Figure - available from: Periodontology 2000
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A generic multi‐causality model for periodontitis, where 5 clusters of causal (risk) factors are playing a role simultaneously, (epi)genetic factors (light blue), lifestyle factors (orange), comorbidities (systemic diseases) (gray), microbial communities, ie, dental biofilms (yellow) and other factors (tooth and dention related and stochasticity) (dark blue). Notably, for each individual periodontitis patient, the relative contribution of the 5 clusters of causal factors varies and needs to be estimated, and as such, theoretically, for each patient, a unique pie chart can be created. Adapted from17 based on124
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Abstract Periodontitis is a complex disease: (a) various causative factors play a role simultaneously and interact with each other; and (b) the disease is episodic in nature, and bursts of disease activity can be recognized, ie, the disease develops and cycles in a nonlinear fashion. We recognize that various causative factors determine the immune...
Citations
... 30,33 Asarinin is known for its pharmacological properties including anti-inflammatory, anticancer, antibacterial, antiviral, and antioxidant. 35 Various studies have shown that asarinin could potentially manage inflammation associated with periodontal diseases, 47 whereas its antitumor and anticancer properties can help to manage oral cancer. 33,39 In addition, Yang et al. 35 found that asarinin could induce caspasedependent apoptotic cell death in human cancer cells, mediated by the increased activation of caspase-3, caspase-8, and caspase-9 in cancer cells. ...
... Patients identified with high-risk genetic profiles might require more intensive management strategies [71]. This could include more frequent professional cleanings to control plaque and tartar buildup, which are more likely to cause inflammation in genetically susceptible individuals. ...
Recent advancements in genetics have significantly reshaped the landscape of dentistry, providing insights that pave the way for precision oral healthcare. This article explores the critical role of genetics in the diagnosis, prevention, and treatment of oral diseases. By integrating genetic markers and next-generation sequencing (NGS) technologies, dental professionals can now tailor treatments based on individual genetic profiles, enhancing the efficacy of interventions for conditions such as periodontitis, dental caries, and craniofacial anomalies. Additionally, this review discusses the implications of genetic discoveries in developing biomarker-driven diagnostics and the potential of multi-omics research to further understand and combat oral diseases.
... A recent study of the bacteriome of the oral cavity in healthy and periodontally diseased dogs showed that early colonisers were significantly more abundant in dogs with healthy gingiva and late colonisers were enriched in dogs with severe periodontitis [22]. Breed as well as individual differences in susceptibility and clinical manifestation of periodontitis could be due to immune function where in some instances the microbial biofilm is tolerated (homeostasis) and in others there is an aberrant response, leading to dysbiosis and inflammation-driven tissue destruction [32]. ...
Background
Periodontal disease, an inflammatory condition initiated by the build-up of plaque on the tooth surface, is a common problem seen within veterinary practices. There are an increasing number of studies which indicate distinctive microbial profiles associated with healthy gingiva and periodontal disease. Most have been based on analysis of samples from populations of mixed breed dogs collected at a specific point in time. A study investigating the development of periodontal disease in Yorkshire terriers provided an opportunity to investigate the concurrent changes in the oral microbiota in this specific breed.
Results
Analysis of 42 subgingival plaque samples from 22 dogs (1 to 4 samples per dog) by 454 pyrosequencing of the V1-V3 region of the 16S rRNA gene resulted in 796,091 filtered sequence reads which were assigned to 286 operational taxonomic units (excluding those deemed noise). Statistical analysis showed that health and mild gingivitis were associated with higher relative abundance of taxa belonging to the phylum Proteobacteria (e.g., Moraxella and Pasteurellaceae). In moderate gingivitis there was increased representation of taxa belonging to the phylum Firmicutes (e.g., Peptostreptococcaceae, Lachnospiraceae, Erysipelotrichaceae and Frigovirgula) and Bacteroidetes (Porphyromonas canoris). Periodontitis was also associated with an increased representation of some taxa belonging to the phylum Firmicutes (e.g., Peptosteptococcaceae), Spirochaetea (e.g., Treponema) and Synergistetes (e.g., Synergistales).
