Modelling changes in clinical attachment loss to classify periodontal disease progression

Salivary periostin levels as a non-invasive biomarker and their clinical correlates among healthy and periodontitis patients-a cross-sectional analytical study

Background

The diagnosis of periodontitis is primarily through clinical and radiographic assessments. However, it is difficult for clinicians to detect incipient periodontitis during the routine clinical assessment. Identifying people at risk for periodontitis and tracking disease development need a dependable biomarker. Currently, no biomarkers meet all the criteria required for an ideal diagnostic test. Therefore, the clinical utility of salivary periostin as a potential screening tool for periodontitis warrants further investigation, particularly through large samples across diverse populations. The present study aimed to investigate salivary periostin levels as a biomarker in individuals with periodontitis and healthy controls.

Methods

Forty-five patients with generalized periodontitis stage III grade A/B and an equivalent number of periodontally healthy controls were evaluated for plaque index (PI), gingival index (GI), pocket probing depth (PPD), and clinical attachment level (CAL). Unstimulated salivary samples from all subjects were taken, and periostin levels were quantified using an ELISA kit.

Results

The average salivary periostin levels were 4.63 in the healthy group and 1.24 in the periodontitis group (P < 0.05). The Spearman coefficient indicated a negative correlation between periostin levels and the gingival index (r = -0.761), plaque index (r = -0.780; P < 0.05), probing pocket depth (PPD) (r = -0.713; P < 0.05) and clinical attachment level (CAL) (r = -0.713; P < 0.05). Linear regression analysis validated the indirect correlation between salivary periostin levels and clinical indicators (Adjusted R square = 0.947).

Conclusions

Salivary periostin levels are associated with periodontal disease. Salivary periostin levels indirectly influence as a non-invasive biomarker of periodontitis. The biomarker periostin is effective for evaluating both healthy and diseased periodontium.

Application of the 2018 Periodontal Status Classification to Epidemiological Survey Data (ACES) Framework to Estimate the Periodontitis Prevalence in the United States

AIMS

To compare periodontitis prevalence estimates based on the Application of the 2018 periodontal status Classification to Epidemiological Survey data (ACES) and the Centers for Disease Control and Prevention/American Academy of Periodontology (CDC/AAP) classification.

MATERIALS AND METHODS

National Health and Nutrition Examination Survey data for the years 2009/2010, 2011/2012 and 2013/2014 were survey-weighted and post-stratified to estimate the prevalence of periodontitis. Estimates based on ACES and CDC/AAP were cross-classified and stratified by age group. Prevalence estimates using different partial recording protocols were examined.

RESULTS

Using the ACES framework, the prevalence of adults with periodontitis was 93.1% (95% CI: 91.9-94.2) (Stage I: 17.9%; Stage II: 46.2%; Stage III: 16.7%; Stage IV: 12.4%). Complexity factors did not alter Stage II prevalence. The CDC/AAP classification yielded a periodontitis prevalence of 38.9% (95% CI: 36.4-41.4) (Mild: 3.5%; Moderate: 28.1%; Severe: 7.3%). Partial recording protocols resulted in increased prevalence in the lower stages of periodontitis.

CONCLUSIONS

The European Federation of Periodontology/American Academy of Periodontology Classification (using the ACES framework) overestimates periodontitis cases compared with the CDC/AAP classification. Including complexity factors in the ACES framework provides limited benefits in staging periodontitis. Partial-mouth recording protocols overestimate health and early disease stages while underestimating more severe disease.

Gingival Crevicular Fluid Biomarkers During Periodontitis Progression and After Periodontal Treatment

OBJECTIVE

To identify gingival crevicular fluid (GCF)-derived inflammatory markers of periodontitis progression and periodontal treatment impact.

