Caries risk assessment in school children using a reduced Cariogram model without saliva tests

<p>Abstract</p> <p>Background</p> <p>To investigate the caries predictive ability of a reduced Cariogram model without salivary tests in schoolchildren.</p> <p>Methods</p> <p>The study group consisted of 392 school children, 10-11 years of age, who volunteered after informed consent. A caries risk assessment was made at baseline with aid of the computer-based Cariogram model and expressed as "the chance of avoiding caries" and the children were divided into five risk groups. The caries increment (ΔDMFS) was extracted from the dental records and bitewing radiographs after 2 years. The reduced Cariogram was processed by omitting the variables "salivary mutans streptococci", "secretion rate" and "buffer capacity" one by one and finally all three. Differences between the total and reduced models were expressed as area under the ROC-curve.</p> <p>Results</p> <p>The baseline caries prevalence in the study population was 40% (mean DMFS 0.87 ± 1.35) and the mean 2-year caries increment was 0.51 ± 1.06. Both Cariogram models displayed a statistically relationship with caries development (p < 0.05); more caries was found among those assessed with high risk compared to those with low risk. The combined sensitivity and specificity decreased after exclusion of the salivary tests and a statistically significant reduction of the area under the ROC-curve was displayed compared with the total Cariogram (p < 0.05). Among the salivary variables, omission of the mutans streptococci enumeration impaired the predictive ability the most.</p> <p>Conclusions</p> <p>The accuracy of caries prediction in school children was significantly impaired when the Cariogram model was applied without enumeration of salivary tests.</p

A surrogate method for comparison analysis of salivary concentrations of Xylitol-containing products

Background: Xylitol chewing gum has been shown to reduce Streptococcus mutans levels and decay. Two studies examined the presence and time course of salivary xylitol concentrations delivered via xylitol-containing pellet gum and compared them to other xylitol-containing products. Methods: A within-subjects design was used for both studies. Study 1, adults (N = 15) received three xylitol-containing products (pellet gum (2.6 g), gummy bears (2.6 g), and commercially available stick gum (Koolerz, 3.0 g)); Study 2, a second group of adults (N = 15) received three xylitol-containing products (pellet gum, gummy bears, and a 33% xylitol syrup (2.67 g). For both studies subjects consumed one xylitol product per visit with a 7-day washout between each product. A standardized protocol was followed for each product visit. Product order was randomly determined at the initial visit. Saliva samples (0.5 mL to 1.0 mL) were collected at baseline and up to 10 time points (~16 min in length) after product consumption initiated. Concentration of xylitol in saliva samples was analyzed using high-performance liquid chromatography. Area under the curve (AUC) for determining the average xylitol concentration in saliva over the total sampling period was calculated for each product. Results: In both studies all three xylitol products (Study 1: pellet gum, gummy bears, and stick gum; Study 2: pellet gum, gummy bears, and syrup) had similar time curves with two xylitol concentration peaks during the sampling period. Study 1 had its highest mean peaks at the 4 min sampling point while Study 2 had its highest mean peaks between 13 to 16 minutes. Salivary xylitol levels returned to baseline at about 18 minutes for all forms tested. Additionally, for both studies the total AUC for the xylitol products were similar compared to the pellet gum (Study 1: pellet gum - 51.3 [micro]g.min/mL, gummy bears - 59.6 [micro]g.min/mL, and stick gum - 46.4 [micro]g.min/mL; Study 2: pellet gum - 63.0 [micro]g.min/mL, gummy bears - 55.9 [micro]g.min/mL, and syrup - 59.0 [micro]g.min/mL). Conclusion: The comparison method demonstrated high reliability and validity. In both studies other xylitol-containing products had time curves and mean xylitol concentration peaks similar to xylitol pellet gum suggesting this test may be a surrogate for longer studies comparing various products.NIDCR-NIH U54 DE14254; Head Start, HRSA 90YD0188/03; and MCHB, HRSA R40MC03622-03

The oral microbiome – an update for oral healthcare professionals

For millions of years, our resident microbes have coevolved and coexisted with us in a mostly harmonious symbiotic relationship. We are not distinct entities from our microbiome, but together we form a 'superorganism' or holobiont, with the microbiome playing a significant role in our physiology and health. The mouth houses the second most diverse microbial community in the body, harbouring over 700 species of bacteria that colonise the hard surfaces of teeth and the soft tissues of the oral mucosa. Through recent advances in technology, we have started to unravel the complexities of the oral microbiome and gained new insights into its role during both health and disease. Perturbations of the oral microbiome through modern-day lifestyles can have detrimental consequences for our general and oral health. In dysbiosis, the finely-tuned equilibrium of the oral ecosystem is disrupted, allowing disease-promoting bacteria to manifest and cause conditions such as caries, gingivitis and periodontitis. For practitioners and patients alike, promoting a balanced microbiome is therefore important to effectively maintain or restore oral health. This article aims to give an update on our current knowledge of the oral microbiome in health and disease and to discuss implications for modern-day oral healthcare

Heritability of Caries Scores, Trajectories, and Disease Subtypes

Previous studies report that dental caries is partially heritable, but there is uncertainty in the magnitude of genetic effects and little understanding of how genetic factors might influence caries progression or caries subtypes. This study aimed to estimate the relative importance of genetic and environmental factors in the etiology of different caries outcomes using a twin-based design. Analysis included up to 41,678 twins in the Swedish Twin Register aged 7 to 97 y, and dental data were obtained from preexisting dental records. The outcome measures were 1) summary indices of caries experience, 2) parameters representing trajectory in caries progression derived from longitudinal modeling, and 3) caries scores in groups of biologically similar tooth surfaces derived from hierarchical clustering of tooth surfaces (termed caries clusters). Additive genetic factors explained between 49.1% and 62.7% of variation in caries scores and between 50.0% and 60.5% of variation in caries trajectories. Seven caries clusters were identified, which had estimates of heritability lying between 41.9% and 54.3%. Shared environmental factors were important for only some of these clusters and explained 16% of variation in fissure caries in molar teeth but little variation in other clusters of caries presentation. The genetic factors influencing these clusters were only partially overlapping, suggesting that different biological processes are important in different groups of tooth surfaces and that innate liability to some patterns of caries presentation may partially explain why groups of tooth surfaces form clusters within the mouth. These results provide 1) improved quantification of genetic factors in the etiology of caries and 2) new data about the role of genetics in terms of longitudinal changes in caries status and specific patterns of disease presentation, and they may help lay the foundations for personalized interventions in the future