Prediction models for the incidence and progression of periodontitis: A systematic review

Contesting the conventional wisdom of periodontal risk assessment

Over the years, several reviews of periodontal risk assessment tools have been published. However, major misunderstandings still prevail in repeated attempts to use these tools for prognostic risk prediction. Here we review the principles of risk prediction and discuss the value and the challenges of using prediction models in periodontology. Most periodontal risk prediction models have not been properly developed according to guidance given for the risk prediction model development. This shortcoming has led to several problems, including the creation of arbitrary risk scores. These scores are often labelled as 'high risk' without explicit boundaries or thresholds for the underlying continuous risk estimates of patient-important outcomes. Moreover, it is apparent that prediction models are often misinterpreted as causal models by clinicians and researchers although they cannot be used as such. Additional challenges like the critical assessment of transportability and applicability of these prediction models, as well as their impact on clinical practice and patient outcomes, are not considered in the literature. Nevertheless, these instruments are promoted with claims regarding their ability to deliver more individualized and precise periodontitis treatment and prevention, purportedly resulting in improved patient outcomes. However, people with or without periodontitis deserve proper information about their risk of developing patient-important outcomes such as tooth loss or pain. The primary objective of disseminating such information should not be to emphasize assumed treatment efficacy, hype individualization of care, or promote business interests. Instead, the focus should be on providing individuals with locally validated and regularly updated predictions of specific risks based on readily accessible and valid key predictors (e.g. age and smoking).

A new direction in periodontitis treatment: biomaterial-mediated macrophage immunotherapy

Periodontitis is a chronic inflammation caused by a bacterial infection and is intimately associated with an overactive immune response. Biomaterials are being utilized more frequently in periodontal therapy due to their designability and unique drug delivery system. However, local and systemic immune response reactions driven by the implantation of biomaterials could result in inflammation, tissue damage, and fibrosis, which could end up with the failure of the implantation. Therefore, immunological adjustment of biomaterials through precise design can reduce the host reaction while eliminating the periodontal tissue's long-term chronic inflammation response. It is important to note that macrophages are an active immune system component that can participate in the progression of periodontal disease through intricate polarization mechanisms. And modulating macrophage polarization by designing biomaterials has emerged as a new periodontal therapy technique. In this review, we discuss the role of macrophages in periodontitis and typical strategies for polarizing macrophages with biomaterials. Subsequently, we discuss the challenges and potential opportunities of using biomaterials to manipulate periodontal macrophages to facilitate periodontal regeneration.

Data-driven prediction model for periodontal disease based on correlational feature analysis and clinical validation

Objectives

This study aimed to investigate the performance and reliability of data-driven models employing correlational feature analysis and clinical validation for predicting periodontal disease.

Methods

The 7th Korea National Health and Nutrition Examination Survey (n = 10,654) was used for correlation analysis to identify significant risk factors for periodontitis. Periodontal prediction models were developed with the selected factors and database, followed by internal validation with 5-fold cross-validation and 1000 bootstrap resampling. External validation was conducted with clinical data (n = 120) collected through self-reported questionnaires, clinical periodontal parameters, and radiographic image analysis. Predictive performance was assessed for logistics regression, support vector machine, random forest, XGBoost, and neural network algorithms using the area under the receiver operating characteristic curves (AUC) and other performance metrics.

Results

Correlation analysis identified 16 features from over 1000 potential risk factors for periodontitis. The best data-driven model (XGBoost) showed AUC values of 0.823 and 0.796 for internal and external validations, respectively. Modeling with clinical data revealed those same measures to be 0.836 and 0.649, respectively. In addition, the data-driven model could predict other clinical periodontal parameters including severe bone loss (AUC = 0.813), gingival bleeding (AUC = 0.694), and tooth loss (AUC = 0.734). A patient case study about prognostic predictions revealed that the probability of periodontitis can be reduced by 6.0 % (stop smoking) and 0.6 % (stop drinking) on average.

