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  • 1
    Online Resource
    Online Resource
    SAGE Publications ; 2000
    In:  Journal of Dental Research Vol. 79, No. 10 ( 2000-10), p. 1808-1814
    In: Journal of Dental Research, SAGE Publications, Vol. 79, No. 10 ( 2000-10), p. 1808-1814
    Abstract: Accumulating evidence indicates that epithelia are not merely mechanical barriers but also important elements of the innate immune system. The present study was performed to examine cytokine responses of oral epithelial cells after infection with the periodontal pathogen Porphyromonas gingivalis. The KB-cell line and primary cultures of periodontal pocket epithelium were infected with P. gingivalis for assessment of bacterial invasion by an antibiotic protection assay, and examinination of expression of interleukin-1 beta, interleukin-6, interleukin-8, and tumor necrosis factor-alpha by in situ hybridization and immunohistochemistry. We observed that P. gingivalis induces a strong cytokine response, positively correlated with the adhesive/invasive potential of the infecting strain, in both KB cells and primary cultures. These findings indicate that the epithelial cells of the periodontal pocket are an integral part of the immune system, eliciting cytokine responses to a bacterial challenge. In this context, the adhesive/invasive phenotype of P. gingivalis appears to contribute to pathogenicity.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2000
    detail.hit.zdb_id: 2057074-0
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  • 2
    Online Resource
    Online Resource
    SAGE Publications ; 2021
    In:  Journal of Dental Research Vol. 100, No. 5 ( 2021-05), p. 549-556
    In: Journal of Dental Research, SAGE Publications, Vol. 100, No. 5 ( 2021-05), p. 549-556
    Abstract: Genome-wide transcriptomic analyses in whole tissues reflect the aggregate gene expression in heterogeneous cell populations comprising resident and migratory cells, and they are unable to identify cell type–specific information. We used a computational method (population-specific expression analysis [PSEA]) to decompose gene expression in gingival tissues into cell type–specific signatures for 8 cell types (epithelial cells, fibroblasts, endothelial cells, neutrophils, monocytes/macrophages, plasma cells, T cells, and B cells). We used a gene expression data set generated using microarrays from 120 persons (310 tissue samples; 241 periodontitis affected and 69 healthy). Decomposition of the whole-tissue transcriptomes identified differentially expressed genes in each of the cell types, which mapped to biologically relevant pathways, including dysregulation of Th17 cell differentiation, AGE-RAGE signaling, and epithelial-mesenchymal transition in epithelial cells. We validated selected PSEA-predicted, differentially expressed genes in purified gingival epithelial cells and B cells from an unrelated cohort ( n = 15 persons), each of whom contributed with 1 periodontitis-affected and 1 healthy gingival tissue sample. Differential expression of these genes by quantitative reverse transcription polymerase chain reaction corroborated the PSEA predictions and pointed to dysregulation of biologically important pathways in periodontitis. Collectively, our results demonstrate the robustness of the PSEA in the decomposition of gingival tissue transcriptomes and its ability to identify differentially regulated transcripts in particular cellular constituents. These genes may serve as candidates for furt her investigation with respect to their roles in the pathogenesis of periodontitis.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2057074-0
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  • 3
    Online Resource
    Online Resource
    SAGE Publications ; 2020
    In:  Journal of Dental Research Vol. 99, No. 1 ( 2020-01), p. 44-50
    In: Journal of Dental Research, SAGE Publications, Vol. 99, No. 1 ( 2020-01), p. 44-50
    Abstract: 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 cur ves; 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.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 2057074-0
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  • 4
    Online Resource
    Online Resource
    SAGE Publications ; 2013
    In:  Journal of Dental Research Vol. 92, No. 12 ( 2013-12), p. 1081-1088
    In: Journal of Dental Research, SAGE Publications, Vol. 92, No. 12 ( 2013-12), p. 1081-1088
