Journal of periodontology最新文献

Background: This study aimed to investigate the relationship between the pan-immune-inflammation value (PIV) and periodontitis based on a large national survey.

Methods: In the present cross-sectional study, data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2009-2014, which included a total of 10,300 participants. The categorization of periodontitis was based on the 2017 classification scheme. The PIV was determined using the formula: (neutrophils count × monocyte count × platelet count)/lymphocytes count. Restricted cubic spline and weighted multivariable logistic regression analyses were employed to evaluate the associations between the PIV with periodontitis.

Results: The associations between PIV and stage III/IV periodontitis followed a U-shaped pattern (P_{non-linearity} < 0.001). The risk of developing stage III/IV periodontitis showed an increasing trend among participants in the first quartile (odds ratio [OR] = 1.21; 95% confidence interval [CI]: 1.01-1.46), third quartile (OR = 1.34; 95% CI: 1.11-1.61), and fourth quartile (OR = 1.47; 95% CI: 1.25-1.73) compared to those in the second quartile. Subgroup analysis indicated stronger associations of PIV with periodontitis in males (OR_Q4vs2 = 1.72, 95% CI: 1.36-2.18) and individuals with hypertension (OR_Q4vs2 = 1.78, 95% CI: 1.38-2.28) with significant interactions (P_interaction < 0.05).

Conclusions: There is a U-shaped association between PIV and stage III/IV periodontitis, which suggests a potential adjunctive treatment strategy for periodontitis. Higher PIV values were found to have a stronger correlation with stage III/IV periodontitis in males and individuals with hypertension. Further prospective trials are needed to confirm the validity of our results.

Plain language summary: A U-shaped association exists between the pan-immune inflammation value and periodontitis in US adults, suggesting that maintaining a moderate immune inflammation response is crucial for periodontal health.

Background: Re-implantation of failed implants is common to maintain the original prosthetic plan; however, it may not always be ideal due to various factors. Few studies have thoroughly investigated the outcomes of re-implanted implants, while considering factors that can enhance their survival rates. Therefore, this study aimed to identify the risk factors that may contribute to the refailure of implants placed the second time by analyzing previously failed implants and evaluating their survival.

Methods: Of 10,666 dental implants placed in 4063 patients at the Department of Periodontics of the Gangneung-Wonju National University Dental Hospital between December 1999 and March 2021, 259 failed implants in 170 patients were evaluated through clinical and radiographic records for patient-, surgical-, implant-, and prosthesis-related factors; survival analysis was conducted for implants that met the inclusion criteria.

Results: Of the 259 failed implants, 80 second-time-placed implants met the inclusion criteria. Survival analysis showed that the 1-year survival rate of second-time-placed implants was 88.1%. Smoking (hazard ratio [HR] = 5.066, p = 0.081), implant surface (HR = 18.776, p < 0.01), and timing of reimplantation (HR = 0.086, p < 0.01) were identified as risk factors influencing the refailure of second-time-placed implants.

Conclusions: The survival rate of second-time-placed implants was lower than that of first-time-placed implants. The risk factors for second-time-placed implant failure were smoking, implant surface, and timing of reimplantation. To prevent further failure, previous failure factors should be analyzed and modifiable risk factors must be controlled before reimplantation.

Background: The Oxidative Balance Score (OBS) is a comprehensive metric that assesses the state of a person's oxidative balance. This study aimed to investigate the relationship between the oxidative balance score and moderate and severe periodontitis in a representative sample of Korean adults.

Methods: Healthcare big data from the 7th Korea National Health and Nutrition Examination Survey (2016-2018) was used, and 16,489 adults aged ≥19 years were included. Multivariate logistic regression analysis was performed to investigate the effect of sex-specific oxidative balance scores on periodontitis.

Results: In comparison with participants with a lower oxidative balance score, those with a higher oxidative balance score had a lower incidence of moderate and severe periodontitis (p < 0.05). After adjusting for covariates, the oxidative balance score was negatively associated with moderate (odds ratio [OR] = 0.952; 95% confidence interval [CI]: 0.934-0.971) and severe (OR = 0.958; 95% CI: 0.931-0.986) periodontitis; however, the result was not significant for severe periodontitis in women's (OR = 0.975; 95% CI: 0.934-1.018). Our study showed a statistically significant association between OBS and moderate and severe periodontitis, the small effect size should be interpreted with caution.