Conclusions

Data-driven models for predicting periodontitis and other periodontal parameters were developed from 16 risk factors, demonstrating enhanced prediction performance and reproducibility in internal-external validations.

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis

This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review

INTRODUCTION

Periodontitis is the main cause of tooth loss and is related to many systemic diseases. Artificial intelligence (AI) in periodontics has the potential to improve the accuracy of risk assessment and provide personalized treatment planning for patients with periodontitis. This systematic review aims to examine the actual evidence on the accuracy of various AI models in predicting periodontitis.

METHODS

Using a mix of MeSH keywords and free text words pooled by Boolean operators ('AND', 'OR'), a search strategy without a time frame setting was conducted on the following databases: Web of Science, ProQuest, PubMed, Scopus, and IEEE Explore. The QUADAS-2 risk of bias assessment was then performed.

RESULTS

From a total of 961 identified records screened, 8 articles were included for qualitative analysis: 4 studies showed an overall low risk of bias, 2 studies an unclear risk, and the remaining 2 studies a high risk. The most employed algorithms for periodontitis prediction were artificial neural networks, followed by support vector machines, decision trees, logistic regression, and random forest. The models showed good predictive performance for periodontitis according to different evaluation metrics, but the presented methods were heterogeneous.

CONCLUSIONS

AI algorithms may improve in the future the accuracy and reliability of periodontitis prediction. However, to date, most of the studies had a retrospective design and did not consider the most modern deep learning networks. Although the available evidence is limited by a lack of standardized data collection and protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.

KNOWLEDGE TRANSFER STATEMENT

The use of AI in periodontics can lead to more accurate diagnosis and treatment planning, as well as improved patient education and engagement. Despite the current challenges and limitations of the available evidence, particularly the lack of standardized data collection and analysis protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.

Association between probiotic consumption and periodontitis: Evidence from NHANES 2009-2014

AIM

The study aimed to provide evidence of the relationship between probiotics consumption and periodontitis.

MATERIALS AND METHODS

A total of 4577 adults who participated in the National Health and Nutrition Examination Survey (NHANES) during 2009-2014 were included in the study. The weighted prevalence of periodontitis was compared among different groups, and a weighted binary logistic regression analysis was conducted to explore the relationship between probiotic consumption and periodontitis. Receiver operating characteristic (ROC) curves were used to assess the role of probiotic consumption in the periodontitis prediction model.

RESULTS

Participants who consumed probiotics had a significantly lower prevalence of periodontitis than those who did not (41.08% vs. 27.83%, p < .001). After fully adjusting for all factors, the odds ratio associated with periodontitis for consuming probiotics was 0.70 (95% confidence interval 0.54-0.92, p = .01) when compared with those who did not consume probiotics. A predictive model including age, sex, ethnicity, poverty income ratio, smoking status and probiotics had 77.0% sensitivity and 60.3% specificity in detecting periodontitis in US adults and achieved an area under the ROC curve of 0.749.

CONCLUSIONS

These results indicate that consuming probiotics is associated with a reduced risk of periodontitis.

Multivariable prediction models of caries increment: a systematic review and critical appraisal

BACKGROUND

Multivariable prediction models are used in oral health care to identify individuals with an increased likelihood of caries increment. The outcomes of the models should help to manage individualized interventions and to determine the periodicity of service. The objective was to review and critically appraise studies of multivariable prediction models of caries increment.

METHODS

Longitudinal studies that developed or validated prediction models of caries and expressed caries increment as a function of at least three predictors were included. PubMed, Cochrane Library, and Web of Science supplemented with reference lists of included studies were searched. Two reviewers independently extracted data using CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) and assessed risk of bias and concern regarding applicability using PROBAST (Prediction model Risk Of Bias ASessment Tool). Predictors were analysed and model performance was recalculated as estimated positive (LR +) and negative likelihood ratios (LR -) based on sensitivity and specificity presented in the studies included.

