Journal of Clinical Periodontology最新文献

Background and Objective

In humans 16 years and above with periodontitis (Population), how do psychological stress and stress-related disorders (PSRD; Exposure) compared to those without PSRD (Comparison) affect clinical attachment level (CAL) upon periodontitis treatment (Outcome), as observed in experimental studies or descriptive pre–post studies (Study design)?

Methods

Five major databases were searched for pre–post studies, descriptive cohort and quasi-experimental or randomised controlled trials (RCTs). For meta-analysis, mean differences in CAL and bleeding on probing (BOP) were pooled with random-effects models. Risk of bias, study quality and certainty of evidence were assessed.

Results

Thirteen studies (eight prospective, two retrospective, two quasi-experimental and one pilot RCT) with 1610 participants were identified. PSRD (measured as stress in nine studies, depression in three studies and anxiety in one study) showed a negative influence on periodontitis treatment (steps 1 + 2: eight studies, steps 1 + 2 + 3 or 4: four studies, step 4: one study) outcomes. Meta-analysis of steps 1 + 2 treatment results revealed that patients with PSRD (exposure: stress)—compared to those without—had smaller reductions in CAL (three studies; n = 170; weighted mean difference (WMD) = 0.78 mm; p = 0.01) and probing pocket depth (PPD; three studies; n = 170; WMD = 1.02 mm; p = 0.04) following periodontitis treatment in studies.

Conclusion

PSRD adversely affected periodontitis treatment outcomes, particularly concerning CAL and PPD, even though our confidence in the effect estimate is limited in the case of CAL and very low in the case of PPD. This conclusion, based on small treatment cohorts and quasi-experimental studies following steps 1 + 2 of periodontitis treatment over a 3–6-month period, warrants validation through rigorously designed studies.

Aim

To comprehensively characterise the bacterial microbiome in peri-implant health, peri-implant mucositis and peri-implantitis.

Materials and Methods

A re-analysis of raw microbiome data was performed from 15 studies, which were finally selected based on the availability of 16S rRNA sequencing. Reads were pre-processed using mothur and classified using the HOMD database. A total of 522 samples were analysed to evaluate diversity estimates and bacterial relative abundance, identifying discriminant features via LEfSe, while predictions of functional potential were obtained using PICRUSt2. Bacterial co-occurrence networks were constructed, and dysbiosis was measured by employing the subgingival microbiome dysbiosis index.

Results

Peri-implantitis showed higher bacterial diversity compared to health and greater microbial richness than peri-mucositis. Each clinical condition displayed a distinct community structure and bacterial co-occurrence networks. The representative species in peri-implant health were Rothia aeria , R. dentocariosa and Veillonella parvula_dispar. Peri-mucositis is characterised by Leptotrichia hongkongensis , L. wadei and Fusobacterium nucleatum subsp. polymorphum, while peri-implantitis is defined by Porphyromonas gingivalis , F. nucleatum subsp. vincentii and Tannerella forsythia . Peri-implantitis exhibited enrichment in predicted microbial pathogenesis pathways and greater bacterial dysbiosis.

Conclusions

These results provide deeper insights into the peri-implant microbiome, identifying key bacterial species, functional processes and interactions that may be crucial to inflammation and destruction during peri-implant diseases.

Aim

To evaluate the T regulatory lymphocyte (Treg) profile and its potential contribution to peri-implant tissue destruction during peri-implantitis (PI).

Methods

PI granulation tissue and crevicular fluid collected during PI surgical (PI group, n = 23) and explantation (PI-X group, n = 23) therapy, with peri-implant healthy tissue from second-stage surgery (H group, n = 20) as controls, were analysed. The inflammatory infiltrate was characterised by H&E staining. The relative expression of Treg-associated transcription factors and cytokines was assessed by RT-qPCR. Forkhead box P3 (FOXP3) and neuropilin (NPR)-1 were detected by immunohistochemistry, and interleukin (IL)-10, TGF-β1 and IL-35 by ELISA. The clinical parameters, namely probing depth (PD), bleeding on probing (BOP) and vertical defect depth (VDD), were also recorded.

Results

PI and PI-X lesions showed up-regulation of FOXP3, HELIOS and IL35B and down-regulation of NRP1 and TGFβ1 mRNA expression, compared to H tissue (p < 0.05). Significantly more FOXP3⁺ cells and significantly less NRP-1⁺ area were detected in PI and PI-X lesions (p < 0.05). IL-35 levels were up-regulated, whereas TGF-β1 levels were down-regulated in PI and PI-X lesions, compared to H samples (p < 0.05). PD and VDD were significantly correlated with the down-regulation of FOXP3 and NRP-1 (p < 0.05).

Conclusions

Treg dysfunction and altered cytokine profiles in PI are associated with inflammation and clinical disease severity.

Aim

To compare the histological healing between implants placed simultaneously with maxillary sinus floor augmentation (MSFA) and those placed with a staged approach in the maxillary sinus with thin bone height.