Conclusions: The oxidative balance score was associated with moderate and severe periodontitis in Korean adults. Therefore, managing this score may help reduce the risk of periodontitis.

Background: Cellular cementum (CC) includes cementocytes, cells suspected to regulate CC formation or resorption as osteocytes do in bone. Sclerostin (SOST) is a secreted negative regulator of Wnt/β-catenin signaling expressed by osteocytes and cementocytes. Osteocyte SOST expression reduces bone formation. We investigated the functional importance of SOST in CC compared with alveolar bone (AB) using a Sost knockout (Sost^-/-) mouse model to better understand the role of cementocytes in CC.

Methods: Mandibles and femurs of Sost^-/- and wild-type (WT) mice were analyzed at 42 and 120 days postnatal (dpn). Maxillary first molars were bilaterally extracted at 42 dpn and both AB healing (maxillary molar sockets) and CC apposition (mandibular first molars) were examined at 21 days post-procedure. Analyses included micro-computed tomography, histology, and immunohistochemistry.

Results: Femur cortical and trabecular bone and mandibular bone volumes were similarly increased in Sost^-/- versus WT mice at 42 and/or 120 dpn. In contrast to previous reports, CC was not increased by Sost^-/- at either age. We conducted challenge experiments on AB and CC to explore tissue-specific responses. Post-extraction AB healing was improved by Sost deletion. In contrast, experimentally-induced apposition in molars failed to stimulate increased CC formation in Sost^-/- versus WT mice. Wnt pathway markers AXIN2 and DKK1, which were increased in Sost^-/- versus WT AB osteocytes, were unchanged in cementocytes.

Conclusions: These data indicate CC is less responsive than AB to SOST deletion. Within the study limitations, these results do not support cementocytes as critical for directing increased CC formation.

Plain language summary: Sclerostin is a protein known to inhibit bone formation, and removing sclerostin leads to more bone formation. Cementum is the thin layer that covers the surface of the tooth's root. Previous studies suggest that inhibiting sclerostin can similarly increase the amount of cementum. We wanted to compare the response of cementum and bone when sclerostin is absent to understand similarities and differences between these two tissues. In this study, we removed the Sost gene (the gene which produces sclerostin) in mice. We found that mice without sclerostin have more bone in their legs and jaws. Moreover, mice without sclerostin also healed better after tooth removal compared with normal mice. Surprisingly, unlike previous studies, we found that the amount of cementum was not different in mice without sclerostin compared with normal mice. Additionally, we challenged the cementum by taking out the opposing tooth to cause the first mandibular molar to move up by building more cementum. Even with this challenge, we found no difference in the amount of cementum in mice lacking sclerost

Background: Chlorhexidine (CHX)-based mouth rinses are frequently prescribed following periodontal surgeries. A more recently available brand of zinc-based mouth rinses advertises one of its mouth rinses as a substitute for chlorhexidine. The purpose of this study was to evaluate, in vitro, the effects of this brand of zinc-based mouth rinses on cell survival, cell motility, and gene expression of human gingival fibroblasts (HGFs).

Methods: HGFs were exposed to essential oil (EO), CHX, and three types of one brand of zinc-based mouth rinses designed to treat breath malodor (ZnA), dry mouth (ZnB), and gingivitis (ZnC). Each mouth rinse was tested over a range of concentrations for its effects on HGF survival and motility. Gene expression of cytokines, interleukins, and growth factors were evaluated via reverse transcriptase-polymerase chain reaction (RT-PCR), as a means to assess potential influences on inflammation and wound healing.

Results: Cell survival was significantly decreased for CHX and ZnC at 10% dilutions (p < 0.05). For all time points, cells exposed to ZnC displayed the greatest reduction in cell motility (p < 0.05). The various mouth rinses examined differentially altered the expression of growth factor transcripts. ZnC particularly enhanced the expression of BMP-2 and FGF-2.

Conclusion: ZnC was more cytotoxic and inhibited cell motility to a greater extent than any of the other mouth rinses. Therefore, using ZnC as an alternative to CHX could potentially have negative effects on wound healing after periodontal surgery. However, further investigation is required to confirm the clinical relevance of these in vitro findings.

