Relationship between the Pathogenic Representatives of Periodontal Pockets Microbiocenosis in Patients with Periodontitis with Varying Degrees of Severity

Зорина, О А; Aa, Kulakov; Boriskina, O A; Rebrikov, Denis

doi:10.32607/20758251-2011-3-2-99-102

Cited by 5 publications

(2 citation statements)

References 9 publications

(9 reference statements)

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“…Much of the previous research has focused on investigating subgingival microbiota levels using a case–control study design. The control group may include healthy subjects [ 30 ], gingivitis patients [ 31 ], or only different periodontal patient groups based on periodontal severity [ 32 ]. Furthermore, some qPCR studies have been conducted solely on subjects with periodontal disease [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

qPCR Detection and Quantification of Aggregatibacter actinomycetemcomitans and Other Periodontal Pathogens in Saliva and Gingival Crevicular Fluid among Periodontitis Patients

et al. 2023

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Objective: The detection of special bacterial species in patients with periodontitis is considered useful for clinical diagnosis and treatment. The aim of this study was to investigate the presence of specific periopathogens and investigate whether there is a correlation between the results of different bacterial species in whole saliva and pooled subgingival plaque samples (healthy and diseased sites) from individuals with periodontitis and periodontally healthy subjects. Materials and methods: In total, 52 patients were recruited and divided into two groups: non-periodontitis and periodontitis patients. For each group, the following periodontal pathogens were detected using real-time polymerase chain reaction: A. actinomycetemcomitans JP2 clone, A. actinomycetemcomitans non JP2 clone, Porphyromonasgingivalis, and total eubacteria. Results: Higher levels of the various studied bacteria were present in both saliva and plaque samples from the periodontitis group in comparison to non-periodontitis subjects. There were significant differences in P. gingivalis and A. actinomycetemcomitans JP2 clones in the saliva of periodontitis patient compared to the control group. Subgingival plaque of diseased sites presented a significant and strong positive correlation between A. actinomycetemcomitans and P. gingivalis. In saliva samples, there was a significant positive correlation between A. actinomycetemcomitans JP2 clone and P. gingivalis (p ≤ 0.002). Conclusion: Quantifying and differentiating these periodontal species from subgingival plaque and saliva samples showed a good potential as diagnostic markers for periodontal disease. Regarding the prevalence of the studied bacteria, specifically A. actinomycetemcomitans JP2 clone, found in this work, and the high rate of susceptibility to periodontal species in Africa, future larger studies are recommended.

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Section: Discussionmentioning

confidence: 99%

qPCR Detection and Quantification of Aggregatibacter actinomycetemcomitans and Other Periodontal Pathogens in Saliva and Gingival Crevicular Fluid among Periodontitis Patients

et al. 2023

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“…The control group may also include gingivitis patients ( Scapoli et al, 2015 ), or they may constitute a separate group ( Masunaga et al, 2010 ). In addition, there are other authors who have studied subgingival samples microbiologically using the qPCR from different periodontal patient groups according to the degree of periodontal affection (mild, moderate, or severe) ( Zorina et al, 2011 ) or the type of disease (chronic vs. aggressive) ( Casarin et al, 2010 ). There are also qPCR studies that have been performed exclusively on subjects with periodontitis ( Kotsilkov et al, 2015 ; Milne et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level

et al. 2017

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Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival plaque samples were obtained from control and periodontal sites (probing pocket depth and clinical attachment loss <4 mm and >4 mm, respectively) from 40 patients with moderate-severe generalized chronic periodontitis. The samples were analyzed by qPCR using TaqMan probes and specific primers to determine the concentrations of Actinobacillus actinomycetemcomitans (Aa), Fusobacterium nucleatum (Fn), Parvimonas micra (Pm), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Tannerella forsythia (Tf), and Treponema denticola (Td). The pathobiont-based models were obtained using multivariate binary logistic regression. The best models were selected according to specified criteria. The discrimination was assessed using receiver operating characteristic curves and numerous classification measures were thus obtained. The nomograms were built based on the best predictive models. Eight bacterial cluster-based models showed an area under the curve (AUC) ≥0.760 and a sensitivity and specificity ≥75.0%. The PiTfFn cluster showed an AUC of 0.773 (sensitivity and specificity = 75.0%). When Pm and AaPm were incorporated in the TdPiTfFn cluster, we detected the two best predictive models with an AUC of 0.788 and 0.789, respectively (sensitivity and specificity = 77.5%). The TdPiTfAa cluster had an AUC of 0.785 (sensitivity and specificity = 75.0%). When Pm was incorporated in this cluster, a new predictive model appeared with better AUC and specificity values (0.787 and 80.0%, respectively). Distinct clusters formed by species with different etiopathogenic role (belonging to different Socransky’s complexes) had a good predictive accuracy for distinguishing a site with periodontal destruction in a periodontal patient. The predictive clusters with the lowest number of bacteria were PiTfFn and TdPiTfAa, while TdPiTfAaFnPm had the highest number. In all the developed nomograms, high concentrations of these clusters were associated with an increased probability of having a periodontal site in a patient with chronic periodontitis.

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The relationship of molecular genetic markers with clinical signs and risk factors of periodontitis

Зорина

Аймадинова

Basova

et al. 2016

Stomat.

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The study revealed positive correlation between bleeding on probing and teeth loss risk with periodontal hypercolonization by Porphyromonas gingivalis, Prevotella intermedia and Treponema denticola. Pathological tooth mobility was associated with hypercolonization by P. intermedia and Tannerella forsythensis. Expression of IL8, TNF-α, MMP8 and MMP9 genes was also assessed in patient groups divided according to the depth of periodontal pockets and-the severity of chronic periodontitis revealing IL8 as positive diagnostic marker.

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Relationship between the Pathogenic Representatives of Periodontal Pockets Microbiocenosis in Patients with Periodontitis with Varying Degrees of Severity

Cited by 5 publications

References 9 publications

qPCR Detection and Quantification of Aggregatibacter actinomycetemcomitans and Other Periodontal Pathogens in Saliva and Gingival Crevicular Fluid among Periodontitis Patients

qPCR Detection and Quantification of Aggregatibacter actinomycetemcomitans and Other Periodontal Pathogens in Saliva and Gingival Crevicular Fluid among Periodontitis Patients

Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level

The relationship of molecular genetic markers with clinical signs and risk factors of periodontitis

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