Prediction of Periodontitis Occurrence: Influence of Classification and Sociodemographic and General Health Information

Chronic oral disease burden at the first 1000 days: Intergenerational risk factors, BRISA cohort

OBJECTIVE

To analyze multiple-causal models, including socioeconomic, obesity, sugar consumption, alcohol smoking, caries, and periodontitis variables in pregnant women with early sugar exposure, obesity, and the Chronic Oral Disease Burden in their offspring around the first 1000 days of life.

METHODS

The BRISA cohort study, Brazil, had two assessments: at the 22nd-25th gestational weeks and during the child's second year (n = 1141). We proposed a theoretical model exploring the association between socioeconomic and pregnancy factors (age, smoking, alcohol, sugars, obesity, periodontitis, and caries) and child's variables (sugars and overweight) with the outcome, Chronic Oral Disease Burden (latent variable deduced from visible plaque, gingivitis, and tooth decay), using structural equation modeling.

RESULTS

Caries and periodontitis were correlated in pregnant women. Addictive behaviors in the gestational period were correlated. Obesity (Standardized coefficient - SC = 0.081; p = 0.047) and added sugar consumption (SC = 0.142; p = 0.041) were observed intergenerationally in the pregnant woman-child dyads. Sugar consumption by the children (SC = 0.210; p = 0.041) increased the Chronic Oral Disease Burden.

CONCLUSIONS

Poor caries and periodontal indicators were correlated in pregnant women and their offspring. Obesity and sugar consumption act intergenerationally. Oral health in early life may change life trajectory since the worst oral conditions predict main NCDs.

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).

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.

Development of a nomogram for identifying periodontitis cases in Denmark

Although self-reported health outcomes are of importance, attempts to validate a clinical applicable instrument (e.g., nomogram) combining sociodemographic and self-reported information on periodontitis have yet to be performed to identify periodontitis cases. Clinical and self-reported periodontitis, along with sociodemographic data, were collected from 197 adults. Akaike information criterion models were developed to identify periodontitis, and nomograms developed based on its regression coefficients. The discriminatory capability was evaluated by receiver-operating characteristic curves. Decision curve analysis was performed. Smoking [OR 3.69 (95%CI 1.89, 7.21)], poor/fair self-rated oral health [OR 6.62 (95%CI 3.23, 13.56)], previous periodontal treatment [OR 9.47 (95%CI 4.02, 22.25)], and tooth loss [OR 4.96 (95%CI 2.47, 9.97)], determined higher probability of having "Moderate/Severe Periodontitis". Age [OR 1.08 (95%CI 1.05, 1.12)], low educational level [OR 1.65 (95%CI 1.34, 2.23)], poor/fair self-rated oral health [OR 3.57 (95%CI 1.82, 6.99)], and previous periodontal treatment [OR 6.66 (95%CI 2.83, 15.68)] determined higher probability for "Any Periodontitis". Both nomograms showed excellent discriminatory capability (AUC of 0.83 (95%CI 0.75, 0.91) and 0.81 (95% CI 0.74, 0.88), good calibration, and slight overestimation of high risk and underestimation of low risk. Hence, our nomograms could help identify periodontitis among adults in Denmark.

Factors associated with periodontal diseases in pregnancy: Findings of the 2015 Pelotas Birth Cohort Study

Although periodontal disease is common during pregnancy, little is known about socioeconomic, behavioral, or biological determinants related to clinically assessed periodontal condition during this period. We assessed the prevalence of periodontal disease and associated factors in pregnant women. This population-based survey used data used from the 2015 Pelotas Birth Cohort Study, Brazil. Pregnant women expected to give birth between December 2014 and May 2016 were interviewed and clinically examined by trained dentist, with periodontal measures collected in all teeth, six sites per tooth. Outcomes were periodontitis (using the 2012 Centers for Disease Control and Prevention and the American Academy of Periodontology criteria) and gingivitis (by the 2018 European Federation of Periodontology/ American Academy of Periodontology classification). Multivariate hierarchical Poisson regression was used to assess the associations between socioeconomic, systemic, and clinical oral factors and periodontal disease. A total of 2,474 pregnant women participated in the study. Prevalence of periodontitis and gingivitis was 14.63% and 21.67%, respectively. Lower educational level and calculus were associated with higher prevalence periodontitis and gingivitis (P<0.05). Smoking was also associated with periodontitis (P=0.05), and lower frequency of toothbrushing (P=0.005) with gingivitis. Periodontal disease, especially gingivitis, was prevalent in pregnant women and their determinants were socioeconomic, environmental, and clinical oral health factors.