Plain language summary: One type of zinc-based mouth rinse designed to replace chlorhexidine (often prescribed after oral surgeries) demonstrated the greatest oral cell death and reduction in cell movement when compared to other zinc-based mouth rinses. These zinc-based mouth rinses also reduced the amounts of proteins involved in regulating inflammation, potentially reducing the destruction of bone holding the teeth in place. They also changed the amounts of several molecules involved in tissue healing. It is unknown if this will speed or slow the healing of the soft tissues of the mouth.

Background: With recent advances in artificial intelligence, the use of this technology has begun to facilitate comprehensive tissue evaluation and planning of interventions. This study aimed to assess different convolutional neural networks (CNN) in deep learning algorithms to detect keratinized gingiva based on intraoral photos and evaluate the ability of networks to measure keratinized gingiva width.

Methods: Six hundred of 1200 photographs taken before and after applying a disclosing agent were used to compare the neural networks in segmenting the keratinized gingiva. Segmentation performances of networks were evaluated using accuracy, intersection over union, and F1 score. Keratinized gingiva width from a reference point was measured from ground truth images and compared with the measurements of clinicians and the DeepLab image that was generated from the ResNet50 model. The effect of measurement operators, phenotype, and jaw on differences in measurements was evaluated by three-factor mixed-design analysis of variance (ANOVA).

Results: Among the compared networks, ResNet50 distinguished keratinized gingiva at the highest accuracy rate of 91.4%. The measurements between deep learning and clinicians were in excellent agreement according to jaw and phenotype. When analyzing the influence of the measurement operators, phenotype, and jaw on the measurements performed according to the ground truth, there were statistically significant differences in measurement operators and jaw (p < 0.05).

Conclusions: Automated keratinized gingiva segmentation with the ResNet50 model might be a feasible method for assisting professionals. The measurement results promise a potentially high performance of the model as it requires less time and experience.

Plain language summary: With recent advances in artificial intelligence (AI), it is now possible to use this technology to evaluate tissues and plan medical procedures thoroughly. This study focused on testing different AI models, specifically CNN, to identify and measure a specific type of gum tissue called keratinized gingiva using photos taken inside the mouth. Out of 1200 photos, 600 were used in the study to compare the performance of different CNN in identifying gingival tissue. The accuracy and effectiveness of these models were measured and compared to human clinician ratings. The study found that the ResNet50 model was the most accurate, correctly identifying gingival tissue 91.4% of the time. When the AI model and clinicians' measurements of gum tissue width were compared, the results were very similar, especially when accounting for different jaws and gum structures. The study also analyzed the effect of various factors on the measurements and found significant differences based on who took the measurements and jaw type. In conclusion, using the ResNet50 model to identify and measure gum tis

Background: Periodontitis is primarily driven by subgingival biofilm dysbiosis. However, the quantification and impact of this periodontal dysbiosis on other oral microbial niches remain unclear. This study seeks to quantify the dysbiotic changes in tongue and salivary microbiomes resulting from periodontitis by applying a clinically relevant dysbiosis index to an integrated data analysis.

Methods: The National Center for Biotechnology Information (NCBI) database was searched to identify BioProjects with published studies on salivary and tongue microbiomes of healthy and periodontitis subjects. Raw sequence datasets were processed using a standardized bioinformatic pipeline and categorized by their ecological niche and periodontal status. The subgingival microbial dysbiosis index (SMDI), a dysbiosis index originally developed using the subgingival microbiome, was computed at species and genus levels and customized for each niche. Its diagnostic accuracy for periodontitis was evaluated using receiver operating characteristic curves.

Results: Four studies, contributing 328 microbiome samples, were included. At both species and genus levels, periodontitis samples had a higher SMDI, but the differences were only significant for subgingival biofilm and saliva (p < 0.001). However, SMDI showed good diagnostic accuracy for periodontitis status for all three niches (area under curve ranging from 0.76 to 0.90, p < 0.05). The dysbiosis index of subgingival biofilm was positively correlated with saliva consistently (p < 0.001) and with the tongue at the genus level (p = 0.036).

