasive biomarkers with considerable potential for the early detection of oral squamous cell carcinoma (OSCC). Although numerous studies have evaluated circulating miRNAs across different biofluids, the comparative diagnostic performance of saliva-, serum-, and plasma-derived miRNAs has not been systematically clarified. Methods: A meta-analysis was performed by screening PubMed, MEDLINE, Scopus, CINAHL, and related databases. Nineteen eligible studies evaluating miRNA-based assays in saliva, serum, or plasma were included. A random-effects bivariate model was used to calculate pooled sensitivity, specificity, and area under the HSROC curve. Meta-regression using log diagnostic odds ratio (lnDOR) examined whether biofluid type significantly influenced diagnostic performance. Results: Salivary miRNAs showed a pooled sensitivity of 0.76 (95% CI: 0.68–0.82; I² = 84.69%), specificity of 0.79 (95% CI: 0.70–0.85; I² = 70.41%), and an AUC of 0.84 (95% CI: 0.80–0.87). Plasma miRNAs produced comparable results with a pooled sensitivity of 0.77 (95% CI: 0.61–0.88; I² = 90.45%), specificity of 0.79 (95% CI: 0.63–0.89; I² = 80.20%), and an AUC of 0.85 (95% CI: 0.81–0.89). Serum-derived miRNAs demonstrated the highest accuracy with a pooled sensitivity of 0.82 (95% CI: 0.70–0.90; I² = 76.92%), specificity of 0.88 (95% CI: 0.75–0.95; I² = 74.87%), and an AUC of 0.91 (95% CI: 0.89–0.94). Despite serum’s numerically superior performance, meta-regression revealed no significant matrix effect (Wald χ² = 0.20, p = 0.903). Conclusions: Although serum-derived miRNAs performed best overall, biofluid type was not a statistically significant determinant of diagnostic performance.

Background: Oral squamous cell carcinoma (OSCC) remains a major global health burden due to delayed diagnosis. Although salivary biomarkers have been explored in previous meta-analyses, these studies were limited to specific biomarker types. Methods: This study followed PRISMA guidelines and was registered in PROSPERO (CRD 420261296936). PubMed, Scopus, MEDLINE, and CINAHL were searched for diagnostic accuracy studies of salivary biomarkers for OSCC. Studies providing sufficient data to construct 2 × 2 tables were included. Pooled sensitivity, specificity, DOR, and HSROC curves were estimated using a bivariate random-effects model, and study quality was assessed using QUADAS-2. Results: Eighteen studies comprising 1647 participants yielded 45 diagnostic datasets across multiple biomarker classes. The pooled sensitivity and specificity were 0.64 (95% CI: 0.59–0.69) and 0.71 (95% CI: 0.66–0.76), respectively. The pooled DOR was 4.53 (95% CI: 3.18–6.47), indicating moderate discriminatory ability, with an AUC of 0.75 (95% CI: 0.71–0.79). Fagan’s nomogram analysis demonstrated that these biomarkers are not suitable for screening the general population and should be reserved for enriched populations (pre-test probability > 10%). Conclusions: Salivary biomarkers demonstrate moderate but highly heterogeneous diagnostic accuracy. Clinical utility is context-dependent and limited to enriched populations with a baseline probability of OSCC >10%. In screening the general population (prevalence < 0.01%), these tests offer no significant clinical utility. They should be considered complementary triage tools rather than definitive diagnostic modalities.

Background/Objective: Orthodontic mini-implants (MI) create new niches that may alter the oral microbiota and modulate host immune responses. While clinical inflammation is not always evident, microbial and molecular changes may precede visible signs of peri-implant infection. This study investigated microbial shifts and inflammatory responses following MI placement, with a focus on Saccharibacteria, nitrate-reducing bacteria (NRB), and periodontopathogens. Methods: Saliva and peri mini-implant crevicular fluid (PMICF) samples were collected from eight orthodontic patients at baseline (T0), one week (T1), and one month (T2) after mini-implant placement. DNA was extracted from each saliva and PMICF sample and pooled across the eight patients for each time point. The pooled DNA were then subjected to 16S rRNA gene sequencing using the Oxford Nanopore MinION platform. Statistical analysis was performed to determine shifts in bacterial abundance, diversity, and co-occurrence patterns across the different sample types (saliva vs. PMICF) and time points. Results: Alpha diversity decreased in PMICF at T2, while it remained stable in saliva samples. Periodontopathogens (Porphyromonas gingivalis, Treponema denticola, Fusobacterium nucleatum) increased in PMICF at T2, while NRB and Saccharibacteria, along with a representative host bacterium (Schaalia odontolytica), remained relatively stable. Co-occurrence analysis showed antagonistic relationships between Saccahribacteria/NRB and periodontopathogens. IL-6 significantly decreased from T1 to T2, while CRP showed a non-significant downward trend. The expression of nitrate reductase genes narG and napA remained stable across time intervals. Conclusions: Despite no clinical inflammation, MI placement led to localized microbial shift and mild inflammatory responses. NRB and Saccharibacteria’s stability and antagonistic relationship to periodontopathogens may indicate that they could be involved in maintaining microbial homeostasis. These findings highlight possible early biomarkers and ecological strategies to support oral health in MI patients.