Materials and Methods

MSFA was performed on both sides of the sinuses in 10 rabbits, followed by simultaneous implant placement in one of the sinuses (group SMT). Four weeks later, implant placement was performed in the other sinus (group STG). The animals were euthanised 8 weeks thereafter. Micro-computed tomographic and histomorphometric analyses were performed.

Results

In micro-computed tomographic images, the implants were well surrounded by newly formed bone (NB) and bone substitute particles, without statistically significant difference in the volume of NB between the groups (p > 0.05). Histomorphometrically, the amount of NB within the total augmented area and ROIs near the implants did not significantly differ between the groups (p > 0.05). The percentage of bone-to-implant contact was not significantly different between the groups (52.2% ± 16.6% vs. 44.9% ± 18.4%; p > 0.05).

Conclusions

Simultaneous implant placement with MSFA resulted in comparable radiographic and histological outcomes to a staged implant placement approach in sinuses with thin bone height. However, such outcomes should be cautiously interpreted within the context of an animal model.

Aim

Oral dysbiosis and periodontal inflammation might play a role in oesophageal carcinogenesis via the translocation of periodontal pathogens. We aimed to assess whether poor oral health is associated with oesophageal cancer.

Methods

We conducted a population-based case–control study using the Korean National Health Insurance Service database to evaluate poor oral health as the exposure and oesophageal cancer as the outcome. Two study designs were employed: (1) oral health analysis using screening data, and (2) periodontal disease analysis using claims data. Oesophageal cancer cases were matched to cancer-free controls at a 1:4 ratio using extensive propensity score matching.

Results

After matching, 4238 cases and 16,904 controls were included in the oral health analysis, and 18,905 cases and 75,604 controls were included in the claims-based analysis. In the multivariable analysis, missing teeth (adjusted OR [aOR], 1.16; 95% confidence interval [CI]: 1.08–1.25) and periodontal disease (aOR, 1.05; 95% CI: 1.01–1.09) were both significantly associated with increased odds of oesophageal cancer. Poor oral hygiene practices including infrequent brushing, irregular bedtime brushing, lack of interdental cleaning and dental visits also showed positive associations. Subgroup analyses showed consistent associations across obesity and smoking status, whereas the associations were less consistent in women, the elderly and non-drinkers.

Conclusion

Poor oral health indicators including inadequate oral hygiene behaviours were associated with elevated odds of oesophageal cancer, suggesting the potential utility of oral health status in risk stratification.

Aim

To evaluate the 3-year clinical and radiographic outcomes of implant-supported restorations with different emergence profiles (CONVEX vs. CONCAVE).

Materials and Methods

A total of 47 patients received a single implant in the aesthetic zone and were allocated to one of three groups: (1) CONVEX: customized provisional with a convex emergence profile (n = 15); (2) CONCAVE: customized provisional with a concave profile (n = 16); (3) Control: no provisional restoration (n = 16). Final crowns in groups CONVEX and CONCAVE were fabricated to replicate the emergence profile of the respective provisional restorations. Follow-ups were performed at baseline, 6 months, 1 year and 3 years. The primary outcome was mid-facial mucosal recession and secondary outcomes included clinical, radiographic and aesthetic outcomes as well as profilometric measurements. Multivariable logistic regressions and mixed-effects models were used to compare the groups.

Results

Out of the 47 patients originally included, 42 were available for re-examination at 3 years follow-up. At 3 years, the frequency of mucosal recession amounted to 46.7% in group CONVEX, 13.3% in group CONCAVE and 40.0% in group Control. Adjusted logistic regression models revealed that the CONVEX group was significantly more likely to show recessions at 3 years (odds ratios [ORs]: 7.3, 95% CI: 1.02–52.14, p = 0.048) when compared with the CONCAVE group. No statistically significant difference in recession frequency was observed between the CONVEX and CONCAVE groups between the 1- and 3-year follow-ups (OR: 3.7, 95% CI: 0.30–46.09, p = 0.303).

Conclusion

The emergence profile design significantly influences soft tissue stability predominantly within the first year after crown insertion. Whenever clinically feasible, a CONCAVE profile is preferable in the aesthetic zone to maintain the level of the mid-facial mucosal margin and reduce the frequency of recessions.

Trial Registration: German Clinical Trials Register: DRKS00009420

Aim

The oral microbiota, a complex and dynamic ecosystem, plays a crucial role in human health, yet systematic studies across the lifespan remain limited. This study aimed to investigate variations in the oral microbiota and the effects of key influencing factors on the oral microbiota at different age groups.

Materials and Methods

In this study, we analysed the oral microbiota of 9662 individuals aged 14–69 years from the US National Health and Nutrition Examination Survey (NHANES) to explore the impact of demographic, lifestyle and environmental factors on microbial diversity and composition. Microbiological characterisation was done using the participants' oral rinses by 16S ribosomal RNA gene sequencing.