    Abstract: The 2 major forms of periodontitis, chronic (CP) and aggressive (AgP), do not display sufficiently distinct histopathological characteristics or microbiological/immunological features. We used molecular profiling to explore biological differences between CP and AgP and subsequently carried out supervised classification using machine-learning algorithms including an internal validation. We used whole-genome gene expression profiles from 310 ‘healthy’ or ‘diseased’ gingival tissue biopsies from 120 systemically healthy non-smokers, 65 with CP and 55 with AgP, each contributing with ≥ 2 ‘diseased’ gingival papillae (n = 241; with bleeding-on-probing, probing depth ≥ 4 mm, and clinical attachment loss ≥ 3 mm), and, when available, a ‘healthy’ papilla (n = 69; no bleeding-on-probing, probing depth ≤ 4 mm, and clinical attachment loss ≤ 4 mm). Our analyses revealed limited differences between the gingival tissue transcriptional profiles of AgP and CP, with genes related to immune responses, apoptosis, and signal transduction overexpressed in AgP, and genes related to epithelial integrity and metabolism overexpressed in CP. Different classifying algorithms discriminated CP from AgP with an area under the curve ranging from 0.63 to 0.99. The small differences in gene expression and the highly variable classifier performance suggest limited dissimilarities between established AgP and CP lesions. Future analyses may facilitate the development of a novel, ‘intrinsic’ classification of periodontitis based on molecular profiling.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2013
    detail.hit.zdb_id: 2057074-0
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  • 5
    Online Resource
    Online Resource
    SAGE Publications ; 2010
    In:  Journal of Dental Research Vol. 89, No. 9 ( 2010-09), p. 879-902
    In: Journal of Dental Research, SAGE Publications, Vol. 89, No. 9 ( 2010-09), p. 879-902
    Abstract: Evidence from epidemiologic studies suggests that periodontal infections are independently associated with subclinical and clinical atherosclerotic vascular disease. Although the strength of the reported associations is modest, the consistency of the data across diverse populations and a variety of exposure and outcome variables suggests that the findings are not spurious or attributable only to the effects of confounders. Analysis of limited data from interventional studies suggests that periodontal treatment generally results in favorable effects on subclinical markers of atherosclerosis, although such analysis also indicates considerable heterogeneity in responses. Experimental mechanistic in vitro and in vivo studies have established the plausibility of a link between periodontal infections and atherogenesis, and have identified biological pathways by which these effects may be mediated. However, the utilized models are mostly mono-infections of host cells by a limited number of ‘model’ periodontal pathogens, and therefore may not adequately portray human periodontitis as a polymicrobial, biofilm-mediated disease. Future research must identify in vivo pathways in humans that may (i) lead to periodontitis-induced atherogenesis, or (ii) result in treatment-induced reduction of atherosclerosis risk. Data from these studies will be essential for determining whether periodontal interventions have a role in the primary or secondary prevention of atherosclerosis.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2010
    detail.hit.zdb_id: 2057074-0
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  • 6
    Online Resource
    Online Resource
    SAGE Publications ; 2019
    In:  Journal of Dental Research Vol. 98, No. 10 ( 2019-09), p. 1053-1062
    In: Journal of Dental Research, SAGE Publications, Vol. 98, No. 10 ( 2019-09), p. 1053-1062
    Abstract: Periodontal medicine is a term used to describe how periodontal infection/inflammation may impact extraoral health. Periodontitis has been linked to over 50 systemic diseases and conditions. As part of the Journal of Dental Research’s Centennial Celebration, this narrative review discusses periodontal medicine research done over the past 100 y, with particular focus on the effects of periodontal disease on 3 pathological conditions: cardiovascular disease, diabetes mellitus, and adverse pregnancy outcomes. We selected 29 total studies that were the “first” of their kind, as they provided novel observations or contributed to shifting paradigms as well as