Obesity, Insulin Resistance, Caries, and Periodontitis: Syndemic Framework

(1) Background: To investigate the grouping of obesity and insulin resistance with caries and periodontitis from a syndemic perspective through pathways of socioeconomic inequalities, smoking, alcohol, and high sugar consumption in adolescence. (2) Methods: The population-based RPS Cohort study, São Luís, Brazil, in ages 18-19 years (n = 2515) was used. The outcomes were the grouping of pbesity and Insulin Resistance Phenotype (latent variable formed by Triglycerides/HDL ratio, TyG index, and VLDL) and the Chronic Oral Disease Burden (latent variable comprising caries, bleeding on probing, probing depth ≥ 4 mm, clinical attachment level ≥ 3 mm, and visible plaque index ≥ 15%). Socioeconomic Inequalities influencing the Behavioral Risk Factors (latent variable formed by added sugar, smoking, and alcohol) were analyzed using structural equation modeling. (3) Results: Socioeconomic Inequalities were associated with the Chronic Oral Disease Burden [Standardized Coefficient (SC) = 0.222, p < 0.001]. Behavioral Risk Factors were associated with increased Chronic Oral Disease Burden (SC = 0.103; p = 0.013). Obesity was associated with the Insulin Resistance Phenotype (SC = 0.072; p < 0.001) and the Chronic Oral Disease Burden (SC = 0.066; p = 0.005). The Insulin Resistance Phenotype and the Chronic Oral Disease Burden were associated (SC = 0.053; p = 0.032). (4) Conclusion: The grouping of obesity and early events of diabetes with caries and periodontitis call for a syndemic approach in adolescence.

Salivary cytokine expression after non-surgical periodontal therapy in smokers: 12-month follow-up

BACKGROUND

Diverse smoking trajectories may influence cytokine expression after non-surgical periodontal therapy and supportive periodontal care. Thus, we aimed to describe cytokine profiles in periodontal healing after periodontal therapy in smokers.

METHODS

A smoking cessation program and non-surgical periodontal therapy were offered to 80 smokers with periodontitis. Smoking trajectories (quitters/light, moderate, heavy) were observed. The association of salivary interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, interferon-gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α) with smoking trajectories and periodontal outcomes was determined using mixed-effects linear regression.

RESULTS

Among quitters/light smokers, IL-1β was associated with an increase in mean periodontal pocket depth (PPD) and mean clinical attachment level (CAL). IL-6 was associated with a decrease in mean PPD and CAL in heavy smokers, whereas IL-8 was associated with a decrease in PPD among moderate smokers. TNFα was associated with a reduction in mean PPD and CAL among quitters/light smokers, while among moderate smokers, TNFα was associated with an increase in mean PPD and CAL. IL-12 and IL-13 were associated with a decrease in mean PPD in moderate smokers.

CONCLUSION

Our findings suggest that distinctive smoking exposures induce differential cytokine expression, which, in turn, seems to influence periodontal repair.

The Association Between Periodontal Disease and Chronic Migraine: A Systematic Review