RESULTS

Among the 765 reports identified, 21 studies providing 66 prediction models fulfilled the inclusion criteria. Over 150 candidate predictors were considered, and 31 predictors remained in studies of final developmental models: caries experience, mutans streptococci in saliva, fluoride supplements, and visible dental plaque being the most common predictors. Predictive performances varied, providing LR + and LR - ranges of 0.78-10.3 and 0.0-1.1, respectively. Only four models of coronal caries and one root caries model scored LR + values of at least 5. All studies were assessed as having high risk of bias, generally due to insufficient number of outcomes in relation to candidate predictors and considerable uncertainty regarding predictor thresholds and measurements. Concern regarding applicability was low overall.

CONCLUSIONS

The review calls attention to several methodological deficiencies and the significant heterogeneity observed across the studies ruled out meta-analyses. Flawed or distorted study estimates lead to uncertainty about the prediction, which limits the models' usefulness in clinical decision-making. The modest performance of most models implies that alternative predictors should be considered, such as bacteria with acid tolerant properties.

TRIAL REGISTRATION

PROSPERO CRD#152,467 April 28, 2020.

Are dental visiting patterns and oral pain associated with dental disease among Norwegian adults? A cross-sectional study based on the Tromsø study

OBJECTIVES

The present study aims to describe the dental visiting patterns in a Norwegian adult population and their associations with sociodemographic and oral health variables, including oral pain. We further explore if the utilization of dental health services and oral pain predicts caries and periodontitis, the most common oral diseases.

MATERIALS AND METHODS

We use data from the seventh wave of the Tromsø study performed in 2015-2016. In this cross-sectional survey, all residents 40 years or older in Tromsø municipality in Norway were invited, of whom 21,083 (65%) participated. All participants answered questionnaires assessing sociodemographic characteristics, use of health services, and self-reported health measures, including pain. Almost 4000 participants underwent a dental examination with registration of caries and periodontitis. Associations of dental visiting patterns and utilization of dental services the past 12 months with sociodemographic-, self-reported-, and clinical oral health measures were analyzed by cross-tabulation and Pearson's χ2 tests, as well as with logistic regression analyses with caries and periodontitis as outcomes.

RESULTS

A regular, annual dental visiting pattern was the most common, but among respondents with severe dental anxiety and poor dental health, visiting for acute problems only or never (symptomatic visiting) was the most common. Intervals of more than 24 months between visits and a symptomatic visiting pattern were associated with caries, whereas shorter than 12-month intervals and a symptomatic visiting pattern were associated with periodontitis. Many characteristics were shared among respondents with the lowest and the highest utilization of dental services, including oral pain, a difficult financial situation and poorer self-reported and clinical dental health.

CONCLUSIONS

Regular dental visits at 12-24 month intervals were associated with beneficial oral health parameters, compared with more frequent, rarer, and symptomatic dental visiting patterns. Oral pain was an unreliable predictor of caries and periodontitis.

Periodontal Status and Potential Risk Factors in the Myanmar Population: Data from the First National Oral Health Survey in 2016-2017

ABSTRACT

Periodontal disease is quite common in many developing countries, but no nationally representative data on periodontal disease is available in Myanmar until 2016. A nationwide cross-sectional survey with a stratified two-stage sampling method was conducted among 4668 participants aged 12-74 years. Periodontal assessments and self-administered questionnaire surveys were conducted to assess the periodontal status and risk factors in the Myanmar population using data from the first national oral health survey in 2016-2017. Descriptive statistics and logistic regression analysis were performed to assess the prevalence of periodontitis and its association with potential risk factors. The prevalence of gingivitis was 38.2%, 41.5%, 44.4%, and 43.7% in 12, 15-18, 35-44, and 60-74 year-olds, respectively. The prevalence of periodontitis in the latter three age groups was 2.3%, 16.3%, and 35.2%, respectively. After adjusting for other risk factors, younger participants aged 15-18 years (odds ratio [OR]: 0.1, 95% confidence interval [95% CI]: 0.0-0.2) and aged 35-44 years (OR: 0.4, 95% CI: 0.3-0.5) were less likely to have periodontitis. Moreover, participants educated upto junior high school level (OR: 1.6, 95% CI: 1.1-2.7), primary school and below level (OR: 1.9, 95% CI: 1.2-3.2), everyday smokers (OR: 1.6, 95% CI: 1.3-2.0), and everyday tobacco chewers (OR: 1.5, 95% CI: 1.2-1.8) were associated with increased risk of periodontitis. Periodontal complications were prevalent in Myanmar older adults. Prevention efforts should focus on tobacco control and oral hygiene.