important studies that made strong contributions to progress in understanding relationships to the systemic conditions. These studies were organized in an overview timeline and broken down into timelines by topic: cardiovascular disease ( n = 10), diabetes ( n = 12), and adverse pregnancy outcomes ( n = 7). Overall, the majority of cross-sectional, case-control, and longitudinal studies have revealed positive associations between poor periodontal status and cardiovascular disease, diabetes metabolic control, and a number of adverse pregnancy outcomes, and these associations are upheld in systematic reviews. Findings from randomized controlled trials testing the effects of periodontal therapy on systemic health outcomes were conflicting and inconsistent. While there has been a great deal of progress, we highlight lessons learned and make comments and suggestions on a number of key aspects, including the heterogeneity of case definitions of periodontal disease across studies, accounting for features of the periodontal phenotype that are most relevant to the biological link between periodontitis and systemic outcomes, the role of other comorbid inflammatory conditions, selection of study participants, and timing and intensity of the periodontal intervention.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2057074-0
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  • 7
    In: Journal of Dental Research, SAGE Publications, Vol. 98, No. 13 ( 2019-12), p. 1488-1496
    Abstract: Microbial communities along mucosal surfaces throughout the digestive tract are hypothesized as risk factors for impaired glucose regulation and the development of clinical cardiometabolic disease. We investigated whether baseline measures of subgingival microbiota predicted fasting plasma glucose (FPG) longitudinally. The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) enrolled 230 diabetes-free adults (77% female) aged 20 to 55 y (mean ± SD, 34 ± 10 y) from whom baseline subgingival plaque and longitudinal FPG were measured. DNA was extracted from subgingival plaque, and V3 to V4 regions of the 16S rRNA gene were sequenced. FPG was measured at baseline and again at 2 y; glucose change was defined as follow-up minus baseline. Multivariable linear models regressed 2-y glucose change onto baseline measures of community diversity and abundances of 369 individual taxa. A microbial dysbiosis index (MDI) summarizing top individual taxa associated with glucose change was calculated and used in regression models. Models were adjusted for age, sex, race/ethnicity, education, smoking status, body mass index, and baseline glucose levels. Statistical significance was based on the false discovery rate (FDR; 〈 0.05) or a Bonferroni-corrected P value of 1 × 10 -4 , derived from the initial 369 hypothesis tests for specific taxa. Mean 2-y FPG change was 1.5 ± 8 mg/dL. Baseline levels of 9 taxa predicted FPG change (all FDR 〈 0.05), among which Stomatobaculum sp oral taxon 097 and Atopobium spp predicted greater FPG change, while Leptotrichia sp oral taxon 498 predicted lesser FPG change (all 3 P values, Bonferroni significant). The MDI explained 6% of variation in longitudinal glucose change ( P 〈 0.001), and baseline glucose levels explained 10% of variation ( P 〈 0.0001). FPG change values ± SE in the third versus first tertile of the MDI were 4.5 ± 0.9 versus 1.6 ± 0.9 ( P 〈 1 × 10 -4 ). Subgingival microbiota predict 2-y glucose change among diabetes-free men and women.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2057074-0
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  • 8
    Online Resource
    Online Resource
    SAGE Publications ; 2019
    In:  Journal of Dental Research Vol. 98, No. 7 ( 2019-07), p. 734-738
    In: Journal of Dental Research, SAGE Publications, Vol. 98, No. 7 ( 2019-07), p. 734-738
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2057074-0
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  • 9
    Online Resource
    Online Resource
    SAGE Publications ; 2016
    In:  Journal of Dental Research Vol. 95, No. 9 ( 2016-08), p. 1010-1017
    In: Journal of Dental Research, SAGE Publications, Vol. 95, No. 9 ( 2016-08), p. 1010-1017