Conclusions: While the impact on the tongue microbiome requires further investigation, periodontitis-associated dysbiosis affects the salivary microbiome and is quantifiable using the dysbiosis index. The diagnostic potential of salivary microbial dysbiosis as a convenient periodontal biomarker for assessing periodontal status has potential public health and clinical applications.

Plain language summary: Periodontitis, a severe inflammation of the gums which causes bone loss, is a disease caused by an imbalance of good and bad bacteria under the gums. However, it is unclear how this bacterial imbalance in the gums affects the bacterial balance of other distinct parts of the mouth, such as the saliva and tongue. This study uses bacteria datasets of four previously published studies, contributing a total of 328 bacterial samples. The data were processed using a uniform data analysis workflow, and a bacterial score, the subgingival microbial dysbiosis index (SMDI), previously shown to capture periodontitis-associated bacteria imbalance, was calculated separately for samples from under the gums, the saliva, and the tongue. The SMDI was able to distinguish between health and periodontitis within each oral location, and in general, the scores were higher for periodontitis samples

Background: To compare bone regeneration and dimensional alteration of alveolar ridge at intact and damaged extraction sockets after alveolar ridge preservation (ARP) and implant placement versus unassisted socket healing followed by guided bone regeneration (GBR) with simultaneous implant placement.

Methods: In 6 beagle dogs, 3 types of extraction sockets in the mandible were created: (1) intact sockets, (2) 1-wall defect sockets and (3) 2-wall defect sockets. The sockets were allocated to undergo either (1) ARP and implant placement 8 weeks later (ARP group) or (2) GBR with simultaneous implant placement after 8 weeks of unassisted socket healing (GBR group). After an additional healing period of 8 weeks, bone regeneration and dimensional changes were evaluated radiographically and histologically.

Results: GBR showed superior bone formation and greater bone gains compared to ARP, regardless of the initial extraction-socket configuration. Although ARP maintained the preexisting alveolar ridge dimensions, peri-implant bone defects were still detected at 8 weeks of follow-up. Histomorphometric analyses confirmed that GBR increased dimensions of the alveolar ridge compared to baseline, and the augmentation and bone regeneration were greater with GBR than with ARP.

Conclusion: Early implant placement with ARP can mitigate alveolar ridge changes in the narrow alveolar ridge. However, early implant placement with simultaneous GBR creates the conditions for enhanced bone regeneration around the implant and greater ridge augmentation compared to ARP, irrespective of the extraction-socket configuration.

Background: Subgingival dental plaque is an ecosystem playing a key role in supporting both oral health and systemic health. Menopause-related changes have the potential to disrupt its balance, which is crucial to postmenopausal well-being. Our study explored how circulating estradiol levels correlate with subgingival microbial composition using checkerboard DNA-DNA hybridization in premenopausal and postmenopausal women. We also demonstrated that combining this method with 16S ribosomal RNA (rRNA) sequencing insights remains valuable for examining subgingival ecology.

Methods: We assessed 40 bacterial species in 77 premenopausal and 81 postmenopausal women using checkerboard DNA-DNA hybridization and measured serum estradiol with enzyme-linked immunosorbent assay (ELISA). Women were categorized by subgingival dysbiosis severity using a modified Subgingival Microbial Dysbiosis Index (mSMDI). Six women from each normobiotic and dysbiotic subgroup across premenopausal and postmenopausal women underwent 16S rRNA sequencing analysis.

Results: DNA checkerboard analysis revealed that most observed variability in individual bacterial proportions is associated with periodontitis. Two species, Leptotrichia buccalis and Streptococcus constellatus, exhibited differences related to estradiol levels within the premenopausal group (p = 0.055 and p = 0.009, respectively). 16S rRNA sequencing confirmed the mSMDI's validity in categorizing normobiotic and dysbiotic states. Menopausal status was not associated with a dysbiotic shift in the subgingival microbiome despite significantly more attachment loss in postmenopausal compared to premenopausal women.

Conclusions: Our results indicate that decreased estradiol levels or increased attachment loss during menopause are not associated with changes in species abundance or dysbiotic shifts in women. The mSMDI may be a useful tool for classifying subgingival ecology based on its normobiotic or dysbiotic inclination.