METHODS

Periodontally healthy (H; n = 112) and periodontitis (P; n = 302) patients were monitored bi-monthly for 1 year without therapy. Periodontitis patients were re-examined 6 months after non-surgical periodontal therapy (NSPT). Levels of 64 biomarkers were measured in the GCF samples collected at each visit from progressing (n = 12 sites in H; n = 76 in P) and stable (n = 100 in H, n = 225 in P) sites. Clinical parameters and log-transformed analyte levels were averaged within clinical groups at each time point and analysed using linear mixed models.

RESULTS

During monitoring, progressing sites had significantly higher levels of IL-1β, MMP-8, IL-12p40, EGF and VEGF. MMP-9 and Periostin were significantly more elevated in stable sites. Distinct cytokine profiles were observed based on baseline PD. Treatment led to significant reductions in Eotaxin, Flt-3L, GDF-15, GM-CSF, IL-1β, IL-17, MIP-1d, RANTES and sCD40L, and increases in IP-10 and MMP-9.

CONCLUSION

Distinct cytokine signatures observed in stable and progressing sites were maintained over time in the absence of treatment and significantly affected by NSPT.

Salivary and serum inflammatory biomarkers during periodontitis progression and after treatment

AIM

To identify serum- and salivary-derived inflammatory biomarkers of periodontitis progression and determine their response to non-surgical treatment.

MATERIALS AND METHODS

Periodontally healthy (H; n = 113) and periodontitis patients (P; n = 302) were monitored bi-monthly for 1 year without therapy. Periodontitis patients were re-examined 6 months after non-surgical periodontal therapy (NSPT). Participants were classified according to disease progression: P0 (no sites progressed; P1: 1-2 sites progressed; P2: 3 or more sites progressed). Ten salivary and five serum biomarkers were measured using Luminex. Log-transformed levels were compared over time according to baseline diagnosis, progression trajectory and after NSPT. Significant differences were sought using linear mixed models.

RESULTS

P2 presented higher levels (p < .05) of salivary IFNγ, IL-6, VEGF, IL-1β, MMP-8, IL-10 and OPG over time. Serum analytes were not associated with progression. NSPT led to clinical improvement and significant reduction of IFNγ, IL-6, IL-8, IL-1β, MMP-8, IL-10, OPG and MMP-9 in saliva and of CRP, MMP-8, MMP-9 and MPO in serum.

CONCLUSIONS

Periodontitis progression results from a sustained pro-inflammatory milieu that is reflected in salivary biomarkers, but less so in serum, likely because of the limited amount of progression per patient. NSPT can significantly decrease the levels of several salivary analytes.

A multi-platform analysis of human gingival crevicular fluid reveals ferroptosis as a relevant regulated cell death mechanism during the clinical progression of periodontitis

Ferroptosis is implicated in the pathogenesis of numerous chronic-inflammatory diseases, yet its association with progressive periodontitis remains unexplored. To investigate the involvement and significance of ferroptosis in periodontitis progression, we assessed sixteen periodontitis-diagnosed patients. Disease progression was clinically monitored over twelve weeks via weekly clinical evaluations and gingival crevicular fluid (GCF) collection was performed for further analyses. Clinical metrics, proteomic data, in silico methods, and bioinformatics tools were combined to identify protein profiles linked to periodontitis progression and to explore their potential connection with ferroptosis. Subsequent western blot analyses validated key findings. Finally, a single-cell RNA sequencing (scRNA-seq) dataset (GSE164241) for gingival tissues was analyzed to elucidate cellular dynamics during periodontitis progression. Periodontitis progression was identified as occurring at a faster rate than traditionally thought. GCF samples from progressing and non-progressing periodontal sites showed quantitative and qualitatively distinct proteomic profiles. In addition, specific biological processes and molecular functions during progressive periodontitis were revealed and a set of hub proteins, including SNCA, CA1, HBB, SLC4A1, and ANK1 was strongly associated with the clinical progression status of periodontitis. Moreover, we found specific proteins - drivers or suppressors - associated with ferroptosis (SNCA, FTH1, HSPB1, CD44, and GCLC), revealing the co-occurrence of this specific type of regulated cell death during the clinical progression of periodontitis. Additionally, the integration of quantitative proteomic data with scRNA-seq analysis suggested the susceptibility of fibroblasts to ferroptosis. Our analyses reveal proteins and processes linked to ferroptosis for the first time in periodontal patients, which offer new insights into the molecular mechanisms of progressive periodontal disease. These findings may lead to novel diagnostic and therapeutic strategies.