Migraine is a neurologic illness that produces intense throbbing pain on one side of the head and affects roughly 1 billion people worldwide. Recent research indicates a relationship between periodontitis and chronic migraines. This study aimed to review the association between chronic migraines and periodontitis through a systematic literature review. Four research databases (Google Scholar, PubMed, ProQuest, and SpringerLink) were searched according to PRISMA guidelines to retrieve the studies included in this review. A search strategy was developed to answer the study question with appropriate inclusion and exclusion criteria. Out of 34 published studies, 8 studies were included in this review. Three of the studies were cross-sectional, 3 were case-control, and 2 were clinical report and medical hypothesis papers. Seven of the 8 included studies showed that there is an association between periodontal disease and chronic migraine. The elevated blood levels of some biomarkers such as leptins, ProCalcitonin (proCT), calcitonin gene-related peptides (CGRPs), Pentraxin 3 (PTX3), and Soluble Tumor Necrosis Factor-like Weak Inducer Of Apoptosis (sTWEAK) play a significant role in this association. The limitations include a small sample size, the influence of anti-inflammatory drugs, and a self-reported headache measure that is subject to misclassification bias. This systematic review reveals a supposed correlation between periodontal disease and chronic migraine, as evidenced by various biomarkers and inflammatory mediators. This suggests that periodontal disease could potentially contribute to the development of chronic migraine. However, to further assess the potential benefits of periodontal treatment in patients with chronic migraine, additional longitudinal studies with larger sample sizes and interventional studies are needed.

Chronic oral diseases burden: The confluence of caries and periodontitis throughout life

AIM

To investigate the confluence of caries and periodontitis indicators from adolescence to elderhood among Americans.

MATERIALS AND METHODS

This cross-sectional study explored the grouping among a set of caries and periodontitis indicators (the proportion of sites with bleeding on probing, moderate probing pocket depth [PPD, 4-5 mm], severe PPD [≥6 mm], moderate clinical attachment level [CAL, 3-4 mm], severe CAL [≥5 mm], number of teeth with furcation involvement, number of decayed teeth, number of teeth with pulp involvement, and the number of missing teeth) in 14,421 Americans from the NHANES III study. Exploratory factorial analysis was used to determine the constructs between those indicators (factorial loading ≥0.3). These analyses were stratified by age and confirmed with a confirmatory factorial analysis. We also performed a sensitivity analysis using the NHANES 2011-2014.

RESULTS

Two constructs were extracted. The first, Chronic Oral Diseases Burden, grouped caries indicators with moderate PPD and moderate CAL for the youngest subjects (13-39 years old), while for the subjects over 50 years, the Chronic Oral Disease Burden grouped caries indicators with severe CAL and PPD and furcation involvement. The second construct, Periodontal Destruction, grouped only periodontitis indicators.

CONCLUSIONS

Caries and periodontitis indicators grouped consistently across the different age ranges in lapse times of 25 years.

Identification and validation of a classifier based on hub aging-related genes and aging subtypes correlation with immune microenvironment for periodontitis

BACKGROUND

Periodontitis (PD), an age-related disease, is characterized by inflammatory periodontal tissue loss, and with the general aging of the global population, the burden of PD is becoming a major health concern. Nevertheless, the mechanism underlying this phenomenon remains indistinct. We aimed to develop a classification model for PD and explore the relationship between aging subtypes and the immune microenvironment for PD based on bioinformatics analysis.

MATERIALS AND METHODS

The PD-related datasets were acquired from the Gene Expression Omnibus (GEO) database, and aging-related genes (ARGs) were obtained from the Human Aging Genomic Resources (HAGR). Four machine learning algorithms were applied to screen out the hub ARGs. Then, an artificial neural network (ANN) model was constructed and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Consensus clustering was employed to determine the aging expression subtypes. A series of bioinformatics analyses were performed to explore the PD immune microenvironment and its subtypes. The hub aging-related modules were defined using weighted correlation network analysis (WGCNA).

RESULTS

Twenty-seven differentially expressed ARGs were dysregulated and a classifier based on four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) was constructed to diagnose PD with excellent accuracy. Subsequently, the mRNA levels of the hub ARGs were validated by quantitative real-time PCR (qRT-PCR). Based on differentially expressed ARGs, two aging-related subtypes were identified. Distinct biological functions and immune characteristics including infiltrating immunocytes, immunological reaction gene sets, the human leukocyte antigen (HLA) gene, and immune checkpoints were revealed between the subtypes. Additionally, the black module correlated with subtype-1 was manifested as the hub aging-related module and its latent functions were identified.

CONCLUSION

Our findings highlight the critical implications of aging-related genes in modulating the immune microenvironment. Four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) formed a classification model, and accompanied findings revealed the essential role of aging in the immune microenvironment for PD, providing fresh inspiration for PD etiopathogenesis and potential immunotherapy.

Periodontal conditions in a 65-year-old population and prevalence of periodontitis according to three different bone level thresholds

AIMS

The aims of this cross-sectional study were to describe the prevalence and severity of periodontal disease in a 65-year-old population in Oslo, Norway, and to investigate to what extent the radiographic bone level threshold for periodontitis case definition influences the prevalence.

MATERIALS AND METHODS

A random sample of 454 subjects underwent a clinical and radiographic examination and answered a questionnaire regarding general health, medications, and smoking habits. Clinical periodontal parameters (periodontal pocket depths, bleeding on probing, mobility, and furcation involvement) and radiographic bone loss were used to identify periodontitis cases and to assess periodontal stage and grade.

RESULTS

Of the 454 participants, 52.6% were defined as "periodontitis cases". Of the total study population "unstable cases of recurrent periodontitis" were present in 38.1%, 16.5% of the participants were assigned to stage II, 32.8% to stage III, and 3.3% to stage IV. When lowering the radiographic bone loss cutoff from > 3 mm to > 2 mm or > 1 mm the prevalence of periodontitis increased to 91.9% and 99.6%.

CONCLUSIONS

Periodontitis was common among 65 year-olds living in Oslo, and in the majority of those with periodontitis, the disease was recurrent and unstable. This study also shows that the choice of bone loss cutoff for defining a periodontitis case affects the prevalence estimates to a large extent. In addition, this study addresses weaknesses in the use of the consensus report of the 2017 World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions for epidemiologic studies in its current form.

Pathways in the association between added sugar consumption, obesity in mother-child dyads, and chronic oral disease burden in early childhood

In this study, we modeled the pathways in the association of socioeconomic status, frequency of added sugar consumption, and obesity in mother-child dyads with chronic oral disease burden in early childhood using structural equation modeling. A population-based study was conducted on preschoolers from public daycare centers in São Luís, Brazil (n = 674) and their mothers. Chronic oral disease burden in early childhood was a latent variable, representing the shared variance of the following indicators: visible plaque index, gingivitis, and dental caries. A higher consumption frequency of added sugars by children [standardized regression coefficient (SC) = 0.219] explained the chronic oral disease burden. A higher consumption frequency of added sugars by mothers was associated with greater consumption of sugar by children (SC = 0.236), and indirectly with a greater chronic oral disease burden (SC = 0.052). Maternal obesity was associated with obesity in the offspring (SC = 0.130). The chronic oral disease burden is already present in early childhood and can be explained by the higher consumption of added sugars by the mother-child dyad. Approaches to preventing chronic oral diseases should focus on common risk factors, start early in life, and promote family involvement in this process.

Internal structure and validity of the bedside oral examination tool in patients with brain injury at neurorehabilitation setting

OBJECTIVES

To assess the internal structure and validity of the 'bedside oral examination' (BOE) instrument in individuals with acquired brain injury (ABI).

METHODS

Ninety ABI individuals were examined using BOE in their first week of neurorehabilitation. BOE measures oral health within eight categories including: swallow, tongue, odour, teeth, lips, saliva, mucosa and gingiva. To assess the validity of BOE, full-mouth clinical examination (gold standard) was performed. The internal structure of BOE was assessed using exploratory and confirmatory factor analyses. To measure the validity, the BOE scores were dichotomised into excellent oral health and altered oral health. Sensitivity, specificity and area under the receiver operating characteristic (ROC) curve of the six/eight BOE items were compared with their related clinical oral examination tool.

RESULTS

Overall, the patients had poor oral health irrespective of the oral examination tool used. Factor analyses indicated two factors within BOE: 'oral hygiene' (teeth, gingiva and mucosa) and 'orofacial health' (lips, swallow and saliva). BOE tongue and odour items loaded in neither factor. BOE items showed low validity since the highest area under the ROC curve was 0.60. Findings on the sensitivity value ranged from 35.0 to 74.2, while specificity from 44.4 to 83.3, depending on the item evaluated.