Optimizing a Diagnostic Model of Periodontitis by Using Targeted Proteomics

Periodontitis (PD), a widespread chronic infectious disease, compromises oral health and is associated with various systemic conditions and hematological alterations. Yet, to date, it is not clear whether serum protein profiling improves the assessment of PD. We collected general health data, performed dental examinations, and generated serum protein profiles using novel Proximity Extension Assay technology for 654 participants of the Bialystok PLUS study. To evaluate the incremental benefit of proteomics, we constructed two logistic regression models assessing the risk of having PD according to the CDC/AAP definition; the first one contained established PD predictors, and in addition, the second one was enhanced by extensive protein information. We then compared both models in terms of overall fit, discrimination, and calibration. For internal model validation, we performed bootstrap resampling (n = 2000). We identified 14 proteins, which improved the global fit and discrimination of a model of established PD risk factors, while maintaining reasonable calibration (area under the curve 0.82 vs 0.86; P < 0.001). Our results suggest that proteomic technologies offer an interesting advancement in the goal of finding easy-to-use and scalable diagnostic applications for PD that do not require direct examination of the periodontium.

Development and international validation of logistic regression and machine-learning models for the prediction of 10-year molar loss

AIM

To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients.

MATERIALS AND METHODS

Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine-learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K-nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment.

RESULTS

The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis-related molar loss. In addition, the ensembled model showed the best calibration performance.

CONCLUSIONS

Through a multi-centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice.

Systematic comparison of machine learning algorithms to develop and validate predictive models for periodontitis

AIM

The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis.

MATERIALS AND METHODS

Using national survey data from Taiwan (n = 3453) and the United States (n = 3685), predictors of periodontitis were extracted from the datasets and pre-processed, and then 10 machine learning algorithms were trained to develop predictive models. The models were validated both internally (bootstrap sampling) and externally (alternative country's dataset). The algorithms were compared across six performance metrics ([i] area under the curve for the receiver operating characteristic [AUC], [ii] accuracy, [iii] sensitivity, [iv] specificity, [v] positive predictive value, and [vi] negative predictive value) and two methods of data pre-processing ([i] machine-learning-based feature selection and [ii] dimensionality reduction into principal components).

RESULTS

Many algorithms showed extremely strong performance during internal validation (AUC > 0.95, accuracy > 95%). However, this was not replicated in external validation, where predictive performance of all algorithms dropped off drastically. Furthermore, predictive performance differed according to data pre-processing methodology and the cohort on which they were trained.

CONCLUSIONS

Larger sample sizes and more complex predictors of periodontitis are required before machine learning can be leveraged to its full potential.

Evaluation of periodontitis-related tooth loss according to the new 2018 classification of periodontitis

The new 2018 classification of periodontal diseases is reported to be related to tooth loss due to periodontal disease (TLPD) during supportive periodontal therapy (SPT). However, few reports have evaluated this relationship for Asians or have analyzed the association of the new classification and TLPD by distinguishing between active periodontal therapy (APT) and SPT. In this study, we retrospectively applied the new classification to 607 Japanese periodontitis patients and examined the relationship between the new classification and annual TLPD rates per patient during the respective periods. TLPD rates were higher in patients in stage IV and/or grade C during both APT and SPT. TLPD during SPT was not associated with the presence or absence of TLPD during APT. Multivariate analysis revealed that stage IV and grade C as independent variables were significantly associated with the number of instances of TLPD not only during the total treatment period, but also during APT or SPT. Our results suggest that the new classification has a significantly strong association with TLPD during both APT and SPT, and that patients diagnosed with stage IV and/or grade C periodontitis had a higher risk of TLPD during both periods.

