Correlation between measures of insulin resistance in fasting and non-fasting blood

<p>Abstract</p> <p>Background</p> <p>Epidemiological investigation of insulin resistance is difficult. Standard measures of insulin resistance require invasive investigations, which are impractical for large-scale studies. Surrogate measures using fasting blood samples have been developed, but even these are difficult to obtain in population-based studies. Measures of insulin resistance have not been validated in non-fasting blood samples. Our objective was to assess the correlations between fasting and non-fasting measures of insulin resistance/sensitivity.</p> <p>Methods</p> <p>Fasting and non-fasting measurements of metabolic function were compared in 30 volunteers (15 male) aged 28 to 48 years. Participants provided a morning blood sample after an overnight fast and a second sample approximately 4 hours after lunch on the same day.</p> <p>Results</p> <p>Non-fasting levels of the adipokines leptin, adiponectin, and leptin:adiponectin ratios were not significantly different and highly correlated with fasting values (r values 0.95, 0.96, and 0.95 respectively, P values < 0.001). There were moderate correlations between fasting and non-fasting estimates of insulin sensitivity using the McAuley (r = 0.60, P = 0.001) and QUICKI formulae (r = 0.39, P = 0.037). The HOMA-IR estimate of insulin resistance was also moderately correlated (r = 0.45, P = 0.016).</p> <p>Conclusions</p> <p>Semi-fasting measures of leptin, adiponectin, and leptin:adiponectin ratios correlate closely with fasting values and are likely to be sufficient for population-based research. Other measures of insulin resistance or sensitivity in semi-fasted blood samples are moderately correlated with values obtained after an overnight fast. These estimates of insulin resistance/sensitivity may also be adequate for many epidemiological studies and would avoid the difficulties of obtaining fasting blood samples.</p

Transcriptome Profiling of Bovine Milk Oligosaccharide Metabolism Genes Using RNA-Sequencing

This study examines the genes coding for enzymes involved in bovine milk oligosaccharide metabolism by comparing the oligosaccharide profiles with the expressions of glycosylation-related genes. Fresh milk samples (n = 32) were collected from four Holstein and Jersey cows at days 1, 15, 90 and 250 of lactation and free milk oligosaccharide profiles were analyzed. RNA was extracted from milk somatic cells at days 15 and 250 of lactation (n = 12) and gene expression analysis was conducted by RNA-Sequencing. A list was created of 121 glycosylation-related genes involved in oligosaccharide metabolism pathways in bovine by analyzing the oligosaccharide profiles and performing an extensive literature search. No significant differences were observed in either oligosaccharide profiles or expressions of glycosylation-related genes between Holstein and Jersey cows. The highest concentrations of free oligosaccharides were observed in the colostrum samples and a sharp decrease was observed in the concentration of free oligosaccharides on day 15, followed by progressive decrease on days 90 and 250. Ninety-two glycosylation-related genes were expressed in milk somatic cells. Most of these genes exhibited higher expression in day 250 samples indicating increases in net glycosylation-related metabolism in spite of decreases in free milk oligosaccharides in late lactation milk. Even though fucosylated free oligosaccharides were not identified, gene expression indicated the likely presence of fucosylated oligosaccharides in bovine milk. Fucosidase genes were expressed in milk and a possible explanation for not detecting fucosylated free oligosaccharides is the degradation of large fucosylated free oligosaccharides by the fucosidases. Detailed characterization of enzymes encoded by the 92 glycosylation-related genes identified in this study will provide the basic knowledge for metabolic network analysis of oligosaccharides in mammalian milk. These candidate genes will guide the design of a targeted breeding strategy to optimize the content of beneficial oligosaccharides in bovine milk

The Influence of Social-Cognitive Factors on Personal Hygiene Practices to Protect Against Influenzas: Using Modelling to Compare Avian A/H5N1 and 2009 Pandemic A/H1N1 Influenzas in Hong Kong

# The Author(s) 2010. This article is published with open access at Springerlink.com Background Understanding population responses to influenza helps optimize public health interventions. Relevant theoretical frameworks remain nascent. Purpose To model associations between trust in information, perceived hygiene effectiveness, knowledge about the causes of influenza, perceived susceptibility and worry, and personal hygiene practices (PHPs) associated with influenza. Methods Cross-sectional household telephone surveys on avian influenza A/H5N1 (2006) and pandemic influenza A/ H1N1 (2009) gathered comparable data on trust in formal and informal sources of influenza information, influenzarelated knowledge, perceived hygiene effectiveness, worry, perceived susceptibility, and PHPs. Exploratory factor analysis confirmed domain content while confirmatory factor analysis was used to evaluate the extracted factors. The hypothesized model, compiled from different theoretical frameworks, was optimized with structural equation modelling using the A/H5N1 data. The optimized model was then tested against the A/H1N1 dataset. Results The model was robust across datasets though corresponding path weights differed. Trust in formal information was positively associated with perceived hygien

Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium

BACKGROUND Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC. METHODS Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals). RESULTS Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation. CONCLUSION This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease ris