Conclusions
This study further advances our understanding of the bacterial changes associated with early periodontal disease. These can be leveraged to improve disease diagnosis, drive awareness and support recommendations for effective preventative and management strategies.
... Initially, in the innate immune response, bacteria present in the dental biofilm interact with receptors on host cells, such as epithelial cells and gingival fibroblasts, through binding between pathogenassociated molecular patterns and pattern recognition receptors. This process triggers the production of proinflammatory cytokines, such as IL-1β, TNF-α, and prostaglandins, which promote the recruitment and activation of inflammatory cells, especially neutrophils and macrophages, thereby increasing tissue destruction Loos & Van Dyke, 2020). In relation to neutrophils, different stimuli from proinflammatory cytokines and chemokines, complement fragments, adhesion molecules, and extracellular Ca 2 + , as well as bacterial formyl peptides interact and activate G protein-coupled receptors, FCγ receptors and integrin β2/ Mac-1, which triggers the degranulation response. ...
... Various signaling molecules are released at the site of inflammation coordinating the immune response, regulating immune cell movement and promoting inflammation [5]. Although inflammation generally serves as a protective response, chronic inflammation can be detrimental, contributing to conditions like arthritis, atherosclerosis, chronic respiratory diseases, periodontitis and inflammatory bowel disease etc., thus maintaining a balanced immune response is crucial for overall health [6]. Periodontitis, a bacterial infection, causes alveolar bone loss, impairing mastication, aesthetics and quality of life. ...
Periodontitis is a chronic bacterial infection causing tissue damage, bone loss and tooth mobility and thus, impacting quality of life. It is influenced by factors like plaque, genetics and immune response, driving inflammation and tissue destruction. Dickkopf-1 inhibits the wingless-related integration site (Wnt) signaling pathway, reducing osteoblast activity and promoting osteoclast formation, leading to bone resorption. Neutralizing Dickkopf-1 activity may facilitate bone regeneration, suggesting its potential as a target for periodontitis treatment. Therefore, it is of interest to review the role of Dickkopf-1 in periodontitis for understanding its impact on alveolar bone resorption highlighting its therapeutic potential in improving alveolar bone health.
... Over 60 genetic variants have been implicated in periodontitis, highlighting pleiotropic links with conditions like cardiovascular diseases. Despite advances, further research is needed to deepen our understanding of how genetic and inflammatory pathways contribute to the disease's pathogenesis and to solidify cause-and-effect relationships [23]. On the other hand, a study in minipigs by Li et al. has shown that epigenetic changes, such as DNA methylation, contribute to increased susceptibility to periodontal disease in diabetic patients [24]. ...
... DM and periodontal diseases are common, chronic, multifactorial diseases. In many studies, diabetes was shown to be an important risk factor for the development of gingivitis and periodontitis [23]. ...
Objective: Periodontal disease is a prevalent chronic inflammatory condition affecting the supporting structures of teeth and is considered one of the chronic complications of Type 2 diabetes mellitus (T2DM). Both diabetes and periodontal diseases are complex, multifactorial diseases to which genetic factors play a crucial role in susceptibility. The TNF-α/HIF-1 pathway might have a regulatory function in periodontal tissues. Several case-control studies have examined the association between TNF-α G308A or HIF-1α C1772T polymorphisms and diabetes complications, but the results have been inconsistent. We aimed to investigate the association between two specific genetic variants -HIF-1α C1772T and TNF-α G308A- and periodontal disease in patients with type 2 diabetes. Methods: A total of 109 individuals were enrolled in the study including 24 chronic periodontitis with T2DM (group 1), 35 gingivitis with T2DM (group 2), 26 non-diabetic individuals with chronic periodontitis (group 3) and 24 periodontally healthy non-diabetic individuals (group 4). The normal allelic and genotype distribution of these variants was analyzed in healthy Turkish adults (n: 120), independent of the study cohort. Allele and genotype distribution of group 4 and healthy Turkish adults were similar. Allelic and genotypic comparisons between group 4 and other groups were evaluated by PCR-RFLP. Allelic, dominant, and recessive genetic models were calculated to assess the strength of the association. Results: We found a significant association between the A allele at TNF-α G308A and the risk of gingivitis in T2DM (OR=3.75, CI:1.015–13.860, p=0.048). There was no association detected between HIF-1α C1772T polymorphisms and risk for periodontal diseases with T2DM. Conclusion: These results suggest that TNF G308A polymorphism may be involved in the pathogenesis of periodontal disease in diabetics. Future studies may contribute to the investigation of the potential polygenic predisposition of the diseases and reinforce our findings.