Multifunctional chitin-based barrier membrane with antibacterial and osteogenic activities for the treatment of periodontal disease

The guided tissue regeneration technique is an effective approach to repair periodontal defect. However, collagen barrier membranes used clinically lose stability easily, leading to soft tissue invasion, surgical site infection, and failure of osteogenesis. An ideal barrier membrane should possess proper antibacterial, osteoconductive activities, and favorable biodegradation. In this study, zinc oxide nanoparticles were homogeneously incorporated into the chitin hydrogel (ChT-1%ZnO) through one-step dissolution and regeneration method from alkaline/urea solution the first time. The remaining weights of ChT-1%ZnO in 150 μg/mL lysozyme solution was 52% after 5 weeks soaking. ChT-1%ZnO showed statistical antibacterial activities for P. gingivalis and S. aureus at 6 h, 12 h, and 24 h. Moreover, ChT-1%ZnO exhibits osteogenesis promotion in vitro, and it was further evaluated with rat periodontal defect model in vivo. The cemento-enamel junction value in ChT-1%ZnO group is 1.608 mm, presenting a statistical difference compared with no-membrane (1.825 mm) and ChT group (1.685 mm) after 8 weeks postoperatively.

Assessing the completeness of periodontal disease documentation in the EHR: a first step in measuring the quality of care

Background

Our objective was to measure the proportion of patients for which comprehensive periodontal charting, periodontal disease risk factors (diabetes status, tobacco use, and oral home care compliance), and periodontal diagnoses were documented in the electronic health record (EHR). We developed an EHR-based quality measure to assess how well four dental institutions documented periodontal disease-related information. An automated database script was developed and implemented in the EHR at each institution. The measure was validated by comparing the findings from the measure with a manual review of charts.

Results

The overall measure scores varied significantly across the four institutions (institution 1 = 20.47%, institution 2 = 0.97%, institution 3 = 22.27% institution 4 = 99.49%, p-value < 0.0001). The largest gaps in documentation were related to periodontal diagnoses and capturing oral homecare compliance. A random sample of 1224 charts were manually reviewed and showed excellent validity when compared with the data generated from the EHR-based measure (Sensitivity, Specificity, PPV, and NPV > 80%).

Conclusion

Our results demonstrate the feasibility of developing automated data extraction scripts using structured data from EHRs, and successfully implementing these to identify and measure the periodontal documentation completeness within and across different dental institutions.

Have you had bleeding from your gums? Self-report to identify giNGival inflammation (The SING diagnostic accuracy and diagnostic model development study)

AIM

To assess the diagnostic performance of self-reported oral health questions and develop a diagnostic model with additional risk factors to predict clinical gingival inflammation in systemically healthy adults in the United Kingdom.

METHODS

Gingival inflammation was measured by trained staff and defined as bleeding on probing (present if bleeding sites ≥ 30%). Sensitivity and specificity of self-reported questions were calculated; a diagnostic model to predict gingival inflammation was developed and its performance (calibration and discrimination) assessed.

RESULTS

We included 2853 participants. Self-reported questions about bleeding gums had the best performance: the highest sensitivity was 0.73 (95% CI 0.70, 0.75) for a Likert item and the highest specificity 0.89 (95% CI 0.87, 0.90) for a binary question. The final diagnostic model included self-reported bleeding, oral health behaviour, smoking status, previous scale and polish received. Its area under the curve was 0.65 (95% CI 0.63-0.67).

CONCLUSION

This is the largest assessment of diagnostic performance of self-reported oral health questions and the first diagnostic model developed to diagnose gingival inflammation. A self-reported bleeding question or our model could be used to rule in gingival inflammation since they showed good sensitivity, but are limited in identifying healthy individuals and should be externally validated.