CONCLUSION

Bedside oral examination does not seem to be an ideal 'single' outcome tool in a neurorehabilitation setting as it lacks validity. BOE evaluates oral health as two independent but correlated components and treat them separately indicating precision treatment depending on their oral health dysfunction. It is advisable to use BOE as a screening tool. However, it should be complimented by proper clinical examination before establishing a treatment plan for oral health in patients with ABI.

Cytokine profiles and the dynamic of gingivitis development in humans

AIM

To investigate the relationship between cytokine profiles and "fast" and "slow" patterns of gingival inflammation development.

MATERIALS AND METHODS

Forty-two adults participated in an experimental gingivitis study, comprising a 2-week hygiene phase (clinical examination and professional cleaning); a 3-week induction phase (absence of oral hygiene); and a 2-week resolution phase (re-establishment of oral hygiene). Plaque and gingival inflammation scores were assessed. Interferon-gamma (IFN-γ), interleukin (IL)-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, and tumour necrosis factor-alpha (TNF-α) from gingival crevicular fluid were collected and measured by multiplex ELISA. Group-based-trajectory-modelling (GBTM) was used to model cytokine profiles over the induction phase. The effect of gingival inflammation on cytokine levels over time was estimated with mixed-effects modelling.

RESULTS

GBTM analysis revealed two cytokine profiles, "non-organized response" (IL-4, IL-6, IL-8, IL-12, and IL-13) and "organized response" (IL-2, IL-10, and TNF-α). Among the "slow" responders, neither cytokine profile was associated with gingivitis. In contrast, a "fast" response was associated with a higher "non-organized response" factor (coef. 0.14) and a lower "organized response" factor (coef. -0.03).

CONCLUSION

A "fast" gingivitis development was associated with a higher "non-organized response" and a lower "organized response", which may elucidate the role of individual variability in gingivitis susceptibility.

Periodontitis and orofacial health-related systemic impairment in patients with brain injury: a factor analysis approach

Aim: To investigate the association of periodontitis to orofacial health-related systemic impairment in patients with acquired brain injury (ABI).Methods: Ninety individuals with ABI were included. Full mouth periodontal examination was performed. Orofacial health-related 'motor' and 'cognitive' scores, dysphagia and feeding status, onset of pneumonia were retrieved from e-journal. Factor analysis dubbed periodontal data as 'moderate' and 'severe' periodontitis while orofacial health-related brain injury scores were dubbed into 'motor' and 'cognitive' domains. Association between periodontal findings and systemic impairments were analyzed using multivariable linear regression models.Results: Higher scores of 'moderate' periodontitis were significantly associated with lower scores of motor impairment (β = -0.2), feeding tube dependency (β = 0.2) and dysphagia (β = 1.21), whereas higher scores of 'severe' periodontitis were associated with lower scores of cognition (β = -0.2) and reduced dental visits (β = -0.2). Both periodontal domains were significantly associated with aging (β = 0.02) and onset of pneumonia (β = 0.5-0.7).Conclusions: Robust association between 'moderate' periodontitis and motor impairment, feeding problems and dysphagia, reflects an acute clinical condition, demanding cross-disciplinary intervention. Periodontal examination can be an early indicator tool for systemic chronic conditions, as ABI and periodontitis share a common environmental, social and biological background. Periodontitis majorly affects ageing population and are prone to pneumonia, compromising rehabilitation plan.

Periodontal disease and preterm birth: Findings from the 2015 Pelotas birth cohort study

OBJECTIVES

To verify the association between periodontal conditions and preterm birth.

MATERIALS AND METHODS

This study used data from the 2015 Pelotas Birth Cohort Study, Brazil. Pregnant women expected to give birth in 2015 were interviewed and dentally examined by a trained dentist, with periodontal measures collected in all teeth, six sites per tooth. Exposure was periodontal disease. Outcomes were preterm birth (all births <37 weeks of gestational age) and early preterm birth (<34 weeks). Analysis was carried out using Poisson regression according to a directed acyclic graph.