Proteomic profile of human gingival crevicular fluid reveals specific biological and molecular processes during clinical progression of periodontitis

BACKGROUND AND OBJECTIVE

There is no clear understanding of molecular events occurring in the periodontal microenvironment during clinical disease progression. Our aim was to explore qualitative and quantitative differences in gingival crevicular fluid (GCF) protein profiles from patients diagnosed with periodontitis between non-progressive and progressive periodontal sites.

METHODS

Five systemically healthy patients diagnosed with periodontitis were monitored weekly in their progression of the disease and GCF samples from 10 candidate sites were obtained. Two groups of five sites, matched from an equal number of teeth, were selected from the five patients: Progression (PG) and Non-Progression (NP). Global protein identification was performed with high-throughput proteomic approaches and label-free analysis determined their relative abundances. Proteins were identified by Proteome Discoverer v2.4 and searched against human SwissProt protein databases. Enrichment bioinformatic analyses were performed in STRING-DB and ShinyGO environment.

RESULTS

1504 and 1500 proteins were identified in NP and PG respectively. Forty-eight proteins were exclusively identified in PG, while 52 were identified in NP. Moreover, 35 proteins were more abundant in PG and 29 proteins in NP (twofold change, p < .05). The NP group was mainly represented by proteins from "response to biotic stimuli and other organisms," "processes of cell death regulation," "peptidase regulation," "protein ubiquitination," and "ribosomal activity" GO categories. The most represented GO categories of the PG group were "assembly of multiprotein complexes," "catabolic processes," "lipid metabolism," and "binding to hemoglobin and haptoglobin."

CONCLUSIONS

There are quantitative and qualitative differences in the proteome of GCF from periodontal sites according to the status of clinical progression of periodontitis. Progressive periodontitis sites are characterized by a protein profile associated with catabolic processes, immune response, and response to cellular stress, while stable periodontitis sites show a protein profile mainly related to wound repair and healing processes, cell death regulation, and chaperone-mediated autophagy. Understanding the etiopathogenic role of these profiles in progressive periodontitis may help to develop new diagnostic and therapeutic approaches.

Pursuing new periodontal pathogens with an improved RNA-oligonucleotide quantification technique (ROQT)

OBJECTIVE

The aim of this study was to optimize the sensitivity, specificity and cost-effectiveness of the RNA-Oligonucleotide Quantification Technique (ROQT) in order to identify periodontal pathogens that remain unrecognized or uncultured in the oral microbiome.

DESIGN

Total nucleic acids (TNA) were extracted from subgingival biofilm samples using an automated process. RNA, DNA and Locked Nucleic Acid (LNA) digoxigenin-labeled oligonucleotide probes targeting 5 cultivated/named species and 16 uncultivated or unnamed bacterial taxa were synthesized. Probe specificity was determined by targeting 96 oral bacterial species; sensitivity was assessed using serial dilutions of reference bacterial strains. Different stringency temperatures were compared and new standards were tested. The tested conditions were evaluated analyzing samples from periodontally healthy individuals, and patients with moderate or severe periodontitis.

RESULTS

The automated extraction method at 63⁰C along with LNA-oligunucleotides probes, and use of reverse RNA sequences for standards yielded stronger signals without cross-reactions. In the pilot clinical study, the most commonly detected uncultivated/unrecognized species were Selenomonas sp. HMT 134, Prevotella sp. HMT 306, Desulfobulbus sp. HMT 041, Synergistetes sp. HMT 360 and Bacteroidetes HMT 274. In the cultivated segment of the microbiota, the most abundant taxa were T. forsythia HMT 613 and Fretibacterium fastidiosum (formerly Synergistetes) HMT 363.