Background Despite diabetes mellitus and periodontal diseases are mutually exclusive, little is known about particular types of bacteria that may have exacerbated the development of diabetics’ periodontal inflammation. The purpose of this study was to descriptively characterize and explore the differences in the salivary microbiomes of individuals with type 2 diabetes (20-40 years old) who had gingivitis or periodontitis to those who did not. Additionally, we evaluated the descriptive relationship between the relative abundance of periodontopathogens and nitrate-reducing bacteria in their salivary microbiome. Methods Saliva was collected from all participants. Genomic DNA was isolated and pooled in equimolar quantities from all individuals within each group to create three pooled libraries: type 2 diabetes (T2DM) patients without periodontal disease (G1), T2DM patients with gingivitis (G2), and T2DM patients with periodontitis (G3). Sequencing was performed using Oxford Nanopore MinION Technology. The relative abundance and bacterial diversity were measured using bioinformatic methods, and all analyses of sequencing data were strictly descriptive and exploratory. Salivary nitrite/nitrate concentrations were measured on individual, un-pooled samples. Results The salivary microbiota among people with type 2 diabetes and periodontal disease (G2; G3) was observed to have greater bacterial diversity and abundance than that of patients without periodontal disease (G1), according to descriptive alpha-diversity analysis. The G3 group exhibited the largest relative abundance of Porphyromonas gingivalis , a key periodontopathogen. Descriptive analysis also suggested that periodontopathic bacteria and nitrate-reducing bacteria have different community structures across the groups. Furthermore, comparison of individual salivary samples showed that nitrite/nitrate concentration was significantly lower in the G3 group compared to the G1 group (p< 0.05). Results reveal that the inverse interaction between increased P. gingivalis and decreased nitrate-reducing bacteria serves as a descriptive hallmark for periodontal disease progression in the T2DM population. Conclusion Results of this exploratory study suggest that the relationship between periodontopathic and denitrifying bacteria in the salivary microbiome varies among those with type 2 diabetes mellitus who also have gingivitis or periodontitis. These distinct microbial features observed may be microbiological characteristics associated with the progression of periodontal disease in this population, warranting further validation as potential indicators for early management.

Background Despite diabetes mellitus and periodontal diseases are mutually exclusive, little is known about particular types of bacteria that may have exacerbated the development of diabetics’ periodontal inflammation. This study’s aim was to compare the salivary microbiomes of individuals with type 2 diabetes (20–40 years old) who had gingivitis or periodontitis to those who did not. Additionally, we evaluated the relationship between the number of periodontopathogens and the amount of nitrate-reducing bacteria in their salivary microbiome. Methods Saliva was collected, DNA was isolated, the entire 16S ribosomal RNA gene was amplified, and sample libraries were prepared in accordance to the Oxford Nanopore MinION Technology procedure. The relative abundance and bacterial diversity in saliva samples that were pooled according to three groups; T2DM patients without periodontal disease (G1), T2DM patients with gingivitis (G2), and T2DM patients with periodontitis (G3), was measured using bioinformatic methods. Additionally, the relationships between the periodontopathic bacteria (Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Fusobacterium spp.) and denitrifying community (Haemophilus, Neisseria, Rothia, and Veillonella) were assessed. Results Alpha-diversity analysis revealed, the G1 group had significantly lower bacterial diversity and abundance than groups G2 and G3 (p< 0.0001). However, the microbiota profiles of diabetic patient groups with periodontitis and gingivitis were comparable. Using receiver operating characteristic (ROC) analysis, potential biomarkers for differentiating between gingivitis and periodontitis were discovered. Areas under the curve (AUC) between Fusobacterium spp. and Neisseria were found to be 0.94 (p = 0.43), while the AUC between P. gingivalis and Rothia was not significant (0.84, p = 0.08). Conclusion People with type 2 diabetes mellitus who also have gingivitis or periodontitis exhibit different relationships between periodontopathic and denitrifying bacteria in their salivary microbiome. These features might be essential indicators for early identification and treatment of gingivitis in order to prevent periodontitis.

Background Despite diabetes mellitus and periodontal diseases are mutually exclusive, little is known about particular types of bacteria that may have exacerbated the development of diabetics’ periodontal inflammation. The purpose of this study was to compare the salivary microbiomes of individuals with type 2 diabetes (20–40 years old) who had gingivitis or periodontitis to those who did not. Additionally, we evaluated the relationship between the number of periodontopathogens and the amount of nitrate-reducing bacteria in their salivary microbiome. Methods Saliva was collected, DNA was isolated, the entire 16S ribosomal RNA gene was amplified, and sample libraries were prepared in accordance to the Oxford Nanopore MinION Technology procedure. The relative abundance and bacterial diversity in saliva samples that were pooled according to three groups; T2DM patients without periodontal disease (G1), T2DM patients with gingivitis (G2), and T2DM patients with periodontitis (G3), was measured using bioinformatic methods. Additionally, the relationships between the periodontopathic bacteria ( Porphyromonas gingivalis , Treponema denticola , Tannerella forsythia , and Fusobacterium spp.) and denitrifying community ( Haemophilus , Neisseria , Rothia , and Veillonella ) were assessed. Results The salivary microbiota among people with type 2 diabetes and periodontal disease (gingivitis, G2; periodontitis, G3) showed more bacterial diversity and abundance than that of patients without periodontal disease (G1), according to alpha-diversity analysis (p < 0.0001). The G3 group exhibited the largest abundance of Porphyromonas gingivalis , a periodontopathogen. Additionally, we discovered that periodontopathic bacteria and nitrate-reducing bacteria have different interactions. Moreover, receiver operating characteristic (ROC) analysis demonstrated that, with a high Area Under the Curve (AUC) of 0.93 (p = 0.04), the connection between Fusobacterium spp. and Neisseria was a powerful indicator for distinguishing between gingivitis and periodontitis. Conclusion Results of this study show that the connection between periodontopathic and denitrifying bacteria in the salivary microbiome varies among those with type 2 diabetes mellitus who also have gingivitis or periodontitis. These features may be crucial markers for the early detection and management of gingivitis to avoid periodontitis.