Are there correlations between radiographic grade levels and modified grade levels of periodontitis?

BACKGROUND

At the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions, periodontitis was reclassified according to a multidimensional staging and grading system. Grading takes variabilities in the rate of disease progression into account, relying on recognized progression risk factors, and is based on the assessment of bone loss at the worst-affected tooth in the dentition as a function of age, which is then modified by factors such as smoking and diabetes mellitus. In this context, the aim of this study was to determine whether there are correlations between radiographically calculated grades and grades modified by the presence of smoking or diabetes.

MATERIALS AND METHODS

In this descriptive study, individuals diagnosed with periodontitis according to the 2017 classifications were examined. The grade of periodontitis was measured using periapical radiography. A modified-grade level was assigned to the patients according to their glycated hemoglobin levels (in diabetics) or their smoking status.

RESULTS

The study included 341 individuals. No statistically significant relationship was revealed by Chi-square testing (P > 0.05) or in the kappa agreement index between the modified grades and the radiographic grades.

CONCLUSIONS

Although there was no correlation between radiographic grade and modified grade in smokers and diabetic individuals, increasing the grade score in these individuals in accordance with the 2017 classification provides clinicians the opportunity to develop a risk-based treatment plan, commensurate with the severity of periodontal disease and the level of risk.

There is a paucity of economic evaluations of prediction methods of caries and periodontitis-A systematic review

Objectives

Direct cost for methods of prediction also named risk assessment in dentistry may be negligible compared with the cost of extensive constructions. On the other hand, as risk assessment is performed daily and for several patients in general dental practice, the costs may be considerable. The objective was to summarize evidence in studies of economic evaluation of prognostic prediction multivariable models and methods of caries and periodontitis and to identify knowledge gaps (PROSPERO registration number: CRD42020149763).

Material and methods

Four electronic databases (PubMed, Web of Science, The Cochrane Library, NHS Economic Evaluation Database) and reference lists of included studies were searched. Titles and abstracts were screened by two reviewers in parallel. Full‐text studies reporting resources used, costs and cost‐effectiveness of prediction models and methods were selected and critically appraised using a protocol based on items from the CHEERS checklist for economic evaluations and the CHARMS checklist for evaluation of prediction studies.

Results

From 38 selected studies, six studies on prediction fulfilled the eligibility criteria, four on caries and two on periodontitis. As the economic evaluations differed in method and perspective among the studies, the results could not be generalized. Our systematic review revealed methodological shortcomings regarding the description of predictive models and methods, and particularly of the economic evaluation.

Conclusions

The systematic review highlighted a paucity of economic evaluations regarding methods or multivariable models for prediction of caries and periodontitis. Our results indicate that what we currently practice using models and methods to predict caries and periodontitis lacks evidence regarding cost‐effectiveness.

Individual predisposition and the intricate interplay between systemic biomarkers and periodontal risk in a general population

BACKGROUND

Increasing age is associated with systemic diseases as well as with periodontal diseases. We wondered whether a biological age score constructed exclusively from systemic biomarkers would reflect periodontal risk factors at baseline and tooth loss as well as periodontal outcome during 10 years follow-up.

METHODS

From the Study of Health in Pomerania (SHIP) 2256 participants (1072 male, 1184 female) were studied for the relationship of the systemic biomarkers glycated hemoglobin (HbA1c), low density lipoprotein cholesterol (LDL), fibrinogen, white blood cell count, blood pressure, and waist circumference to their age. Construction of a biological age (BA) score allowed its comparison with the participants' actual chronological age (CA) and their predisposition to periodontal disease.

RESULTS

Though nearly identical in CA, participants appearing younger than their true age had a significantly reduced burden of periodontal risk factors. If BA > CA, then risk factors were more frequent including smoking, oral hygiene, dental visits, education, and income. After 10 years, in participants with identical CA, tooth loss followed their BA calculated at baseline, that is, with BA > CA fewer teeth were preserved. Similarly, periodontal measures varied according to BA; sex differences were obvious. Most significant were BA-related differences in inflammatory and anthropometry parameters.

