Metabolomic markers reveal novel pathways of ageing and early development in human populations

BACKGROUND Human ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age. METHODS Metabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels. RESULTS We identified a panel of 22 metabolites which combined are strongly correlated with age (R(2) = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10(-157)) and lung function (FEV1 beta = -0.04, SE = 0.008, P = 1.8 × 10(-8) adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = -0.01, SE = 0.002, P = 1.9 × 10(-6)) and birthweight (beta = -0.06, SE = 0.01, P = 2.5 × 10(-9)). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10(-6)). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = -0.20, SE = 0.04, P = 2.9 × 10(-8)). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes. CONCLUSIONS Our data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing

İnce iğne aspirasyon sistolojisi'nin iki değişkenli rasgele etki modeline göre meta-analizi'nin özet işlem karakteristiği eğrisi ve hiyerarşik özet işlem karakteristiği eğrisi.

In this study, meta-analysis of diagnostic tests, Summary Receiver Operating Characteristic (SROC) curve, bivariate random effects and Hierarchical Summary Receiver Operating Characteristic (HSROC) curve theories have been discussed and accuracy in literature of Fine Needle Aspiration (FNA) biopsy that is used in the diagnosis of masses in breast cancer (malignant or benign) has been analyzed. FNA Cytological (FNAC) examination in breast tumor is, easy, effective, effortless, and does not require special training for clinicians. Because of the uncertainty related to FNAC‘s accurate usage in publications, 25 FNAC studies have been gathered in the meta-analysis. In the plotting of the summary ROC curve, the logit difference and sums of the true positive rates and the false positive rates included in the meta-analysis‘s codes have been generated by SAS. The formula of the bivariate random effects model and hierarchical summary ROC curve is presented in context with the literature. Then bivariate random effects implementation with the new SAS PROC GLIMMIX is generated. Moreover, HSROC implementation is generated by SAS PROC HSROC NLMIXED. Curves are plotted with RevMan Version 5 (2008). It has been stated that the meta-analytic results of bivariate random effects are nearly identical to the results from the HSROC approach. The results achieved through both random effects meta-analytic methods prove that FNA Cytology is a diagnostic test with a high level of distinguish over breast tumor.M.S. - Master of Scienc

Glycosylation of immunoglobulin g:role of genetic and epigenetic influences

Objective To determine the extent to which genetic and epigenetic factors contribute to variations in glycosylation of immunoglobulin G (IgG) in humans. Methods 76 N-glycan traits in circulating IgG were analyzed by UPLC in 220 monozygotic and 310 dizygotic twin pairs from TwinsUK. A classical twin study design was used to derive the additive genetic, common and unique environmental components defining the variance in these traits. Epigenome-wide association analysis was performed using the Illumina 27k chip. Results 51 of the 76 glycan traits studied have an additive genetic component (heritability, h2)≥ 0.5. In contrast, 12 glycan traits had a low genetic contribution (h2<0.35). We then tested for association between methylation levels and glycan levels (P<2 x10-6). Among glycan traits with low heritability probe cg08392591 maps to a CpG island 5’ from the ANKRD11 gene, a p53 activator on chromosome 16. Probe cg26991199 maps to the SRSF10 gene involved in regulation of RNA splicing and particularly in regulation of splicing of mRNA precursors upon heat shock. Among those with high heritability we found cg13782134 (mapping to the NRN1L gene) and cg16029957 mapping near the QPCT gene to be array-wide significant. The proportion of array-wide epigenetic associations was significantly larger (P<0.005) among glycans with low heritability (42%) than in those with high heritability (6.2%). Conclusions Glycome analyses might provide a useful integration of genetic and non-genetic factors to further our understanding of the role of glycosylation in both normal physiology and disease

Long term conservation of human metabolic phenotypes and link to heritability

Changes in an individual&#39;s human metabolic phenotype (metabotype) over time can be indicative of disorder-related modifications. Studies covering several months to a few years have shown that metabolic profiles are often specific for an individual. This &quot;metabolic individuality&quot; and detected changes may contribute to personalized approaches in human health care. However, it is not clear whether such individual metabotypes persist over longer time periods. Here we investigate the conservation of metabotypes characterized by 212 different metabolites of 818 participants from the Cooperative Health Research in the Region of Augsburg; Germany population, taken within a 7-year time interval. For replication, we used paired samples from 83 non-related individuals from the TwinsUK study. Results indicated that over 40&nbsp;% of all study participants could be uniquely identified after 7&nbsp;years based on their metabolic profiles alone. Moreover, 95&nbsp;% of the study participants showed a high degree of metabotype conservation (&gt;70&nbsp;%) whereas the remaining 5&nbsp;% displayed major changes in their metabolic profiles over time. These latter individuals were likely to have undergone important biochemical changes between the two time points. We further show that metabolite conservation was positively associated with heritability (rank correlation 0.74), although there were some notable exceptions. Our results suggest that monitoring changes in metabotypes over several years can trace changes in health status and may provide indications for disease onset. Moreover, our study findings provide a general reference for metabotype conservation over longer time periods that can be used in biomarker discovery studies

An atlas of genetic influences on human blood metabolites

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease