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

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

SUBMITTER: Lee J

PROVIDER: S-EPMC7602207 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

Lee Jungwon J Lee Jun-Beom JB Song Hyun-Young HY Son Min Jung MJ Li Ling L Rhyu In-Chul IC Lee Yong-Moo YM Koo Ki-Tae KT An Jung-Sub JS Kim Jin Sup JS Kim Eunkyung E

Diagnostics (Basel, Switzerland) 20201014 10

This study aims to investigate and assess salivary biomarkers and microbial profiles as a means of diagnosing periodontitis. A total of 121 subjects were included: 28 periodontally healthy subjects, 24 with Stage I periodontitis, 24 with Stage II, 23 with Stage III, and 22 with Stage IV. Salivary proteins (including active matrix metalloproteinase-8 (MMP-8), pro-MMP-8, total MMP-8, C-reactive protein, secretory immunoglobulin A) and planktonic bacteria (including <i>Aggregatibacter actinomycetem ...[more]

PMID: 33066545

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Project description:Background and objectiveAggressive periodontitis (AgP) is prevalent and shows a rapid course in African individuals. Although a strong focus has been placed on Aggregatibacter actinomycetemcomitans, new methods support the existence of a complex subgingival microflora in AgP. The purpose of the present study was to map the subgingival microbiota as well as explore the presence of A. actinomycetemcomitans and the JP2 clone in a group of Sudanese individuals with AgP, using different analytical methods.Material and methodsA study population consisting of 19 patients with AgP was recruited from patients seeking treatment at University of Science and Technology (UST) in Khartoum. Fifteen healthy subjects were included as controls. Plaque samples were analyzed for 272 taxa using human oral microbe identification microarrays and for 26 periodontal taxa using DNA-DNA hybridization checkerboard. Conventional polymerase chain reaction (PCR) was applied for the detection of A. actinomycetemcomitans and the JP2 clone in plaque. Saliva from patients with AgP was analyzed using quantitative PCR (qPCR) for the detection of A. actinomycetemcomitans.ResultsEubacterium yurii was detected more frequently in patients with AgP than in controls, and E. nodatum was found in patients with AgP only. A. actinomycetemcomitans was found in plaque samples of two (12%) patients by human oral microbe identification microarrays and in five (29%) patients with AgP by conventional PCR, as well as in six (32%) of the AgP saliva samples by qPCR. The JP2 clone was identified in only one patient.ConclusionThe classical periodontal pathogens were not present in high amounts in AgP in the population studied here. Species of Eubacterium, which are not typically associated with AgP, were often detected in individuals with disease. Using laboratory methods with different sensitivities and detection levels allowed identification of variances in microbial communities. The findings reported in this study provide a basis for the further understanding of AgP.

Project description:Periodontitis is a microbial-induced chronic inflammatory disease, which may not only result in tooth loss, but can also contribute to the development of various systemic diseases. The transition from healthy to diseased periodontium depends on microbial dysbiosis and impaired host immune response. Although periodontitis is a common disease as well as associated with various systemic inflammatory conditions, the taxonomic profiling of the salivary microbiota in periodontitis and its association with host immune and inflammatory mediators has not been reported. Therefore, the aim of this study was to identify key pathogens and their potential interaction with the host's inflammatory mediators in saliva samples for periodontitis risk assessment. The microbial 16S rRNA gene sequencing and the levels of inflammatory mediators were performed in saliva samples from patients with chronic periodontitis and periodontally healthy control subjects. The salivary microbial community composition differed significantly between patients with chronic periodontitis and healthy controls. Our analyses identified a number of microbes, including bacteria assigned to Eubacterium saphenum, Tannerella forsythia, Filifactor alocis, Streptococcus mitis/parasanguinis, Parvimonas micra, Prevotella sp., Phocaeicola sp., and Fretibacterium sp. as more abundant in periodontitis, compared to healthy controls. In samples from healthy individuals, we identified Campylobacter concisus, and Veillonella sp. as more abundant. Integrative analysis of the microbiota and inflammatory mediators/cytokines revealed associations that included positive correlations between the pathogens Treponema sp. and Selenomas sp. and the cytokines chitinase 3-like 1, sIL-6Rα, sTNF-R1, and gp130/sIL-6Rβ. In addition, a negative correlation was identified between IL-10 and Filifactor alocis. Our results reveal distinct and disease-specific patterns of salivary microbial composition between patients with periodontitis and healthy controls, as well as significant correlations between microbiota and host-mediated inflammatory cytokines. The positive correlations between the pathogens Treponema sp. and Selenomas sp. and the cytokines chitinase 3-like 1, sIL-6Rα, sTNF-R1, and gp130/sIL-6Rβ might have the future potential to serve as a combined bacteria-host salivary biomarker panel for diagnosis of the chronic infectious disease periodontitis. However, further studies are required to determine the capacity of these microbes and inflammatory mediators as a salivary biomarker panel for periodontitis.