CONCLUSIONS

In general, samples from severe patients had the greatest levels of organisms. Classic (T. forsythia, P. gingivalis) and newly proposed (F. alocis and Desulfobulbus sp. HMT 041) pathogens were present in greater amounts in samples from severe periodontitis sites, followed by moderate periodontitis sites.

Subgingival host-microbiome metatranscriptomic changes following scaling and root planing in grade II/III periodontitis

AIM

To assess the effects of scaling and root planing (SRP) on the dynamics of gene expression by the host and the microbiome in subgingival plaque samples.

MATERIALS AND METHODS

Fourteen periodontitis patients were closely monitored in the absence of periodontal treatment for 12 months. During this period, comprehensive periodontal examination and subgingival biofilm sample collection were performed bi-monthly. After 12 months, clinical attachment level (CAL) data were compiled and analysed using linear mixed models (LMM) fitted to longitudinal CAL measurements for each tooth site. LMM classified the sites as stable (S), progressing (P), or fluctuating (F). After the 12-month visit, subjects received SRP, and at 15 months they received comprehensive examination and supportive periodontal therapy. Those procedures were repeated at the 18-month visit, when patients were also sampled. Each patient contributed with one S, one P, and one F site collected at the 12- and 18-month visits. Samples were analysed using Dual RNA-Sequencing to capture host and bacterial transcriptomes simultaneously.

RESULTS

Microbiome and host response behaviour were specific to the site's progression classification (i.e., S, P, or F). Microbial profiles of pre- and post-treatment samples exhibited specific microbiome changes, with progressing sites showing the most significant changes. Among them, Porphyromonas gingivalis was reduced after treatment, while Fusobacterium nucleatum showed an increase in proportion. Transcriptome analysis of the host response showed that interleukin (IL)-17, TNF signalling pathways, and neutrophil extracellular trap formation were the primary immune response activities impacted by periodontal treatment.

CONCLUSIONS

SRP resulted in a significant "rewiring" of host and microbial activities in the progressing sites, while restructuring of the microbiome was minor in stable and fluctuating sites.

Viruses, periodontitis, and comorbidities

Seminal studies published in the 1990s and 2000s explored connections between periodontal diseases and systemic conditions, revealing potential contributions of periodontal diseases in the initiation or worsening of systemic conditions. The resulting field of periodontal medicine led to the publication of studies indicating that periodontal diseases can influence the risk of systemic conditions, including adverse pregnancy outcomes, cardiovascular and respiratory diseases, as well as Alzheimer disease and cancers. In general, these studies hypothesized that the periodontal bacterial insult and/or the associated proinflammatory cascade could contribute to the pathogenesis of these systemic diseases. While investigations of the biological basis of the connections between periodontal diseases and systemic conditions generally emphasized the bacteriome, it is also biologically plausible, under an analogous hypothesis, that other types of organisms may have a similar role. Human viruses would be logical "suspects" in this role, given their ubiquity in the oral cavity, association with periodontal diseases, and ability to elicit strong inflammatory response, compromise immune responses, and synergize with bacteria in favor of a more pathogenic microbial consortium. In this review, the current knowledge of the role of viruses in connecting periodontal diseases and systemic conditions is examined. We will also delve into the mechanistic basis for such connections and highlight the importance of those relationships in the management and treatment of patients.

Long-term dynamics of the human oral microbiome during clinical disease progression

BACKGROUND

Oral microbiome dysbiosis is linked to overt inflammation of tooth-supporting tissues, leading to periodontitis, an oral condition that can cause tooth and bone loss. Microbiome dysbiosis has been described as a disruption in the symbiotic microbiota composition's stability that could adversely affect the host's health status. However, the precise microbiome dynamics that lead to dysbiosis and the progression of the disease are largely unknown. The objective of our study was to investigate the long-term dynamics of periodontitis progression and its connection to dysbiosis.

