Periodontal conditions, oral Candida albicans and salivary proteins in type 2 diabetic subjects with emphasis on gender

<p>Abstract</p> <p>Background</p> <p>The association between periodontal conditions, oral yeast colonisation and salivary proteins in subjects with type 2 diabetes (T2D) is not yet documented. The present study aimed to assess the relationship between these variables in type 2 diabetic subjects with reference to gender.</p> <p>Methods</p> <p>Fifty-eight type 2 diabetic subjects (23 males and 35 females) with random blood glucose level ≥ 11.1 mmol/L were investigated. Periodontal conditions (plaque index [PI], bleeding on probing [BOP], probing pocket depth [PD] (4 to 6 mm and ≥ 6 mm), oral yeasts, salivary immunoglobulin (Ig) A, IgG and total protein concentrations, and number of present teeth were determined.</p> <p>Results</p> <p>Periodontal conditions (PI [<it>p </it>< 0.00001], BOP [<it>p </it>< 0.01] and PD of 4 to 6 mm [<it>p </it>< 0.001], salivary IgG (μg)/mg protein (<it>p </it>< 0.001) and salivary total protein concentrations (<it>p </it>< 0.05) were higher in type 2 diabetic females with <it>Candida albicans </it>(<it>C. albicans</it>) colonisation compared to males in the same group. Type 2 diabetic females with <it>C. albicans </it>colonisation had more teeth compared to males in the same group (<it>p </it>< 0.0001).</p> <p>Conclusion</p> <p>Clinical and salivary parameters of periodontal inflammation (BOP and IgG (μg)/mg protein) were higher in type 2 diabetic females with oral <it>C. albicans </it>colonisation compared to males in the same group. Further studies are warranted to evaluate the association of gender with these variables in subjects with T2D.</p

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Novel and established anthropometric measures and the prediction of incident cardiovascular disease: a cohort study

OBJECTIVES: The aim of this study was to compare novel and established anthropometrical measures in their ability to predict cardiovascular disease (CVD), and to determine whether they improve risk prediction beyond classical risk factors in a cohort study of 60-year-old men and women. We also stratified the results according to gender to identify possible differences between men and women. Furthermore, we aimed to replicate our findings in a large independent cohort (The Malmo Diet and Cancer study-cardiovascular cohort). METHODS: This was a population-based study of 1751 men and 1990 women, aged 60 years and without CVD at baseline, with 375 incident cases of CVD during 11 years of follow-up. Weight, height, waist circumference (WC), hip circumference and sagittal abdominal diameter (SAD) were measured at baseline. Body mass index (BMI), waist-hip ratio (WHR), waist-hip-height ratio (WHHR), WC-to-height ratio (WCHR) and SAD-to-height ratio (SADHR) were calculated. RESULTS: All anthropometric measures predicted CVD in unadjusted Cox regression models per s.d. increment (hazard ratios, 95% confidence interval), while significant associations after adjustments for established risk CVD factors were noted for WHHR 1.20 (1.08-1.33), WHR 1.14 (1.02-1.28), SAD 1.13 (1.02-1.25) and SADHR 1.17 (1.06-1.28). WHHR had higher increases in C-statistics, and model improvements (likelihood ratio tests (P<0.001)). In the replication study (MDC-CC, n = 5180), WHHR was the only measure that improved Cox regression models in men (P = 0.01). CONCLUSION: WHHR, a new measure reflecting body fat distribution, showed the highest risk estimates after adjustments for established CVD risk factors. These findings were verified in men but not women in an independent cohort

Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks

Graph centralities are commonly used to identify and prioritize disease genes in transcriptional regulatory networks. Studies on small networks of experimentally validated protein-protein interactions underpin the general validity of this approach and extensions of such findings have recently been proposed for networks inferred from gene expression data. However, it is largely unknown how well gene centralities are preserved between the underlying biological interactions and the networks inferred from gene expression data. Specifically, while previous studies have evaluated the performance of inference methods on synthetic gene expression, it has not been established how the choice of inference method affects individual centralities in the network. Here, we compare two gene centrality measures between reference networks and networks inferred from corresponding simulated gene expression data, using a number of commonly used network inference methods. The results indicate that the centrality of genes is only moderately conserved for all of the inference methods used. In conclusion, caution should be exercised when inspecting centralities in reverse-engineered networks and further work will be required to establish the use of such networks for prioritizing disease genes