RESULTS

A total of 2,474 women participated in the study. Incidence of preterm births was 10.2% and of early preterm births was 3.5%. Frequency of gingivitis was 21.7%, and periodontitis was 14.9%. Periodontitis was associated with a risk almost two times higher of having early preterm delivery compared with healthy pregnant women (RR 1.93; 95% CI 1.09-3.43). Presence of 5+ mm periodontal pocket with bleeding on probing was also associated with higher risk for early preterm delivery.

CONCLUSIONS

The association between periodontal disease in pregnancy and the occurrence of preterm delivery is sensitive to the case definitions. Periodontal disease increased the risk of early preterm delivery.

Oral Health Birth Cohort Studies: Achievements, Challenges, and Potential

Birth cohorts are those among observational studies that provide understanding of the natural history and causality of diseases since early in life. Discussions during an International Association for Dental Research symposium in London, United Kingdom, in 2018, followed by a workshop in Bangkok, Thailand, in 2019, concluded that there are few birth cohort studies that consider oral health and that a broader discussion on similarities and differences among those studies would be valuable. This article aims to 1) bring together available long-term data of oral health birth cohort studies from the low, middle, and high-income countries worldwide and 2) describe similarities and differences among these studies. This work comprises 15 studies from all 5 continents. The most studied dental conditions and exposures are identified; findings are summarized; and methodological differences and similarities among studies are presented. Methodological strengths and weaknesses are also highlighted. Findings are summarized in 1) the negative impact of detrimental socioeconomic status on oral health changes over time, 2) the role of unfavorable patterns of dental visiting on oral health, 3) associations between general and oral health, 4) nutritional and dietary effects on oral health, and 5) intergenerational influences on oral health. Dental caries and dental visiting patterns have been recorded in all studies. Sources of fluoride exposure have been documented in most of the more recent studies. Despite some methodological differences in the way that the exposures and outcomes were measured, some findings are consistent. Predictive models have been used with caries risk tools, periodontitis occurrence, and permanent dentition orthodontic treatment need. The next steps of the group's work are as follows: 1) establishing a consortium of oral health birth cohort studies, 2) conducting a scoping review, 3) exploring opportunities for pooled data analyses to answer pressing research questions, and 4) promoting and enabling the development of the next generation of oral health researchers.

Mechanical Periodontal Therapy Recovered the Phagocytic Function of Monocytes in Periodontitis

BACKGROUND

Several studies have focused on the association between periodontitis and systemic implications; however, the biological mechanisms of the immune responses before and after periodontal therapy involved in this relationship, such as phagocytic functions, remain unclear.

OBJECTIVES

This study aimed to investigate whether periodontal treatment improves the phagocytic function of blood monocytes in patients with severe periodontitis. Materials and Methods. A nonrandomized sample of 55 participants was enrolled in the study. Two groups were studied: control (n = 27, healthy subjects without periodontal disease) and patients (n = 27, healthy subjects without periodontal disease) and patients (.

RESULTS

Periodontitis induced impaired phagocytosis by monocytes. Phagocytosis at baseline was significantly lower in periodontitis patients [median, 13.2 (range of 7.1 to 20.8) and 60.7 (40.6 to 88.6)] than in controls [27.4 (15.5 to 40.5)] and 98 (68.2 to 122.9)] for nonsensitized or sensitized samples, respectively. After supportive therapy, patients showed a significant enhancement of phagocytic functions [33.7 (14.6 to 53.2) and 108.5 (99.6 to 159.5)] for nonsensitized and sensitized samples, respectively. Periodontal treatment increased the phagocytic capacity to a level similar to that observed in the control group and improved the capacity of phagocytes to produce superoxide anion.

CONCLUSIONS

The results suggest that periodontal therapy in patients with severe periodontitis provides a state of homeostasis due to the reestablishment of phagocytic function and increased production of NBT (Regional Registry No. RBR-24T799; Universal Registry No. U1111-1133-5512).

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.

