Effect of smoking on subgingival microflora of patients with periodontitis in Japan

<p>Abstract</p> <p>Background</p> <p>Smoking is a risk factor for periodontitis. To clarify the contribution of smoking to periodontitis, it is essential to assess the relationship between smoking and the subgingival microflora. The aim of this study was to gain an insight into the influence of smoking on the microflora of Japanese patients with periodontitis.</p> <p>Methods</p> <p>Sixty-seven Japanese patients with chronic periodontitis (19 to 83 years old, 23 women and 44 men) were enrolled in the present study. They consisted of 30 smokers and 37 non-smokers. Periodontal parameters including probing pocket depth (PPD) and bleeding on probing (BOP) and oral hygiene status were recorded. Detection of <it>Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Prevotella intermedia, Tannerella forsythia, Fusobacterium nucleatum/periodonticum, Treponema denticola </it>and <it>Campylobacter rectus </it>in subgingival plaque samples was performed by polymerase chain reaction. Association between the detection of periodontopathic bacteria and smoking status was analyzed by multiple logistic regression analysis and chi-square test.</p> <p>Results</p> <p>A statistically significant association was found between having a PPD ≥ 4 mm and detection of <it>T. denticola, P. intermedia, T. forsythia</it>, or <it>C. rectus</it>, with odds ratios ranging from 2.17 to 3.54. A significant association was noted between BOP and the detection of <it>C. rectus </it>or <it>P. intermedia</it>, and smoking, with odds ratios ranging from 1.99 to 5.62. Prevalence of <it>C. rectus </it>was higher in smokers than non-smokers, whereas that of <it>A. actinomycetemcomitans </it>was lower in smokers.</p> <p>Conclusions</p> <p>Within limits, the analysis of the subgingival microbial flora in smokers and non-smokers with chronic periodontitis suggests a relevant association between smoking and colonization by the specific periodontal pathogens including <it>C. rectus</it>.</p

Melatonin expression in periodontal disease

It was the purpose of this study to examine the relationship between periodontal diseases and melatonin level. Material and Methods:  Forty-six patients with periodontal disease, together with 26 age- and gender-matched healthy controls, were included. Periodontal status was assessed using the Community Periodontal Index. Plasma and salivary melatonin levels were determined using specific commercial radioimmunoassays, whereas lymphocyte subpopulations (e.g. CD3, CD4, CD8, C19 and natural killer cells) were analyzed using flow cytometry. Results:  Patients with periodontal disease had significantly ( p <  0.001) lower plasma (9.46 ± 3.18 pg/mL) and saliva (2.55 ± 0.99 pg/mL) melatonin levels than healthy control patients (14.33 ± 4.05 and 4.22 ± 0.87 pg/mL, respectively). A biphasic relationhip was observed between plasma melatonin levels and Community Periodontal Indices. The plasma melatonin level was reduced in patients with a lower Community Periodontal Index value (1 or 2) and increased in patients with a higher Community Periodontal Index value (3 or 4). Salivary melatonin parallels the changes of plasma melatonin. The higher the Community Periodontal Index, the older the patient and the higher the total lymphocyte counts. CD4 concentrations also increased as the disease worsened. Conclusion:  The results obtained from this study suggest that melatonin could act as a protective function in fighting periodontal infection. However, further studies in this area are encouraged.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65967/1/j.1600-0765.2007.00978.x.pd

The identification of bacteria associated with periodontal disease and dental caries by enzymatic methods

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73856/1/j.1399-302X.1986.tb00322.x.pd

Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends

Google Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; however ILI does not necessarily correlate with actual influenza virus infections.Influenza activity data from 2003-04 through 2007-08 were obtained from three US surveillance systems: Google Flu Trends, CDC Outpatient ILI Surveillance Network (CDC ILI Surveillance), and US Influenza Virologic Surveillance System (CDC Virus Surveillance). Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data. An analysis was performed to investigate outlier observations and determine the extent to which they affected the correlations between surveillance data. Pearson's correlation coefficient describing Google Flu Trends and CDC Virus Surveillance over the study period was 0.72 (95% CI: 0.64, 0.79). The correlation between CDC ILI Surveillance and CDC Virus Surveillance over the same period was 0.85 (95% CI: 0.81, 0.89). Most of the outlier observations in both comparisons were from the 2003-04 influenza season. Exclusion of the outlier observations did not substantially improve the correlation between Google Flu Trends and CDC Virus Surveillance (0.82; 95% CI: 0.76, 0.87) or CDC ILI Surveillance and CDC Virus Surveillance (0.86; 95%CI: 0.82, 0.90).This analysis demonstrates that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza. Most of the outlier observations occurred during the 2003-04 influenza season that was characterized by early and intense influenza activity, which potentially altered health care seeking behavior, physician testing practices, and internet search behavior

Characterization of the L-Lactate Dehydrogenase from Aggregatibacter actinomycetemcomitans

Aggregatibacter actinomycetemcomitans is a Gram-negative opportunistic pathogen and the proposed causative agent of localized aggressive periodontitis. A. actinomycetemcomitans is found exclusively in the mammalian oral cavity in the space between the gums and the teeth known as the gingival crevice. Many bacterial species reside in this environment where competition for carbon is high. A. actinomycetemcomitans utilizes a unique carbon resource partitioning system whereby the presence of L-lactate inhibits uptake of glucose, thus allowing preferential catabolism of L-lactate. Although the mechanism for this process is not fully elucidated, we previously demonstrated that high levels of intracellular pyruvate are critical for L-lactate preference. As the first step in L-lactate catabolism is conversion of L-lactate to pyruvate by lactate dehydrogenase, we proposed a model in which the A. actinomycetemcomitans L-lactate dehydrogenase, unlike homologous enzymes, is not feedback inhibited by pyruvate. This lack of feedback inhibition allows intracellular pyruvate to rise to levels sufficient to inhibit glucose uptake in other bacteria. In the present study, the A. actinomycetemcomitans L-lactate dehydrogenase was purified and shown to convert L-lactate, but not D-lactate, to pyruvate with a Km of approximately 150 µM. Inhibition studies reveal that pyruvate is a poor inhibitor of L-lactate dehydrogenase activity, providing mechanistic insight into L-lactate preference in A. actinomycetemcomitans

Genetic aspects of dental disorders

The document attached has been archived with permission from the Australian Dental Association. An external link to the publisher’s copy is included.This paper reviews past and present applications of quantitative and molecular genetics to dental disorders. Examples are given relating to craniofacial development (including malocclusion), oral supporting tissues (including periodontal diseases) and dental hard tissues (including defects of enamel and dentine as well as dental caries). Future developments and applications to clinical dentistry are discussed. Early investigations confirmed genetic bases to dental caries, periodontal diseases and malocclusion, but research findings have had little impact on clinical practice. The complex multifactorial aetiologies of these conditions, together with methodological problems, have limited progress until recently. Present studies are clarifying previously unrecognized genetic and phenotypic heterogeneities and attempting to unravel the complex interactions between genes and environment by applying new statistical modelling approaches to twin and family data. linkage studies using highly polymorphic DNA markers are providing a means of locating candidate genes, including quantitative trait loci (QTL). In future, as knowledge increases: it should be possible to implement preventive strategies for those genetically-predisposed individuals who are identified-predisposed individuals who are identified to be at risk.Grant C. Townsend, Michael J. Aldred and P. Mark Bartol

Use of 16S ribosomal RNA gene analyses to characterize the bacterial signature associated with poor oral health in West Virginia