Project description:This study compared the subgingival microbiota of subjects with refractory periodontitis (RP) to those in subjects with treatable periodontitis (GRs = good responders) or periodontal health (PH) using the Human Oral Microbe Identification Microarray (HOMIM).At baseline, subgingival plaque samples were taken from 47 subjects with periodontitis and 20 individuals with PH and analyzed for the presence of 300 species by HOMIM. The subjects with periodontitis were classified as having RP (n = 17) based on mean attachment loss (AL) and/or more than three sites with AL >or=2.5 mm after scaling and root planing, surgery, and systemically administered amoxicillin and metronidazole or as GRs (n = 30) based on mean attachment gain and no sites with AL >or=2.5 mm after treatment. Significant differences in taxa among the groups were sought using the Kruskal-Wallis and chi(2) tests.More species were detected in patients with disease (GR or RP) than in those without disease (PH). Subjects with RP were distinguished from GRs or those with PH by a significantly higher frequency of putative periodontal pathogens, such as Parvimonas micra (previously Peptostreptococcus micros or Micromonas micros), Campylobacter gracilis, Eubacterium nodatum, Selenomonas noxia, Tannerella forsythia (previously T. forsythensis), Porphyromonas gingivalis, Prevotella spp., Treponema spp., and Eikenella corrodens, as well as unusual species (Pseudoramibacter alactolyticus, TM7 spp. oral taxon [OT] 346/356, Bacteroidetes sp. OT 272/274, Solobacterium moorei, Desulfobulbus sp. OT 041, Brevundimonas diminuta, Sphaerocytophaga sp. OT 337, Shuttleworthia satelles, Filifactor alocis, Dialister invisus/pneumosintes, Granulicatella adiacens, Mogibacterium timidum, Veillonella atypica, Mycoplasma salivarium, Synergistes sp. cluster II, and Acidaminococcaceae [G-1] sp. OT 132/150/155/148/135) (P <0.05). Species that were more prevalent in subjects with PH than in patients with periodontitis included Actinomyces sp. OT 170, Actinomyces spp. cluster I, Capnocytophaga sputigena, Cardiobacterium hominis, Haemophilus parainfluenzae, Lautropia mirabilis, Propionibacterium propionicum, Rothia dentocariosa/mucilaginosa, and Streptococcus sanguinis (P <0.05).As determined by HOMIM, patients with RP presented a distinct microbial profile compared to patients in the GR and PH groups.

Project description:BackgroundDental plaque microbes play a key role in the development of periodontal disease. Numerous high-throughput sequencing studies have generated understanding of the bacterial species associated with both canine periodontal health and disease. Opportunities therefore exist to utilise these bacterial biomarkers to improve disease diagnosis in conscious-based veterinary oral health checks. Here, we demonstrate that molecular techniques, specifically quantitative polymerase chain reaction (qPCR) can be utilised for the detection of microbial biomarkers associated with canine periodontal health and disease.ResultsOver 40 qPCR assays targeting single microbial species associated with canine periodontal health, gingivitis and early periodontitis were developed and validated. These were used to quantify levels of the respective taxa in canine subgingival plaque samples collected across periodontal health (PD0), gingivitis (PD1) and early periodontitis (PD2). When qPCR outputs were compared to the corresponding high-throughput sequencing data there were strong correlations, including a periodontal health associated taxa, Capnocytophaga sp. COT-339 (rs =0.805), and two periodontal disease associated taxa, Peptostreptococcaceae XI [G-4] sp. COT-019 (rs=0.902) and Clostridiales sp. COT-028 (rs=0.802). The best performing models, from five machine learning approaches applied to the qPCR data for these taxa, estimated 85.7% sensitivity and 27.5% specificity for Capnocytophaga sp. COT-339, 74.3% sensitivity and 67.5% specificity for Peptostreptococcaceae XI [G-4] sp. COT-019, and 60.0% sensitivity and 80.0% specificity for Clostridiales sp. COT-028.ConclusionsA qPCR-based approach is an accurate, sensitive, and cost-effective method for detection of microbial biomarkers associated with periodontal health and disease. Taken together, the correlation between qPCR and high-throughput sequencing outputs, and early accuracy insights, indicate the strategy offers a prospective route to the development of diagnostic tools for canine periodontal disease.

Dataset Information

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

Publications

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

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