Microbiome Profiles of Ligature-Induced Periodontitis in Nonhuman Primates across the Life Span

This investigation compared the microbiomes colonizing teeth during the initiation, progression, and resolution of periodontitis in nonhuman primates (Macaca mulatta) at different ages. Subgingival plaque samples were collected at baseline; 0.5, 1, and 3 months following ligature-induced periodontitis; and following naturally occurring disease resolution at 5 months. Samples were analyzed using 16S amplicon sequencing to identify bacterial profiles across age groups: young (<3 years of age), adolescent (3 to 7 years), adult (12 to 15 years), and aged (17 to 23 years). α-Diversity of the microbiomes was greater in the adult/aged samples than in the young/adolescent samples. β-Diversity of the samples demonstrated clear age group differences, albeit individual variation in microbiomes between animals within the age categories was noted. Phylum distributions differed between the young/adolescent animals and the adult/aged animals at each of the time points, showing an enrichment of the phyla Spirochetes, Fusobacteria, and Bacteroidetes associated with periodontitis. Major differences in the top 50 operational taxonomic units (OTUs) were noted in the young and adolescent microbiomes during initiation and progression postligation compared to the adult and aged animals. The proportions of a large number of species in the top 50 OTUs were lower at baseline and in resolved disease microbiomes in the young samples, while profiles in adolescent animals were more consistent with the disease microbiomes. Microbiome profiles for resolution for adults and aged animals appeared more resilient and generally maintained a pattern similar to that of disease. Use of the model can expand our understanding of the crucial interactions of the oral microbiome and host responses in periodontitis.

Metabolic syndrome and periodontitis: A structural equation modeling approach

BACKGROUND

This study aimed to investigate the association between metabolic syndrome (MetS) and periodontitis among young adults, and also to compare results using observed and latent variables for MetS and periodontitis.

METHODS

Data from the 1982 Pelotas Birth Cohort, Brazil, were used. Metabolic syndrome at the age of 23 years was measured using clinical and biochemical analysis and set as the main exposure. Periodontitis at the age of 31 years was clinically measured and set as the outcome. Confounding variables included sex and maternal education, assessed at birth, family income at 23 years, and smoking status at the age of 23 and 30 years. Factor analyses (exploratory and confirmatory) were performed to define latent variables for MetS and periodontitis. In addition, both conditions were also defined as categorical observed variables. The association between MetS and periodontitis was tested in structural equation models.

RESULTS

Two latent periodontal variables were identified: "initial" and "advanced" periodontitis, while one latent variable was identified for MetS. Metabolic syndrome is positively associated with "advanced" (coefficient 0.11; P value < 0.01), but not with "initial" (coefficient -0.01; P value = 0.79) periodontitis. When MetS and periodontitis were set as observed variables in the structural equation models, no association was found irrespective of the criteria used for periodontitis classification.

CONCLUSIONS

There was a positive association between metabolic syndrome and "advanced" periodontitis, when the multiple dimensions of both diseases were accounted in latent variables. Nevertheless, when MetS and periodontitis were treated as observed variables, no association was detected irrespective of the criteria used for periodontitis classification.

Ageing effects on humoral immune responses in chronic periodontitis

Periodontal disease is a dominant global bacterial infection that increases with ageing.

AIM

This report focuses on host adaptive immune responses in periodontitis. While experimental models and humans diagnosed with periodontitis demonstrate an antigenic specificity for particular oral bacteria, we have a limited understanding of (i) how ageing affects the adaptive immune responses to these bacteria that chronically colonize the oral cavity for decades prior to disease expression and (ii) how the magnitude and specificity of the response interface with pathogens that emerge within the bacterial ecology during exacerbations of disease.

MATERIALS AND METHODS

Serum antibody levels to a group of pathogenic and commensal oral bacteria were measured in a population of individuals from 21 to 74 years of age, stratified based on clinical status of the periodontium, smoking and sex.

RESULTS

Clinical parameters were not significantly different within health, gingivitis or periodontitis groups related to age. Antibody to oral pathogens and commensals was similar in different age groups in each of the clinical categories, with no age correlation noted in the periodontitis patients.

CONCLUSIONS

The adaptive immune responses to oral bacteria that chronically colonize the oral cavity appear generally unaffected by age, but clearly are linked to the extent of disease.