    Abstract: Analytic approaches confined to fold-change comparisons of gene expression patterns between states of health and disease are unable to distinguish between primary causal disease drivers and secondary noncausal events. Genome-wide reverse engineering approaches can facilitate the identification of candidate genes that may distinguish between causal and associative interactions and may account for the emergence or maintenance of pathologic phenotypes. In this work, we used the algorithm for the reconstruction of accurate cellular networks (ARACNE) to analyze a large gene expression profile data set (313 gingival tissue samples from a cross-sectional study of 120 periodontitis patients) obtained from clinically healthy ( n = 70) or periodontitis-affected ( n = 243) gingival sites. The generated transcriptional regulatory network of the gingival interactome was subsequently interrogated with the master regulator inference algorithm (MARINA) and gene expression signature data from healthy and periodontitis-affected gingiva. Our analyses identified 41 consensus master regulator genes (MRs), the regulons of which comprised between 25 and 833 genes. Regulons of 7 MRs ( HCLS1, ZNF823, XBP1, ZNF750, RORA, TFAP2C, and ZNF57) included 〉 500 genes each. Gene set enrichment analysis indicated differential expression of these regulons in gingival health versus disease with a type 1 error between 2% and 0.5% and with 〉 80% of the regulon genes in the leading edge. Ingenuity pathway analysis showed significant enrichment of 36 regulons for several pathways, while 6 regulons (those of MRs HCLS1, IKZF3, ETS1, NHLH2, POU2F2, and VAV1) were enriched for 〉 10 pathways. Pathways related to immune system signaling and development were the ones most frequently enriched across all regulons. The unbiased analysis of genome-wide regulatory networks can enhance our understanding of the pathobiology of human periodontitis and, after appropriate validation, ultimately identify target molecules of diagnostic, prognostic, or therapeutic value.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2016
    detail.hit.zdb_id: 2057074-0
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  • 10
    Online Resource
    Online Resource
    SAGE Publications ; 2015
    In:  Journal of Dental Research Vol. 94, No. 9_suppl ( 2015-09), p. 201S-211S
    In: Journal of Dental Research, SAGE Publications, Vol. 94, No. 9_suppl ( 2015-09), p. 201S-211S
    Abstract: Periodontitis and type 2 diabetes mellitus are known to be associated. The relationship between periodontal microbiota and early diabetes risk has not been studied. We investigated the association between periodontal bacteria and prediabetes prevalence among diabetes-free adults. ORIGINS (the Oral Infections, Glucose Intolerance and Insulin Resistance Study) cross sectionally enrolled 300 diabetes-free adults aged 20 to 55 y (mean ± SD, 34 ± 10 y; 77% female). Prediabetes was defined as follows: 1) hemoglobin A1c values ranging from 5.7% to 6.4% or 2) fasting plasma glucose ranging from 100 to 125 mg/dL. In 1,188 subgingival plaque samples, 11 bacterial species were assessed at baseline, including Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Actinomyces naeslundii. Full-mouth clinical periodontal examinations were performed, and participants were defined as having no/mild periodontitis vs. moderate/severe periodontitis per the definition of the Centers for Disease Control and Prevention / American Academy of Periodontology. Modified Poisson regression evaluated prediabetes prevalence across bacterial tertiles. Prevalence ratios and 95% confidence intervals for third vs. first tertiles are presented. All analyses were adjusted for cardiometabolic risk factors. All results presented currently arise from the baseline cross section. Prediabetes prevalence was 18%, and 58% of participants had moderate/severe periodontitis. Prevalence ratios (95% confidence intervals) summarizing associations between bacterial levels and prediabetes were as follows: A. actinomycetemcomitans, 2.48 (1.34, 4.58), P = 0.004; P. gingivalis, 3.41 (1.78, 6.58), P = 0.0003; T. denticola, 1.99 (0.992, 4.00), P = 0.052; T. forsythia, 1.95 (1.0, 3.84), P = 0.05; A. naeslundii, 0.46 (0.25, 0.85), P = 0.01. The prevalence ratio for prediabetes among participants with moderate/severe vs. no/mild periodontitis was 1.47 (0.78, 2.74), P = 0.23. Higher colonization levels of specific periodontal microbiota are associated with higher prediabetes prevalence among diabetes-free adults.
    Type of Medium: Online Resource
    ISSN: 0022-0345 , 1544-0591
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
    detail.hit.zdb_id: 2057074-0
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