<p>Abstract</p> <p>Background</p> <p>West Virginia has the worst oral health in the United States, but the reasons for this are unclear. This pilot study explored the etiology of this disparity using culture-independent analyses to identify bacterial species associated with oral disease.</p> <p>Methods</p> <p>Bacteria in subgingival plaque samples from twelve participants in two independent West Virginia dental-related studies were characterized using 16S rRNA gene sequencing and Human Oral Microbe Identification Microarray (HOMIM) analysis. Unifrac analysis was used to characterize phylogenetic differences between bacterial communities obtained from plaque of participants with low or high oral disease, which was further evaluated using clustering and Principal Coordinate Analysis.</p> <p>Results</p> <p>Statistically different bacterial signatures (<it>P </it>< 0.001) were identified in subgingival plaque of individuals with low or high oral disease in West Virginia based on 16S rRNA gene sequencing. Low disease contained a high frequency of <it>Veillonella </it>and <it>Streptococcus</it>, with a moderate number of <it>Capnocytophaga</it>. High disease exhibited substantially increased bacterial diversity and included a large proportion of Clostridiales cluster bacteria (<it>Selenomonas</it>, <it>Eubacterium, Dialister</it>). Phylogenetic trees constructed using 16S rRNA gene sequencing revealed that Clostridiales were repeated colonizers in plaque associated with high oral disease, providing evidence that the oral environment is somehow influencing the bacterial signature linked to disease.</p> <p>Conclusions</p> <p>Culture-independent analyses identified an atypical bacterial signature associated with high oral disease in West Virginians and provided evidence that the oral environment influenced this signature. Both findings provide insight into the etiology of the oral disparity in West Virginia.</p

A comparison of pharmacoepidemiological study designs in medication use and traffic safety research

In order to explore how the choice of different study designs could influence the risk estimates, a case–crossover and case–time–control study were carried out and their outcomes were compared with those of a traditional case–control study design that evaluated the association between the exposure to psychotropic medications and the risk of having a motor vehicle accident (MVA). A record-linkage database availing data for 3,786 cases and 18,089 controls during the period 2000–2007 was used. The study designs (i.e., case–crossover and case–time–control) were derived from published literature, and the following psychotropic medicines were examined: antipsychotics, anxiolytics, hypnotics and sedatives, and antidepressants, stratified in the two groups selective serotonin reuptake inhibitors (SSRIs) and other antidepressants. Moreover, in order to further investigate the effects of frequency of psychoactive medication exposure on the outcomes of the case–crossover analysis, the data were also stratified by the number of defined daily doses (DDDs) and days of medication use in the 12 months before the motor vehicle accident. Three-thousand seven-hundred fifty-two cases were included in this second part of the case–crossover analysis. The case–crossover design did not show any statistically significant association between psychotropic medication exposure and MVA risk [e.g., SSRIs—Adj. OR = 1.00 (95 % CI: 0.69–1.46); Anxiolytics—Adj. OR = 0.95 (95 % CI: 0.68–1.31)]. The case–time–control design only showed a borderline statistically significant increased traffic accident risk in SSRI users [Adj. OR = 1.16 (95 % CI: 1.01–1.34)]. With respect to the stratifications by the number of DDDs and days of medication use, the analyses showed no increased traffic accident risk associated with the exposure to the selected medication groups [e.g., SSRIs, <20 DDDs—Adj. OR = 0.65 (95 % CI: 0.11–3.87); SSRIs, 16–150 days—Adj. OR = 0.55 (95 % CI: 0.24–1.24)]. In contrast to the above-mentioned results, our recent case–control study found a statistically significant association between traffic accident risk and exposure to anxiolytics [Adj. OR = 1.54 (95 % CI: 1.11–2.15)], and SSRIs [Adj. OR = 2.03 (95 % CI: 1.31–3.14)]. Case–crossover and case–time–control analyses produced different results than those of our recent case–control study (i.e., case–crossover and case–time–control analyses did not show any statistically significant association whereas the case–control analysis showed an increased traffic accident risk in anxiolytic and SSRI users). These divergent results can probably be explained by the differences in the study designs. Given that the case–crossover design is only appropriate for short-term exposures and the case–time–control design is an elaboration of this latter, it can be concluded that, probably, these two approaches are not the most suitable ones to investigate the relation between MVA risk and psychotropic medications, which, on the contrary, are often use chronically