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Supplemental material, sj-docx-1-jct-10.1177_23800844221143683 for Multiple Imputation for Partial Recording Periodontal Examination Protocols by J.S. Preisser, T. Shing, B.F. Qaqish, K. Divaris and J. Beck in JDR Clinical & Translational Research

Supplemental material, sj-docx-1-jdr-10.1177_00220345221109775 for Phenotype Harmonization in the GLIDE2 Oral Health Genomics Consortium by K. Divaris, S. Haworth, J.R. Shaffer, V. Anttonen, J.D. Beck, Y. Furuichi, B. Holtfreter, D. Jönsson, T. Kocher, S.M. Levy, P.K.E. Magnusson, D.W. McNeil, K. Michaëlsson, K.E. North, U. Palotie, P.N. Papapanou, P.J. Pussinen, D. Porteous, K. Reis, A. Salminen, A.S. Schaefer, T. Sudo, Y.Q. Sun, A.L. Suominen, T. Tamahara, S.M. Weinberg, P. Lundberg, M.L. Marazita and I. Johansson in Journal of Dental Research

PURPOSE: The purpose of the study was to develop and evaluate an automated machine learning algorithm (AutoML) for children’s classification according to early childhood caries (ECC) status. METHODS: Clinical, demographic, behavioral, and parent-reported oral health status information for a sample of 6,404 three- to five-year-old children (mean age equals 54 months) participating in an epidemiologic study of early childhood oral health in North Carolina was used. ECC prevalence (decayed, missing, and filled primary teeth surfaces [dmfs] score greater than zero, using an International Caries Detection and Assessment System score greater than or equal to three caries lesion detection threshold) was 54 percent. Ten sets of ECC predictors were evaluated for ECC classification accuracy (i.e., area under the ROC curve [AUC], sensitivity [Se], and positive predictive value [PPV]) using an AutoML deployment on Google Cloud, followed by internal validation and external replication. RESULTS: A parsimonious model including two terms (i.e., children’s age and parent-reported child oral health status: excellent/very good/good/fair/poor) had the highest AUC (0.74), Se (0.67), and PPV (0.64) scores and similar performance using an external National Health and Nutrition Examination Survey (NHANES) dataset (AUC equals 0.80, Se equals 0.73, PPV equals 0.49). Contrarily, a comprehensive model with 12 variables covering demographics (e.g., race/ethnicity, parental education), oral health behaviors, fluoride exposure, and dental home had worse performance (AUC equals 0.66, Se equals 0.54, PPV equals 0.61). CONCLUSIONS: Parsimonious automated machine learning early childhood caries classifiers, including single-item self-reports, can be valuable for ECC screening. The classifier can accommodate biological information that can help improve its performance in the future.

Dental caries is characterized by a dysbiotic shift at the biofilm–tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study’s analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)–machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10−3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10−6) and N-acetylneuraminate (p = 6.8 × 10−6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10−6) and epicatechin (P = 2.9 × 10−6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

