Journal of Dental Research最新文献

By age 5, approximately one-fifth of children have early childhood caries (ECC). Both the oral microbiome and host genetics are thought to influence susceptibility. Whether the oral microbiome modifies genetic susceptibility to ECC has not been tested. We test whether the salivary bacteriome modifies the association of a polygenic score (PGS, a score derived from genomic data that summarizes genetic susceptibility to disease) for primary tooth decay on ECC in the Center for Oral Health Research in Appalachia 2 longitudinal birth cohort. Children were genotyped using the Illumina Multi-Ethnic Genotyping Array and underwent annual dental examinations. We constructed a PGS for primary tooth decay using weights from an independent, genome-wide association meta-analysis. Using Poisson regression, we tested for associations between the PGS (high versus low) and ECC incidence, adjusting for demographic characteristics (n = 783). An incidence-density sampled subset of the cohort (n = 138) had salivary bacteriome data at 24 mo of age. We tested for effect modification of the PGS on ECC case status by salivary bacterial community state type (CST). By 60 mo, 20.69% of children had ECC. High PGS was not associated with an increased rate of ECC (incidence rate ratio, 1.09; 95% confidence interval [CI], 0.83-1.42). However, having a cariogenic salivary bacterial CST at 24 mo was associated with ECC (odds ratio [OR], 7.48; 95% CI, 3.06-18.26), which was robust to PGS adjustment. An interaction existed between the salivary bacterial CST and the PGS on the multiplicative scale (P = 0.04). The PGS was associated with ECC (OR, 4.83; 95% CI, 1.29-18.17) only among individuals with a noncariogenic salivary bacterial CST (n = 70). Genetic causes of caries may be harder to detect when not accounting for cariogenic oral microbiomes. As certain salivary bacterial CSTs increased ECC risk across genetic risk strata, preventing colonization of cariogenic microbiomes would be universally beneficial.

Periodontitis is a common finding among people with diabetes mellitus (DM) and has been cited as a DM complication. Whether and how periodontitis relates to other diabetes-related complications has yet to be explored. This study aims to examine the clustering of periodontitis with other diabetes-related complications and explore pathways linking diabetes-related complications with common risk factors. Using data from participants with DM across 3 cycles of the National Health and Nutrition Examination Survey (NHANES) (n = 2,429), we modeled direct and indirect pathways from risk factors to diabetes-related complications, a latent construct comprising periodontitis, cardiovascular diseases, proteinuria, and hypertension. Covariates included age, sex, socioeconomic status (SES), smoking, physical activity, healthy diet, alcohol consumption, hemoglobin A1c (HbA1c), dyslipidemia, and body mass index (BMI). Sensitivity analyses were performed considering participants with overweight/obesity and restricting the sample to individuals without DM. Periodontitis clustered with other diabetes complications, forming a latent construct dubbed diabetes-related complications. In NHANES III, higher HbA1c levels and BMI, older age, healthy diet, and regular physical activity were directly associated with the latent variable diabetes-related complications. In addition, a healthy diet and BMI had a total effect on diabetes-related complications. Although sex, smoking, dyslipidemia, and SES demonstrated no direct effect on diabetes-related complications in NHANES III, a direct effect was observed using NHANES 2011-2014 cycles. Sensitivity analysis considering participants with overweight/obesity and without DM showed consistent results. Periodontal tissue breakdown seems to co-occur with multiple diabetes-related complications and may therefore serve as a valuable screening tool for other well-known diabetes-related complications.

Pregnancy initiates a temporary transition in the maternal physiological state, with a shift in the oral microbiome and a potential increase in frequency of oral diseases. The risk of oral disease is higher among populations of Hispanic and Black women and those with lower socioeconomic status (low SES), demonstrating a need for intervention within these high-risk populations. To further our understanding of the oral microbiome of high-risk pregnant women, we characterized the oral microbiome in 28 nonpregnant and 179 pregnant low-SES women during their third trimester living in Rochester, New York. Unstimulated saliva and supragingival plaque samples were collected cross-sectionally, followed by assessment of the bacterial (16S ribosomal RNA) and fungal (18S ITS) microbiota communities. Trained and calibrated dentists performed oral examinations to determine the number of decayed teeth and plaque index. Initially, plaque from 28 nonpregnant women and 48 pregnant women were compared; these data showed significant differences in bacterial abundances based on pregnancy status. To further our understanding of the oral microbiome within the pregnant population, we next examined the oral microbiome within this population based on several variables. Streptococcus mutans, Streptococcus oralis, and Lactobacillus were associated with a greater number of decayed teeth. The composition of fungal communities differed between plaque and saliva, demonstrating 2 distinct "mycotypes" that were represented by a greater abundance of Candida in plaque and Malassezia in saliva. Veillonella rogosae, a common oral bacterium, was negatively associated with both plaque index and salivary Candida albicans colonization by culture data. This was further emphasized by in vitro inhibition of C. albicans by V. rogosae. Identification of interactions between the bacterial or fungal oral communities revealed that V. rogosae was positively associated with the oral commensal Streptococcus australis and negatively with the cariogenic Lactobacillus genus, suggesting V. rogosae as a potential biomarker of a noncariogenic oral microbiome.

