Is periodontal disease a public health problem?

Batchelor, Paul

doi:10.1038/sj.bdj.2014.912

Cited by 65 publications

(46 citation statements)

References 28 publications

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“…Inflammatory diseases such as periodontitis are one of the most common pathologies in oral cavity, representing the most common inflammatory disease in humans (30,31). Considering that some of our candidate biomarkers are cytokines (IL-8 and IL-1β), one may suggest that inflammatory diseases in oral cavity may represent a confusion factor in our analysis.…”

Section: Discussionmentioning

confidence: 99%

Salivary Biomarkers for Detection of Oral Squamous Cell Carcinoma in a Taiwanese Population

Gleber‐Netto

Yakob

et al. 2016

Clinical Cancer Research

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Purpose This study evaluated the discriminatory power of salivary transcriptomic and proteomic biomarkers in distinguishing oral cancer (OSCC) cases from controls and potentially malignant oral disorders (PMOD). Experimental design A total of 180 samples (60 OSCC patients, 60 controls and 60 PMOD patients) were used in the study. Seven transcriptomic markers (IL-8, IL-1β, SAT1, OAZ1, DUSP1, S100P, H3F3A) were measured using quantitative real time PCR and two proteomic markers (IL-8 and IL-1β) were evaluated by ELISA. Results Among 7 transcriptomic markers, transcript level of DUSP1 was significantly lower in OSCC patients than in controls and PMOD patients. Between the proteomic markers, the protein concentration of IL-8 and IL-1β was significantly higher in OSCC patients than controls and dysplasia patients. Univariate fractional polynomial models revealed that salivary IL-8 protein has the highest AUC value between OSCC patients and controls (0.74) and between OSCC and PMOD patients (0.72). Applying a 2-markers fractional polynomial model, salivary IL-8 protein combined with IL-1β gave the best AUC value for discrimination between OSCC patients and controls, as well as the IL-8 protein combined with H3F3A mRNA gave the best AUC value for discrimination between OSCC and PMOD patients. Multivariate models analysis combining salivary analytes and risk factor exposure related to oral carcinogenesis formed the best combinatory variables for differentiation between OSCC vs PMOL (AUC=0.80), OSCC vs controls (AUC=0.87) and PMOD vs. controls (AUC=0.78). Conclusions Combination of transcriptomic and proteomic salivary markers is of great value for oral cancer detection and differentiation from PMOD patients and controls.

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Section: Discussionmentioning

confidence: 99%

Salivary Biomarkers for Detection of Oral Squamous Cell Carcinoma in a Taiwanese Population

Gleber‐Netto

Yakob

et al. 2016

Clinical Cancer Research

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“…In addition to the substantial economic burden and negative impact of these diseases on quality of life [3,4], oral bacteria and periodontal infections have been indicated as potential risk factors for several systemic diseases [5][6][7][8]. Due to the anatomical proximity of the periodontal biofilm to the gingival blood stream, periodontal pockets may act as reservoirs of microbial pathogens and their products, as well as inflammatory mediators and immunocomplexes that may disseminate to other sites of the human body [7,9].…”

Section: Introductionmentioning

confidence: 99%

Periodontal-disease-associated biofilm: A reservoir for pathogens of medical importance

Colombo

Magalhães

Hartenbach

et al. 2016

Microbial Pathogenesis

242

154

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The ecological diversity of the periodontal microenvironment may provide suitable conditions for the colonization of species not usually considered members of the oral microbiota. In this investigation, we aimed to determine the prevalence and levels of pathogenic species of medical relevance in the microbiota of individuals with distinct periodontal clinical status. Subgingival biofilm was obtained from patients with periodontal health (H, n = 81), gingivitis (G, n = 55), generalized aggressive (AgP, n = 36) or chronic periodontitis (CP, n = 98), and analyzed for 39 microbial taxa using a checkerboard DNA-DNA hybridization technique. Microbial differences among groups, as well as associations between clinical and microbiological parameters were sought by non-parametric and univariate correlation tests. Neisseria spp., Peptostreptococus anaerobius, Candida albicans, enterobacteria, Pseudomonas aeruginosa, Eubacterium saphenum, Clostridium difficile and Olsenella uli were detected in high mean prevalence and counts in the subgingival microbiota of the study population. Species that were more related to periodontal inflammation and tissue destruction at the patient and site levels included enterobacteria, C. albicans, Neisseria spp., P. aeruginosa, O. uli, Hafnia alvei, Serratia marcescens and Filifactor alocis (p < 0.05). In contrast, Fusobacterium necrophorum, Lactobacillus acidophilus, Staphylococcus aureus and Streptococcus pneumoniae were associated with periodontal health (p < 0.05). Pathogenic species of medical importance may be detected in high prevalence and levels in the periodontal microbiota. Regardless of their role in periodontal health or disease, the periodontal biofilm may be a source for dissemination and development of systemic infections by these pathogenic microorganisms.

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“…Periodontitis is the most common chronic inflammatory disease of humans worldwide, affecting nearly half of adults in the United Kingdom and the United States of America 1, 2 . The condition is characterized by destruction of the connective tissue and alveolar bone surrounding the teeth and has many negative impacts in life quality 3 , for instance, loss of permanent tooth.…”

Section: Introductionmentioning

confidence: 99%

Bacterial protease uses distinct thermodynamic signatures for substrate recognition

Bezerra

Ohara‐Nemoto

Cornaciu

et al. 2017

Sci Rep

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Porphyromonas gingivalis and Porphyromonas endodontalis are important bacteria related to periodontitis, the most common chronic inflammatory disease in humans worldwide. Its comorbidity with systemic diseases, such as type 2 diabetes, oral cancers and cardiovascular diseases, continues to generate considerable interest. Surprisingly, these two microorganisms do not ferment carbohydrates; rather they use proteinaceous substrates as carbon and energy sources. However, the underlying biochemical mechanisms of their energy metabolism remain unknown. Here, we show that dipeptidyl peptidase 11 (DPP11), a central metabolic enzyme in these bacteria, undergoes a conformational change upon peptide binding to distinguish substrates from end products. It binds substrates through an entropy-driven process and end products in an enthalpy-driven fashion. We show that increase in protein conformational entropy is the main-driving force for substrate binding via the unfolding of specific regions of the enzyme (“entropy reservoirs”). The relationship between our structural and thermodynamics data yields a distinct model for protein-protein interactions where protein conformational entropy modulates the binding free-energy. Further, our findings provide a framework for the structure-based design of specific DPP11 inhibitors.

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Is periodontal disease a public health problem?

Cited by 65 publications

References 28 publications

Salivary Biomarkers for Detection of Oral Squamous Cell Carcinoma in a Taiwanese Population

Salivary Biomarkers for Detection of Oral Squamous Cell Carcinoma in a Taiwanese Population

Periodontal-disease-associated biofilm: A reservoir for pathogens of medical importance

Bacterial protease uses distinct thermodynamic signatures for substrate recognition

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