RESULTS

We studied three different teeth groups: sites that showed disease progression, sites that remained stable during the study, and sites that exhibited a cyclic deepening followed by spontaneous recovery. Time-series analysis revealed that communities followed a characteristic succession of bacteria clusters. Stable and fluctuating sites showed high asynchrony in the communities (i.e., different species responding dissimilarly through time) and a reordering of the communities where directional changes dominated (i.e., sample distance increases over time) in the stable sites but not in the fluctuating sites. Progressing sites exhibited low asynchrony and convergence (i.e., samples distance decreases over time). Moreover, new species were more likely to be recruited in stable samples if a close relative was not recruited previously. In contrast, progressing and fluctuating sites followed a neutral recruitment model, indicating that competition between closely related species is a significant component of species-species interactions in stable samples. Finally, periodontal treatment did not select similar communities but stabilized α-diversity, centered the abundance of different clusters to the mean, and increased community rearrangement.

CONCLUSIONS

Here, we show that ecological principles can define dysbiosis and explain the evolution and outcomes of specific microbial communities of the oral microbiome in periodontitis progression. All sites showed an ecological succession in community composition. Stable sites were characterized by high asynchrony, a reordering of the communities where directional changes dominated, and new species were more likely to be recruited if a close relative was not recruited previously. Progressing sites were characterized by low asynchrony, community convergence, and a neutral model of recruitment. Finally, fluctuating sites were characterized by high asynchrony, community convergence, and a neutral recruitment model.

Impact of interproximal composite restorations on periodontal tissue health: Clinical and cytokine profiles from a pre-post quasi-experimental study

BACKGROUND

The aim of this study is to clinically and molecularly evaluate the effect of an interproximal iuxta/subgingival direct composite restoration on periodontal tissue healing.

METHODS

Individuals in need of a posterior iuxta/subgingival interproximal restoration were consecutively enrolled. After enrollment, a test (site with tooth decay) and a control site (intact contralateral tooth) were identified. After a periodontal examination (probing depth [PD], clinical attachment level, recession, plaque, and bleeding on probing [BOP]) and a sampling of gingival crevicular fluid, the composite restoration was performed (T₀ ). Clinical and molecular assessments were repeated at 3 (T₃ ), 6 (T₆ ), and 12 (T₁₂ ) months after the restoration. Intragroup pre-post comparisons for quantitative variables were performed either through one-way ANOVA or Kruskal-Wallis test. A multivariate linear regression analysis was then modeled. With α = 0.05, a power of 80% will be reached with the inclusion of 41 individuals.

RESULTS

Biometric parameters demonstrated an increased mean PD (ΔPDT₀ -T₁₂ = -0.83 mm; P = 0.001) and loss of attachment (AL) (ΔCALT₀ -T₁₂ = -0.91 mm; P = 0.005) in the test site at 12 months. Accordingly, in the final multivariate regression model the radiographic distance between the bone crest and the restorative margin at baseline accounted for the dependent variable "attachment loss (AL)" (ΔCALT₀ -T₁₂ ) (P <0.05).

CONCLUSIONS

Iuxta/subgingival interproximal restorative margins jeopardized clinically and molecularly the periodontal tissue healing at least up to 1 year of follow-up.

Evaluation of periostin levels in gingival crevicular fluid in association between coronary heart disease and chronic periodontitis

BACKGROUND

Periostin is a protein, which is essential for periodontal tissue integrity, development and maturity. The aim of this study was to evaluate the role of gingival crevicular fluid (GCF) periostin levels in the association between coronary heart disease (CHD) and chronic periodontitis (CP).

MATERIALS AND METHODS

This matched case-control study was conducted on 116 participants. The participants were matched for age, gender, and body mass index and divided into four groups as follows: (1) 29 patients with CHD and sever CP (CHD-CP), (2) 29 patients with CHD and without CP (CHD-H), (3) 29 patients without CHD and with sever CP (H-CP), and (4) 29 healthy participants (H-H). The GCF periostin was collected and evaluated using the enzyme-linked immunosorbent assay (ELASA). Finally, the data were analyzed by analysis of variance using the stata software. Significance was assigned at P < 0.05.