Dental adhesives provide retention to composite fillings in dental restorations. Microtensile bond strength (µTBS) test is the most used laboratory test to evaluate bonding performance of dental adhesives. The traditional approach for developing dental adhesives involves repetitive laboratory measurements, which consumes enormous time and resources. Machine learning (ML) is a promising tool for accelerating this process. This study aimed to develop ML models to predict the µTBS of dental adhesives using their chemical features and to identify important contributing factors for µTBS. Specifically, the chemical composition and µTBS information of 81 dental adhesives were collected from the manufacturers and the literature. The average µTBS value of each adhesive was labeled as either 0 (if <36 MPa) or 1 (if ≥36 MPa) to denote the low and high µTBS classes. The initial 9-feature data set comprised pH, HEMA, BisGMA, UDMA, MDP, PENTA, filler, fluoride, and organic solvent (OS) as input features. Nine ML algorithms, including logistic regression, k-nearest neighbor, support vector machine, decision trees and tree-based ensembles, and multilayer perceptron, were implemented for model development. Feature importance analysis identified MDP, pH, OS, and HEMA as the top 4 contributing features, which were used to construct a 4-feature data set. Grid search with stratified 10-fold cross-validation (CV) was employed for hyperparameter tunning and model performance evaluation using 2 metrics, the area under the receiver operating characteristic curve (AUC) and accuracy. The 4-feature data set generated slightly better performance than the 9-feature data set, with the highest AUC score of 0.90 and accuracy of 0.81 based on stratified CV. In conclusion, ML is an effective tool for predicting dental adhesives with low and high µTBS values and for identifying important chemical features contributing to the µTBS. The ML-based data-driven approach has great potential to accelerate the discovery of new dental adhesives and other dental materials.

Cleft lip and palate have a complex inheritance, and 90% of its variation in the population is due to genetic contributors. The impact of surgical procedures on maxillofacial growth is well known, but the interference of intrinsic factors in these growth outcomes is not elucidated. The present study aimed to analyze genetic polymorphisms and frequency of dental anomalies as predictors of maxillofacial growth in patients born with cleft lip with or without cleft palate. From a cohort of 537 individuals, operated on by the same surgeon, 121 were analyzed 2 times, to define changes in maxillary growth prognosis by occlusal scores in a minimum 4-y follow-up. In a second step, a subset of 360 individuals had maxillofacial growth outcomes evaluated using Wits, nasion perpendicular to point A, and occlusal scores. The markers MMP2 rs9923304, GLI2 rs3738880 and rs2279741, TGFA rs2166975, and FGFR2 rs11200014 and rs10736303 were genotyped, and frequency of dental anomalies and cleft severity were determined to define evidence of overrepresentation of alleles associated with maxillofacial growth outcomes. Age and age at primary surgical treatment, sex, and cleft laterality were variables adjusted in the analysis. We found an association between the frequency of dental anomalies and the maxillofacial growth in unilateral (P = 0.001) and bilateral (P = 0.03) individuals with clefts. MMP2 rs9923304 and maxillofacial growth were associated (P < 0.0001). There was also an association between GLI2 rs3738880 and TGFA rs2166975 and maxillary outcomes in individuals born with unilateral cleft lip and palate (P = 0.003 and P = 0.004, respectively), as well as between FGFR2 rs11200014 and maxillary outcomes regardless of cleft type (P = 0.005). Statistical evidence of an interaction between MMP2 rs9923304 and GLI2 rs3738880 was observed (P < 0.0001). Presence of dental anomalies and genetic variation in MMP2, GLI2, TGFA, and FGFR2 were associated with worse maxillofacial growth outcomes in individuals born with clefts.

