Serum zinc and periodontitis in non-diabetic smoking and non-smoking adults: NHANES 2011-2014.

OBJECTIVE: This study aimed to evaluate the association of serum zinc with periodontitis in non-diabetics based on smoking status, using a representative sample of adults in the United States. METHODS: A total of 1051 participants who underwent full-mouth periodontal examination and serum zinc testing were enrolled from NHANES 2011 to 2014. The covariate-adjusted association of serum zinc concentrations with periodontitis was explored using multivariable logistic regression, restricted cubic spines, and sensitivity analysis. RESULTS: The mean age of the 1051 adults was 54.5 years, 59.37% were male, and 20.65% had periodontitis. Analysis of the results showed that serum zinc was associated with periodontitis. The overall adjusted odds of periodontitis were 9% (odds ratio [OR]: 0.91; 95% confidence interval [CI]: 0.83-1.00) and 14% (OR: 0.86; 95% CI: 0.75-0.98) for nonsmokers and smokers, respectively. Smokers with T3 serum zinc exhibited a 53% reduction in the fully adjusted odds of periodontitis (OR: 0.47; 95% CI: 0.23-0.96), when compared to the reference group (T1 serum zinc), with serum zinc as the categorical variable. CONCLUSIONS: Serum zinc levels were associated with the risk of periodontitis in non-diabetic smokers but not non-smokers.

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human.

DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.