RESULTS

The results showed that there was a significant difference in the GCF periostin levels in the four groups (P < 0.05). Moreover, according to the results of the Bonferroni's test, differences in the mean periostin levels were significant (P < 0.001) between CHD-CP and CHD-H, CHD-CP and H-CP, CHD-CP and H-H, CHD-H and H-H, and also between H-CP and H-H.

CONCLUSION

The periostin levels reduced in the CHD patients, especially in the CHD-CP group. The findings reveal a probable role of periostin in the association between CHD and CP.

Subgingival microbiome is associated with alveolar bone loss measured 5 years later in postmenopausal women

BACKGROUND

The aim of this study was to quantify the association between subgingival microbiota and periodontal disease progression in older women, for which limited published data exist.

METHODS

A total of 1016 postmenopausal women, aged 53 to 81 years, completed baseline (1997 to 2001) and 5-year (2002 to 2006) dental exams that included probing depth, clinical attachment level, gingival bleeding, and radiographic alveolar crestal height (ACH). Baseline microbiota were measured in subgingival plaque using 16S rRNA sequencing. Associations between 52 microbiota we previously found statistically significantly associated with clinical periodontal disease at baseline, were examined with disease progression. The traditional Socransky microbiota complexes also were evaluated. Side-by-side radiograph comparisons were used to define progression as ≥2 teeth with ≥1 mm ACH loss or ≥1 new tooth loss to periodontitis. The association between baseline centered log(2) ratio transformed microbial relative abundances and 5-year periodontal disease progression was measured with generalized linear models.

RESULTS

Of 36 microbiota we previously showed were elevated in moderate/severe disease at baseline, 24 had statistically significantly higher baseline mean relative abundance in progressing compared with non-progressing women (P < .05, all); which included all Socransky red bacteria (P. gingivalis, T. forsythia, T. denticola). Of 16 microbiota elevated in none/mild disease at baseline, five had statistically significantly lower baseline abundance in non-progressing compared with progressing women (P < 0.05, all), including one Socransky yellow bacteria (S. oralis). When adjusted for baseline age, socioeconomic status, and self-rated general health status, odds ratios for 5-year progression ranged from 1.18 to 1.51 (per 1-standard deviation increment in relative abundance) for microbiota statistically significantly (P < 0.05) positively associated with progression, and from 0.77 to 0.82 for those statistically significantly (P < 0.05) inversely associated with progression. These associations were similar when stratified on baseline levels of pocket depth, gingival bleeding, ACH, and smoking status.

CONCLUSIONS

These prospective results affirm clearly that subgingival microbiota are measurably elevated several years prior to progression of alveolar bone loss, and include antecedent elevations in previously undocumented taxa additional to known Socransky pathogenic complexes.

Biosensor and Lab-on-a-chip Biomarker-identifying Technologies for Oral and Periodontal Diseases

Periodontitis is a complex multifactorial disease that can lead to destruction of tooth supporting tissues and subsequent tooth loss. The most recent global burden of disease studies highlight that severe periodontitis is one of the most prevalent chronic inflammatory conditions affecting humans. Periodontitis risk is attributed to genetics, host-microbiome and environmental factors. Empirical diagnostic and prognostic systems have yet to be validated in the field of periodontics. Early diagnosis and intervention prevents periodontitis progression in most patients. Increased susceptibility and suboptimal control of modifiable risk factors can result in poor response to therapy, and relapse. The chronic immune-inflammatory response to microbial biofilms at the tooth or dental implant surface is associated with systemic conditions such as cardiovascular disease, diabetes or gastrointestinal diseases. Oral fluid-based biomarkers have demonstrated easy accessibility and potential as diagnostics for oral and systemic diseases, including the identification of SARS-CoV-2 in saliva. Advances in biotechnology have led to innovations in lab-on-a-chip and biosensors to interface with oral-based biomarker assessment. This review highlights new developments in oral biomarker discovery and their validation for clinical application to advance precision oral medicine through improved diagnosis, prognosis and patient stratification. Their potential to improve clinical outcomes of periodontitis and associated chronic conditions will benefit the dental and overall public health.