Adenoid cystic carcinoma (ACC) is a rare salivary gland tumor, displaying aggressive behavior with frequent recurrence and metastasis. Little information exists regarding the impact of clinicopathological parameters and adjuvant radiotherapy (aRT) on ACC disease specific (DSS) and overall survival (OS). We extracted demographic, treatment, and survival information of 1439 patients with major or minor intraoral salivary gland ACC from the Surveillance, Epidemiology, and End Results (SEER) database. The associations between tumor characteristics and aRT with OS and DSS were estimated using hazard ratios (HR) and 95% confidence intervals (CI). Submandibular gland ACCs had the worst prognosis (adjusted DSS HR = 1.48; 95% CI = 0.99–2.20, compared to parotid), and this difference was more pronounced among patients with advanced-stage tumors (adjusted DSS HR = 1.93; 95% CI = 1.13–3.30). aRT was associated with increased overall survival only among stage III submandibular ACC patients (HR = 0.64; 95% CI = 0.42–0.98) and had no benefit in any other group. In conclusion, submandibular gland ACC carries a worse prognosis than other gland subsites and may benefit from aRT. The different outcomes between submandibular gland and other major or minor gland ACCs warrant further mechanistic investigation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Background: Oral health literacy is an important construct for both clinical and public health outcomes research. The need to quantify and test OHL has led to the development of measurement instruments and has generated a substantial body of recent literature. A commonly used OHL instrument is REALD-30, a word recognition scale that has been adapted for use in several languages. The objective of this study was the development and testing of the Greek language oral health literacy measurement instrument (GROHL). Methods: Data from 282 adult patients of two private dental clinics in Athens, Greece were collected via in-person interviews. Forty-four words were initially considered and tested for inclusion. Item response theory analysis (IRT) and 2-parameter logistic models assessing difficulty and discriminatory ability were used to identify an optimal scale composition. Internal consistency was examined using Cronbach's alpha and test-retest reliability was measured using intraclass correlation coefficient (ICC) in a subset of 20 participants over a two-week period. Convergent validity was tested against functional health literacy screening (HLS) items, dental knowledge (DK), oral health behaviors (OHBs), oral health-related quality of life (OHRQoL; OHIP-14 index), as well as self-reported oral and general health status. Results: From an initial item pool of 44 items that were carried forward to IRT, 12 were excluded due to no or little variance, 10 were excluded due to low item-test correlation, and 2 due to insignificant contribution to the scale, i.e., difficulty parameter estimate with p > 0.05. The twenty remaining items composed the final index which showed favorable internal consistency (alpha = 0.80) and test-retest reliability (ICC = 0.95). The summary score distribution did not depart from normality (p = 0.32; mean = 11.5; median = 12; range = 1-20). GROHL scores were positively correlated with favorable oral hygiene behaviors and dental attendance, as well as HLS, DK and education level. Conclusion: The GROHL demonstrated good psychometric properties and can be used for outcomes research in clinical and public health settings. © 2020 The Author(s).

Background: The oral health of preschool-age children can affect their quality of life (QoL) as well their families. The Early Childhood Oral Health Impact Scale (ECOHIS) is a reliable instrument that has been used to assess the impact of oral health problems and their treatment on the QoL of preschool-age children and their families’. Objective: To report the development, evaluation and psychometric properties of the Greek version of ECOHIS (Gr-ECOHIS). Methods: Participants of this cross-sectional study were 176 mothers and their young (aged 25-71 months) children, patients of a private pediatric dental practice. During a structured interview, they completed a questionnaire, including a translated, Greek language version of the ECOHIS. Data on children’s oral health were obtained via clinical examinations. The psychometric properties of Gr-ECOHIS evaluated were reliability (internal and test-retest) and construct (convergent and discriminant) validity. Test-retest reliability was determined in an independent sample of 20 mother-child dyads, who completed the Gr-ECOHIS twice within a two-week interval. Results: The scale showed excellent internal consistency (Cronbach’s alpha=0.85) and test-retest reliability (Intraclass Correlation Coefficient= 0.97). Gr-ECOHIS showed a strong correlation with dental caries (Spearman’s rho=0.62, p

To investigate racial differences in the associations between periodontitis and 10-year self-reported incident tooth loss in a biracial, community-based cohort of US late middle-aged and older adults.Subjects were 3,466 dentate men and women aged 53-74 who underwent dental examinations from 1996 to1998. In 2012-2013, telephone interviewers asked participants about tooth loss in the preceding 10 years. Separate multivariable ordinal logistic regression models were used to calculate proportional odds ratios (OR) and 95% confidence intervals (CI) as estimates of association between periodontitis and tooth loss for Whites and African-Americans (AAs).The majority of participants were White (85 percent) and female (57 percent) with 23 teeth on average at enrollment. Approximately half the Whites (56 percent) and AAs (49 percent) had periodontitis. At follow-up, approximately 44 percent of AAs and 38 percent of Whites reported having lost ≥1 tooth. In multivariable models, severe periodontitis (OR = 3.03; 95% CI = 2.42-3.80) and moderate periodontitis (OR = 1.64; 95% CI= 1.39-1.94) were significant risk factors of incident tooth loss among Whites. For AAs, severe but not moderate periodontitis increased the odds of incident tooth loss (OR = 2.22; 95% CI = 1.37-3.59). In the final model, education was inversely associated with incident tooth loss among AAs, while lower income was associated with greater odds of tooth loss among Whites.In this population-based cohort, there is racial heterogeneity in the association between periodontitis and tooth loss. Interventions to reduce the impact of periodontitis on tooth loss need to consider these differences.