... Periodontitis, a chronic inflammatory disease affecting the soft and hard tissues surrounding teeth, is the sixth most common disease globally, with a prevalence of 45-50% among the population [9]. Periodontitis is a chronic inflammatory disease initiated by dental plaque biofilm, with its core mechanisms involving the interplay between immuneinflammatory responses and pathogenic bacteria [10]. Pathogenic bacteria within the dental plaque, such as Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans, release virulence factors, including lipopolysaccharides and proteases [11]. ...
Unstable atherosclerotic plaques are a major cause of acute cardiovascular events and ischemic stroke. Clinical studies have suggested a link between periodontitis and atherosclerotic plaque progression, but the underlying mechanisms remain unclear. To investigate this, transcriptomic datasets related to periodontitis and atherosclerosis were downloaded from Gene Expression Omnibus. A weighted gene co-expression network analysis was used to identify gene modules associated with periodontitis, and the Limma R package identified differentially expressed genes (DEGs) between unstable and stable plaques. Overlapping genes were defined as periodontitis-related DEGs, followed by functional enrichment analysis and protein–protein interaction network construction. Machine learning methods were used to identify biomarkers for unstable plaques related to periodontitis, which were validated using external datasets. Immune infiltration and single-cell analyses were performed to explore the relationship between biomarkers and immune cells. A total of 161 periodontitis-related DEGs were identified, with the pathway analysis showing associations with immune regulation and collagen matrix degradation. HCK, NCKAP1L, and WAS were identified as biomarkers for unstable plaques, demonstrating a high diagnostic value (AUC: 0.9884, 95% CI: 0.9641–1). Immune infiltration analysis revealed an increase in macrophages within unstable plaques. Single-cell analysis showed HCK expression in macrophages and dendritic cells, while NCKAP1L and WAS were expressed in macrophages, dendritic cells, NK cells, and T cells. Consensus clustering identified three expression patterns within unstable plaques. Our findings were validated in atherosclerotic mouse models with periodontitis. This study provides insights into how periodontitis contributes to plaque instability, supporting diagnosis and intervention in patients with periodontitis.
... Periodontitis is a chronic multifactorial inflammatory disease of the supporting structures of the teeth [1,2]. The worldwide prevalence of all forms of periodontitis is estimated to be 62%, while the age-standardized prevalence of severe periodontitis is 12.5% [3,4]. ...
... Control subjects were included when they [1] did not fulfill the criteria for the case definition of periodontitis, [2] had not previously been treated for periodontitis, and [3] did not have interproximal alveolar bone loss on recent bitewing radiographs (≤ 1 year old); a distance of ≤ 3 mm between the cemento-enamel junction and the most coronal part of the radiographic alveolar crest was accepted for a non-periodontitis control subject. ...
Aims
To explore the potential of metabolomic profiles of oral rinse samples to distinguish between patients with severe periodontitis (stage III/IV) and non‐periodontitis controls. This is coupled to an analysis of differences in metabolomic profiles between individuals without periodontitis, patients with localized periodontitis, and patients with generalized periodontitis.
Methods
Periodontitis patients and controls were recruited, all aged ≥ 40 years. Study participants were asked to rinse vigorously for 30 s with 10 mL phosphate buffered saline. Metabolites were identified using a semi‐targeted liquid chromatography tandem mass spectrometry (LC–MS/MS) platform.