Determinants of Periodontal/Periapical Lesion Stability and Progression

Periodontal and periapical lesions are infectious inflammatory osteolitytic conditions in which a complex inflammatory immune response mediates bone destruction. However, the uncertainty of a lesion's progressive or stable phenotype complicates understanding of the cellular and molecular mechanisms triggering lesion activity. Evidence from clinical and preclinical studies of both periodontal and periapical lesions points to a high receptor activator of NF-κB ligand/osteoprotegerin (RANKL/OPG) ratio as the primary determinant of osteolytic activity, while a low RANKL/OPG ratio is often observed in inactive lesions. Proinflammatory cytokines directly modulate RANKL/OPG expression and consequently drive lesion progression, along with pro-osteoclastogenic support provided by Th1, Th17, and B cells. Conversely, the cooperative action between Th2 and Tregs subsets creates an anti-inflammatory and proreparative milieu associated with lesion stability. Interestingly, the trigger for lesion status switch from active to inactive can originate from an unanticipated RANKL immunoregulatory feedback, involving the induction of Tregs and a host response outcome with immunological tolerance features. In this context, dendritic cells (DCs) appear as potential determinants of host response switch, since RANKL imprint a tolerogenic phenotype in DCs, described to be involved in both Tregs and immunological tolerance generation. The tolerance state systemically and locally suppresses the development of exacerbated and pathogenic responses and contributes to lesions stability. However, immunological tolerance break by comorbidities or dysbiosis could explain lesions relapse toward activity. Therefore, this article will provide a critical review of the current knowledge concerning periodontal and periapical lesions activity and the underlying molecular mechanisms associated with the host response. Further studies are required to unravel the role of immunological responsiveness or tolerance in the determination of lesion status, as well as the potential cooperative and/or inhibitory interplay among effector cells and their impact on RANKL/OPG balance and lesion outcome.

Patterns of periodontal disease progression based on linear mixed models of clinical attachment loss

AIM

The goal of the present longitudinal cohort study was to examine patterns of periodontal disease progression at progressing sites and subjects defined based on linear mixed models (LMM) of clinical attachment loss (CAL).

MATERIALS AND METHODS

A total of 113 periodontally healthy and 302 periodontitis subjects had their CAL calculated bimonthly for 12 months. LMMs were fitted for each site and the predicted CAL levels used to categorize their progression state. Participants were grouped based on the number of progressing sites into unchanged, transitional and active subjects. Patterns of periodontal disease progression were explored using descriptive statistics.

RESULTS

Progression occurred primarily at molars (50% of progressing sites) and inter-proximal sites (72%), affected a higher proportion of deep than shallow sites (2.7% versus 0.7%), and pocketing was the main mode of progression (49%). We found a low level of agreement (47%) between the LMM and traditional approaches to determine progression such as change in CAL ≥3 mm. Fourteen per cent of subjects were classified as active and among those 93% had periodontitis. The annual mean rate of progression for the active subjects was 0.35 mm/year.

CONCLUSION

Progressing sites and subjects defined based on LMMs presented patterns of disease progression similar to those previously reported in the literature.

Influence of Periodontal Disease on Changes of Glycated Hemoglobin Levels in Patients With Type 2 Diabetes Mellitus: A Retrospective Cohort Study

BACKGROUND

Little evidence is available regarding the effects of long-term periodontal infection on diabetes mellitus (DM) control. The aim of this retrospective cohort study is to evaluate influence of periodontal status on changes of glycated hemoglobin (HbA1c) levels of patients with type 2 DM (DMt2).