Evidence connects mental illness to other adverse health conditions, including oral health. However, longitudinal associations between mental and oral health remain understudied. We aimed to examine mental health-oral health associations prospectively in a nationally representative US cohort. Data were from the Population Assessment of Tobacco and Health (PATH) Study. The Global Appraisal of Individual Needs-Short Screener measured 3 types of mental health symptoms: internalizing, externalizing, and substance use problems. Six self-reported oral health conditions related to periodontal disease were evaluated: self-rated oral health, bleeding gums, loose teeth, tooth extraction, gum disease, and bone loss around teeth. Cross-sectional analysis within PATH Study wave 4 (2016 to 2018, n = 30,746) compared the survey-weighted prevalence of the 6 oral health outcomes according to severity of mental health problems. Prospectively, oral health outcomes were assessed 2 y later (wave 5, 2018 to 2019) according to wave 4 (baseline) mental health problems (n = 26,168). Survey-weighted logistic regression models controlled for confounders (age, sex, tobacco use, etc.) with imputation for missing values. All 6 adverse oral health conditions were greater in prevalence among participants with severe internalizing problems. Multiple conditions were also associated with severe externalizing or substance use problems. Longitudinally associations attenuated, but multiple associations of meaningful magnitude persisted, most with internalizing problems. For example, the adjusted odds ratio was 1.27 (95% CI, 1.08 to 1.50) for bleeding gums and 1.37 (95% CI, 1.12 to 1.68) for tooth extraction when we compared severe versus none/low internalizing problems. Providers should expect higher levels of oral disease among patients with adverse mental health symptoms. Independent of externalizing and substance use problems, symptoms of internalizing problems (related to depression and/or anxiety) are plausible risk factors for future oral disease. Better integration and coordination of mental and oral health treatment and prevention are recommended.

The hemostatic and inflammatory systems work hand in hand to maintain homeostasis at mucosal barrier sites. Among the factors of the hemostatic system, fibrin is well recognized for its role in mucosal homeostasis, wound healing, and inflammation. Here, we present a basic overview of the fibrinolytic system, discuss fibrin as an innate immune regulator, and provide recent work uncovering the role of fibrin-neutrophil activation as a regulator of mucosal/periodontal homeostasis. We reason that the role of fibrin in periodontitis becomes most evident in individuals with the Mendelian genetic defect, congenital plasminogen (PLG) deficiency, who are predisposed to severe periodontitis in childhood due to a defect in fibrinolysis. Consistent with plasminogen deficiency being a risk factor for periodontitis, recent genomics studies uncover genetic polymorphisms in PLG, encoding plasminogen, being significantly associated with periodontal disease, and suggesting PLG variants as candidate risk indicators for common forms of periodontitis.

Tooth enamel is generated by ameloblasts. Any failure in amelogenesis results in defects in the enamel, a condition known as amelogenesis imperfecta. Here, we report that mice with deficient autophagy in epithelial-derived tissues (K14-Cre;Atg7^F/F and K14-Cre;Atg3^F/F conditional knockout mice) exhibit amelogenesis imperfecta. Micro-computed tomography imaging confirmed that enamel density and thickness were significantly reduced in the teeth of these mice. At the molecular level, ameloblast differentiation was compromised through ectopic accumulation and activation of NRF2, a specific substrate of autophagy. Through bioinformatic analyses, we identified Bcl11b, Dlx3, Klk4, Ltbp3, Nectin1, and Pax9 as candidate genes related to amelogenesis imperfecta and the NRF2-mediated pathway. To investigate the effects of the ectopic NRF2 pathway activation caused by the autophagy deficiency, we analyzed target gene expression and NRF2 binding to the promoter region of candidate target genes and found suppressed gene expression of Bcl11b, Dlx3, Klk4, and Nectin1 but not of Ltbp3 and Pax9. Taken together, our findings indicate that autophagy plays a crucial role in ameloblast differentiation and that its failure results in amelogenesis imperfecta through ectopic NRF2 activation.

We aimed to develop and validate caries prognosis models in primary and permanent teeth after 2 and 10 y of follow-up through a machine learning (ML) approach, using predictors collected in early childhood. Data from a 10-y prospective cohort study conducted in southern Brazil were analyzed. Children aged 1 to 5 y were first examined in 2010 and reassessed in 2012 and 2020 regarding caries development. Dental caries was assessed using the Caries Detection and Assessment System (ICDAS) criteria. Demographic, socioeconomic, psychosocial, behavioral, and clinical factors were collected. ML algorithms decision tree, random forest, and extreme gradient boosting (XGBoost) were employed, along with logistic regression. The discrimination and calibration of models were verified in independent sets. From 639 children included at the baseline, we reassessed 467 (73.3%) and 428 (66.9%) children in 2012 and 2020, respectively. For all models, the area under receiver operating characteristic curve (AUC) at training and testing was above 0.70 for predicting caries in primary teeth after 2-y follow-up, with caries severity at the baseline being the strongest predictor. After 10 y, the SHAP algorithm based on XGBoost achieved an AUC higher than 0.70 in the testing set and indicated caries experience, nonuse of fluoridated toothpaste, parent education, higher frequency of sugar consumption, low frequency of visits to the relatives, and poor parents' perception of their children's oral health as top predictors for caries in permanent teeth. In conclusion, the implementation of ML shows potential for determining caries development in both primary and permanent teeth using easy-to-collect predictors in early childhood.