Results
In total, 38 periodontitis patients (18 localized, 20 generalized stage III/IV periodontitis patients) and 16 controls were included. Metabolomic profiles of oral rinse samples were able to distinguish patients with severe periodontitis (stage III/IV) from non‐periodontitis controls. Among various variables for the severity of periodontitis, we found that the number of sites with deep pockets (PPD) ≥ 6 mm explained best the differences in metabolomic profiles between controls and patients with severe periodontitis. Subjects with a high number of sites with PPD ≥ 6 mm were characterized by a higher level of phosphorylated nucleotides, amino acids, peptides, and dicarboxylic acids. Metabolomic profiles were also significantly different between controls vs. generalized periodontitis and between localized periodontitis vs. generalized periodontitis ( p < 0.05).
Conclusion
Our study demonstrates that simply collected oral rinse samples are suitable for LC–MS/MS based metabolomic analysis. We show that a metabolomic profile with a substantial number of metabolites can distinguish severe periodontitis patients from non‐periodontitis controls. These observations can be a basis for further studies into screening to identify subjects with the risk of having severe periodontitis.
... (3) Inflammation-immune crosstalk: chronic inflammation induces oxidative stress (ROS levels 3 × baseline, impairing tissue regeneration and perpetuating dysbiosis; (4) Clinical translation hurdles: hydrogel-based therapies face challenges in probiotic viability (50 % activity loss during storage), patient compliance with repeated applications, and heterogeneous treatment responses due to genetic/epigenetic variability. Future advancements must integrate multi-responsive hydrogels with biofilm-disrupting nanomaterials and personalized microbial consortia to overcome these barriers [24][25][26][27]. ...
... Periodontitis is a complex, chronic inflammatory disease that causes destruction of the tooth-supporting apparatus and can lead to tooth loss [1]. Rheumatoid arthritis is a chronic, autoimmune, inflammatory systemic disease, which is characterized by persistent joint inflammation, and, like periodontitis, also can cause bone loss. ...
Rheumatoid arthritis and periodontitis are comorbidities that share mutual pathways. IL-1β is a pro-inflammatory cytokine that plays a crucial role in both diseases. One of the treatment options for rheumatoid arthritis is the use of an IL-1 receptor antagonist (IL-1RA) such as anakinra. Anakinra tempers the disease by decreasing bone resorption and it could possibly stimulate bone formation. Here, we investigate the effect of anakinra in a periodontal disease setting on osteoclastogenesis by co-culturing periodontal ligament fibroblasts (PDLFs) and peripheral blood mononuclear cells (PBMCs) that contain monocytes, a source of osteoclast precursors, as well as by culturing PBMCs alone. The effect of anakinra on PDLF-mediated osteogenesis was studied under mineralization conditions. To mimic a chronic infection such as that prevalent in periodontitis, 10 ng/mL of IL-1β was added either alone or with 10 µg/mL of anakinra. Osteoclastogenesis experiments were performed using co-cultures of PDLF and PBMCs and PBMCs only. Osteoclastogenesis was determined through the formation of multinucleated cells in co-cultures of PDLF and PBMCs, as well as PBMCs alone, at day 21, and gene expression through qPCR at day 14. Osteogenesis was determined by measuring alkaline phosphatase activity (ALP) per cell at day 14. Anakinra is effective in downregulating IL-1β mediated leukocyte clustering and osteoclastogenesis in the co-cultures of both PDLF and PMBCs and PBMCs alone. Gene expression analysis shows that IL-1β increases the expression of the osteoclastogenic marker RANKL and its own expression. This higher expression of IL-1β at the RNA level is reduced by anakinra. Moreover, IL-1β downregulates OPG expression, which is upregulated by anakinra. No effects of anakinra on osteogenesis were seen. Clinically, these findings suggest that anakinra could have a beneficial systemic effect on periodontal breakdown in rheumatoid arthritis patients taking anakinra.