Indicators of poor oral health, including smoking, have been associated with increased risk of head and neck squamous cell carcinoma, especially oropharyngeal squamous cell carcinoma (OPSCC), yet few studies have examined whether this association is modified by human papillomavirus (HPV) status.Data from interviews and tumor HPV status from a large population-based case-control study, the Carolina Head and Neck Cancer Study (CHANCE), were used to estimate the association between oral health indicators and smoking among 102 HPV-positive patients and 145 HPV-negative patients with OPSCC and 1396 controls. HPV status was determined by p16INK4a (p16) immunohistochemistry. Unconditional, multinomial logistic regression was used to estimate odds ratios (ORs) for all oral health indictors adjusting for important covariates.Routine dental examinations were associated with a decreased risk of both HPV-negative OPSCC (OR, 0.52; 95% confidence interval [CI], 0.35-0.76) and HPV-positive OPSCC (OR, 0.55; 95% CI, 0.36-.86). Tooth mobility (a proxy for periodontal disease) increased the risk of HPV-negative disease (OR, 1.70; 95% CI, 1.18-2.43) slightly more than the risk for HPV-positive disease (OR, 1.45; 95% CI, 0.95-2.20). Ten or more pack-years of cigarette smoking were strongly associated with an increased risk of HPV-negative OPSCC (OR, 4.26; 95% CI, 2.85-6.37) and were associated less with an increased risk of HPV-positive OPSCC (OR, 1.62; 95% CI, 1.10-2.38).Although HPV-positive and HPV-negative HNSCC differ significantly with respect to etiology and tumorigenesis, the current findings suggest a similar pattern of association between poor oral health, frequency of dental examinations, and both HPV-positive and HPV-negative OPSCC. Future research is required to elucidate interactions between poor oral health, tobacco use, and HPV in the development of OPSCC. Cancer 2017;71-80. © 2016 American Cancer Society.

Background: The observational relationship between obesity and periodontitis is widely known, yet causal evidence is lacking. Our objective was to investigate causal associations between periodontitis and body mass index (BMI).

Introduction Elucidating the microbial ecology of endodontic infections (EIs) is a necessary step in developing effective intracanal antimicrobials. The aim of the present study was to investigate the bacterial composition of symptomatic and asymptomatic primary and persistent infections in a Greek population using high-throughput sequencing methods. Methods 16S amplicon pyrosequencing of 48 root canal bacterial samples was conducted, and sequencing data were analyzed using an oral microbiome-specific and a generic (Greengenes) database. Bacterial abundance and diversity were examined by EI type (primary or persistent), and statistical analysis was performed by using non-parametric and parametric tests accounting for clustered data. Results Bacteroidetes was the most abundant phylum in both infection groups. Significant, albeit weak associations of bacterial diversity were found, as measured by UniFrac distances with infection type (analyses of similarity, R = 0.087, P =.005) and symptoms (analyses of similarity, R = 0.055, P =.047). Persistent infections were significantly enriched for Proteobacteria and Tenericutes compared with primary ones; at the genus level, significant differences were noted for 14 taxa, including increased enrichment of persistent infections for Lactobacillus, Streptococcus, and Sphingomonas. More but less abundant phyla were identified using the Greengenes database; among those, Cyanobacteria (0.018%) and Acidobacteria (0.007%) were significantly enriched among persistent infections. Persistent infections showed higher phylogenetic diversity (PD) (asymptomatic: PD = 9.2, standard error [SE] = 1.3; symptomatic: PD = 8.2, SE = 0.7) compared with primary infections (asymptomatic: PD = 5.9, SE = 0.8; symptomatic: PD = 7.4, SE = 1.0). Conclusions The present study revealed a high bacterial diversity of EI and suggests that persistent infections may have more diverse bacterial communities than primary infections. © 2015 American Association of Endodontists.