CONCLUSIONS

The results support the assumption that risk profiles aggregated in BA constitute a characteristic susceptibility pattern unique to each individual, common to both systemic and periodontal diseases. Although BA was constructed exclusively from systemic measures at baseline, BA reflects the oral conditions at follow-up.

The prevalence and severity of periodontal disease in Mainland China: Data from the Fourth National Oral Health Survey (2015-2016)

AIM

To evaluate periodontal conditions in adults in mainland China based on data from the 4th National Oral Health Survey.

MATERIALS AND METHODS

Data of adult subjects (35- to 44-year-old group [N = 4409], 55- to 64-year-old group [N = 4622], and 65- to 74-year-old group [N = 4428]) were analysed. Demographic, socio-economic, personal habit, dental history, and health attitude data were obtained using a questionnaire. Periodontal condition was assessed using the standardized case definitions of the 2018 classification scheme. A multivariate analysis was performed to investigate the relationship between periodontitis severity and age, smoking status, gender, and region using generalized additive models after adjusting for confounders.

RESULTS

The frequency of subjects with periodontitis was 52.8%, 69.3%, and 64.6% in the three age groups, respectively. The frequency of subjects with severe periodontitis (stage III or IV) was 10.6%, 37.3%, and 43.5% in the three age groups, respectively. The severity of periodontal disease was positively associated with age. Current and former smokers exhibited significantly greater disease severity than non-smokers after adjustment for confounders. No significant difference of severity was found between males and females.

CONCLUSION

Within the limitations of the study, the prevalence of periodontitis is very high among adults in mainland China. Periodontal status is associated with age and smoking status.

Proteome Analysis of Molecular Events in Oral Pathogenesis and Virus: A Review with a Particular Focus on Periodontitis

Some systemic diseases are unquestionably related to periodontal health, as periodontal disease can be an extension or manifestation of the primary disease process. One example is spontaneous gingival bleeding, resulting from anticoagulant treatment for cardiac diseases. One important aspect of periodontal therapy is the care of patients with poorly controlled disease who require surgery, such as patients with uncontrolled diabetes. We reviewed research on biomarkers and molecular events for various diseases, as well as candidate markers of periodontal disease. Content of this review: (1) Introduction, (2) Periodontal disease, (3) Bacterial and viral pathogens associated with periodontal disease, (4) Stem cells in periodontal tissue, (5) Clinical applications of mass spectrometry using MALDI-TOF-MS and LC-MS/MS-based proteomic analyses, (6) Proteome analysis of molecular events in oral pathogenesis of virus in GCF, saliva, and other oral Components in periodontal disease, (7) Outlook for the future and (8) Conclusions. This review discusses proteome analysis of molecular events in the pathogenesis of oral diseases and viruses, and has a particular focus on periodontitis.

Examining Bias and Reporting in Oral Health Prediction Modeling Studies

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

Age-Specific Predictive Models of the Upper Quintile of Periodontal Attachment Loss