METHODS

Eighty patients (mean age: 56.0 ± 8.9 years) with DMt2 were included. Patients were non-smokers, aged ≥40 years, and using antidiabetic drugs. Demographics, health history, and HbA1c levels were retrieved from medical charts. Probing depth and clinical attachment loss (AL) were recorded.

RESULTS

Patients were examined at two time points within a mean interval of 38.6 ± 6.6 months. Increase in HbA1c over time was statistically significant when severe periodontitis was diagnosed at baseline (2.32%, 95% confidence interval [CI]: 1.50% to 3.15%), in patients showing at least one tooth with ≥2 mm of AL progression (2.24%, 95% CI: 1.56% to 2.91%), in males (2.75%, 95% CI: 1.72% to 3.78%), and in those with HbA1c <6.5% at baseline (3.08%, 95% CI: 2.47% to 3.69%). After adjusting for baseline HbA1c, significant changes were still observed for severe periodontitis and progression of AL with increases of 0.85% and 0.9%, respectively. After adjusting for sex and HbA1c, AL progression was also statistically significant, with increases of 0.84%.

CONCLUSIONS

Periodontitis progression was associated with increase in HbA1c in patients with DMt2. Identification of these risk factors suggests that periodontal treatment may improve glycemic control of patients with DMt2 by eliminating periodontal infection.

Polymeric Nanoparticle-Based Photodynamic Therapy for Chronic Periodontitis in Vivo

Antimicrobial photodynamic therapy (aPDT) is increasingly being explored for treatment of periodontitis. Here, we investigated the effect of aPDT on human dental plaque bacteria in suspensions and biofilms in vitro using methylene blue (MB)-loaded poly(lactic-co-glycolic) (PLGA) nanoparticles (MB-NP) and red light at 660 nm. The effect of MB-NP-based aPDT was also evaluated in a clinical pilot study with 10 adult human subjects with chronic periodontitis. Dental plaque samples from human subjects were exposed to aPDT—in planktonic and biofilm phases—with MB or MB-NP (25 µg/mL) at 20 J/cm²in vitro. Patients were treated either with ultrasonic scaling and scaling and root planing (US + SRP) or ultrasonic scaling + SRP + aPDT with MB-NP (25 µg/mL and 20 J/cm²) in a split-mouth design. In biofilms, MB-NP eliminated approximately 25% more bacteria than free MB. The clinical study demonstrated the safety of aPDT. Both groups showed similar improvements of clinical parameters one month following treatments. However, at three months ultrasonic SRP + aPDT showed a greater effect (28.82%) on gingival bleeding index (GBI) compared to ultrasonic SRP. The utilization of PLGA nanoparticles encapsulated with MB may be a promising adjunct in antimicrobial periodontal treatment.

Modelling changes in clinical attachment loss to classify periodontal disease progression

AIM

The goal of this study was to identify progressing periodontal sites by applying linear mixed models (LMM) to longitudinal measurements of clinical attachment loss (CAL).

METHODS

Ninety-three periodontally healthy and 236 periodontitis subjects had their CAL measured bi-monthly for 12 months. The proportions of sites demonstrating increases in CAL from baseline above specified thresholds were calculated for each visit. The proportions of sites reversing from the progressing state were also computed. LMM were fitted for each tooth site and the predicted CAL levels used to categorize sites regarding progression or regression. The threshold for progression was established based on the model-estimated error in predictions.

RESULTS

Over 12 months, 21.2%, 2.8% and 0.3% of sites progressed, according to thresholds of 1, 2 and 3 mm of CAL increase. However, on average, 42.0%, 64.4% and 77.7% of progressing sites for the different thresholds reversed in subsequent visits. Conversely, 97.1%, 76.9% and 23.1% of sites classified as progressing using LMM had observed CAL increases above 1, 2 and 3 mm after 12 months, whereas mean rates of reversal were 10.6%, 30.2% and 53.0% respectively.

CONCLUSION

LMM accounted for several sources of error in longitudinal CAL measurement, providing an improved method for classifying progressing sites.