Results

Our findings revealed a clear age-related trend in microbial diversity, with Shannon diversity peaking in middle-aged and declining in older adults. The composition of the oral microbiota also varied significantly with age, as different genera exhibited distinct abundance patterns across the lifespan. Gender and race emerged as key influencing factors, with males showing greater Shannon diversity and greater relative abundances of Atopobium, Megasphaera and Porphyromonas spp., and Whites were enriched in Rothia and Veillonella. Socioeconomic factors and lifestyle, particularly smoking, were strongly associated with shifts in microbial communities.

Conclusions

These findings provide a comprehensive overview of the dynamic changes in the oral microbiota throughout life and underscore the intricate interplay between host and environmental factors in shaping microbial composition, offering a foundation for future research on microbiota-related health interventions.

Aim

Periodontitis proxy variables enable an expansion of periodontal research. The study aimed to estimate the validity of questionnaire items and registry data in relation to Stage III–IV periodontitis and having 50% bone loss.

Methods

Malmö Offspring Dental Study (MODS) participants (995) filled out questionnaires and underwent periodontal and panoramic radiography examinations. The questionnaire items, number of periodontal treatment procedures (PTP) in the Dental Health Register (DHR), and number of teeth with ≥ 6 mm probing depth in the Swedish Quality Register for Caries and Periodontal Disease (SKaPa) were evaluated as proxies for severe periodontitis. Stage III–IV periodontitis was the primary reference standard.

Results

For PTP-based severe periodontitis proxy in DHR, positive predictive value (PPV) was 88% and negative predictive value (NPV) 87% for Stage III–IV. The SKaPa-based proxy showed poor positive predictive values (PPVs, < 70%), but similar area under the curve (AUC), 0.74, compared with the DHR data (AUC 0.76). Sensitivity was < 70%, and specificity > 90% for the DHR and SKaPa proxies. Identification of cases with periodontitis by questionnaire combined with the demographic variables age, sex, smoking habits and education yielded good discriminatory ability (AUC > 0.75).

Conclusion

Register-based data can effectively identify individuals with severe periodontitis in large cohort studies, thereby advancing periodontal research.

Aim

To construct predictive models of periodontitis progression by applying Machine Learning (ML) to baseline data from a study of periodontitis progression.

Materials and Methods

Logistic regression (LR), multi-layer perceptron (MLP) and probabilistic graphic model (PGM) were utilised on data from a multi-centre longitudinal study in which periodontally healthy (n = 113) and periodontitis participants (n = 302) were examined bi-monthly for 12 months without treatment. Periodontal examination was performed, and salivary levels of 10 analytes were determined. Clinical and demographic parameters and analytes levels were input into the model. The performance of 14 models was compared using the area under the receiver operating characteristic curve (AUROC), and feature importance was assessed using SHapley Additive exPlanations (SHAP).

Results

The PGM model (Clinical measures, saliva IL-1β, age, sex) demonstrated the best overall performance (AUROC = 0.88), compared to LR (AUROC = 0.72) and MLP (AUROC = 0.58). Although MLP had a lower Brier score (0.12), its sensitivity was 0, limiting its clinical utility. In contrast, PGM achieved a balanced sensitivity (0.55) and specificity (0.81). Feature importance analyses highlighted the number of deep periodontal pockets as a key driver of model predictions in both PGM and MLP.

Conclusions

ML models can predict periodontitis progression, supporting early detection strategies. Our integrative approach, combining clinical data with salivary biomarkers such as IL-1β, improved predictive accuracy.

Aim

Saliva is a diagnostic surrogate for microbial and host biomarkers in periodontitis, but whether it reflects subgingival plaque, gingival crevicular fluid (GCF) or serum remains debated. This study profiled GCF and subgingival plaque from all sites of periodontitis patients, comparing them to saliva and serum.

Materials and Methods

Saliva, serum, subgingival plaque and GCF were obtained from three patients with stage III, grade C periodontitis, having 23, 25 and 27 teeth, respectively, with six sites sampled per tooth. All plaque and GCF samples were pooled per patient. Shotgun sequencing and mass spectrometry proteomics were used for microbiome and proteome analysis, respectively.

Results

Totally 277 microbial taxa were collectively identified in saliva and plaque, 93 of which were differentially abundant between the two. Saliva exhibited higher overall species diversity, but lower periodontal pathogen abundance. A total of 1153 host proteins were identified (saliva: 803; GCF: 932; serum: 195) across the three biological fluids, with 685 shared among saliva and GCF and 109 among all three. Saliva contained slightly fewer proteins than GCF, but shared several common immune, metabolic and enzyme regulation pathways.

Conclusion

Saliva is effective for broad microbiome and proteome screening; whereas plaque delivers greater precision in identifying specific periodontal pathogens directly associated with a periodontal pocket.