A practical method to identify people who are most affected by periodontitis in their age group is currently unavailable. We focused on individuals with mean clinical attachment loss (CAL) above the 80th percentile within each of 10 age groups (5-y intervals between 30 and 74 y as well as ≥75 y). We developed predictive models using combined data from 2 cohorts (2009 to 2010 and 2011 to 2012) from the NHANES (National Health and Nutrition Examination Survey; development cohort [DC], n = 6,757), and we carried out external validation using data from a third NHANES cohort (2013 to 2014; validation cohort [VC], n = 3,447). We used 1) age-specific logistic regression models with stepwise selection to identify significant demographic variables, habits, medical conditions, and selected clinical periodontal parameters (proportion of teeth with probing depth ≥4 mm at incisors and molars and with visible [≥2 mm] recession) and to calculate propensity scores (PSs); 2) Youden's J statistic to select optimum PS cutoffs to maximize diagnostic performance using receiver operating characteristic curves; and 3) bootstrap resampling with 1,000 replicates to validate the age-specific models and adjust the PS and optimal PS cutoffs for overfitting. The bootstrap-adjusted PSs were used as single predictors of mean CAL over the 80th percentile in the VC. The age-specific upper quintiles of mean CAL ranged between 1.63 and 3.24 mm in the DC and between 1.87 and 3.20 mm in the VC. The area under the curve of the models exceeded 0.85 in all age groups in the DC and 0.84 in the VC, indicating well-validated diagnostic performance. In the DC, sensitivity values ranged between 0.75 and 0.97 and exceeded 0.83 in 8 of 10 age groups. Corresponding values in the VC ranged between 0.56 and 0.89 and exceeded 0.68 in 8 of 10 age groups. We conclude that modeling that incorporates readily obtainable variables through a brief patient interview and an abbreviated periodontal examination accurately identifies individuals who are most affected by periodontitis in different ages.

Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry

This comprehensive review of the 2018 dental literature is provided to inform busy dentists about progress in the profession. Developed by the Committee on Scientific Investigation of the American Academy of Restorative Dentistry, each author brings discipline-specific expertise to one of the 8 sections of the report including (1) prosthodontics; (2) periodontics, alveolar bone, and peri-implant tissues; (3) implant dentistry; (4) dental materials and therapeutics; (5) occlusion and temporomandibular disorders; (6) sleep-related breathing disorders; (7) oral medicine and oral and maxillofacial surgery; and (8) dental caries and cariology. The report targets important information that will likely influence day-to-day treatment decisions. Each review is not intended to stand alone but to update interested readers so that they may visit source materials if greater detail is desired. As the profession continues its march toward evidence-based clinical decision-making, an already voluminous library of potentially valuable dental literature continues to grow. It is the intention of this review and its authors to provide assistance in navigating the extensive dental literature published in 2018. It is our hope that readers find this work useful in the clinical management of patients moving forward.

Evaluating Modeling and Validation Strategies for Tooth Loss

Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients' age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.

Predictors for tooth loss in periodontitis patients: Systematic review and meta-analysis

AIM

A range of predictors for tooth loss in periodontitis patients have been reported. We performed a systematic review and meta-analysis to assess the consistency and magnitude of any association between a total of 12 predictors and tooth loss.

MATERIALS AND METHODS

Medline/Embase/Central were searched for longitudinal studies investigating the association between predictors and tooth loss in periodontitis patients. Random-effects meta-analysis was performed, and study quality assessed.

RESULTS

Twenty studies (15,422 patients, mean follow-up: 12 years) were included. The mean annual tooth loss/patient was 0.12 (min./max: 0.01/0.36). Older patients (n = 8 studies; OR: 1.05, 95% CI: 1.03-1.08/year), non-compliant ones (n = 11; 1.51, 1.06-2.16), diabetics (n = 7; 1.80, 1.26-2.57), those with IL-1-polymorphism (n = 3; 1.80; 1.29-2.52) and smokers (n = 15; 1.98, 1.58-2.48) had a significantly higher risk of tooth loss. Teeth with bone loss (n = 3; 1.04, 1.03-1.05/%), high probing pocket depth (n = 6; 3.19, 1.70-5.98), mobility (n = 4; 3.71, 1.65-8.38) and molars (n = 4; 4.22, 2.12-8.39), especially with furcation involvement (n = 5; 2.68, 1.75-4.08) also showed higher risks. Gender (n = 16; 0.95, 0.86-1.05) and endodontic affection (n = 3; 3.62, 0.99-13.2) were not significantly associated with tooth loss.

CONCLUSIONS

Older, non-compliant, smoking or diabetic patients, and teeth with bone loss, high probing pocket depth, mobility, or molars, especially with furcation involvement showed higher risks of tooth loss.