Factors related with adiponectinemia in obese and normal-weight women and with its variation in weight loss programs

Objective: To assess different factors influencing adiponectinemia in obese and normal-weight women; to identify factors associated with the variation (Δ) in adiponectinemia in obese women following a 6-month weight loss program, according to surgical/non-surgical interventions. Methods: We studied 100 normal-weight women and 112 obese premenopausal women; none of them was on any medical treatment. Women were characterized for anthropometrics, daily macronutrient intake, smoking status, contraceptives use, adiponectin as well as IL-6 and TNF-α serum concentrations. Results: Adiponectinemia was lower in obese women (p < 0.001), revealing an inverse association with waist-to-hip ratio (p < 0.001; r = –0.335). Normal-weight women presented lower adiponectinemia among smokers (p = 0.041); body fat, waist-to-hip ratio, TNF-α levels, carbohydrate intake, and smoking all influence adiponectinemia (r 2 = 0.436). After weight loss interventions, a significant modification in macronutrient intake occurs followed by anthropometrics decrease (chiefly after bariatric procedures) and adiponectinemia increase (similar after surgical and non-surgical interventions). After bariatric intervention, Δ adiponectinemia was inversely correlated to Δ waist circumference and Δ carbohydrate intake (r 2 = 0.706). Conclusion: Anthropometrics, diet, smoking, and TNF-α levels all influence adiponectinemia in normal-weight women, although explaining less than 50% of it. In obese women, anthropometrics modestly explain adiponectinemia. Opposite to non-surgical interventions, after bariatric surgery adiponectinemia increase is largely explained by diet composition and anthropometric changes.

Arrow Plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on samples subgroups

Background: A common task in analyzing microarray data is to determine which genes are differentially expressed across two (or more) kind of tissue samples or samples submitted under experimental conditions. Several statistical methods have been proposed to accomplish this goal, generally based on measures of distance between classes. It is well known that biological samples are heterogeneous because of factors such as molecular subtypes or genetic background that are often unknown to the experimenter. For instance, in experiments which involve molecular classification of tumors it is important to identify significant subtypes of cancer. Bimodal or multimodal distributions often reflect the presence of subsamples mixtures. Consequently, there can be genes differentially expressed on sample subgroups which are missed if usual statistical approaches are used. In this paper we propose a new graphical tool which not only identifies genes with up and down regulations, but also genes with differential expression in different subclasses, that are usually missed if current statistical methods are used. This tool is based on two measures of distance between samples, namely the overlapping coefficient (OVL) between two densities and the area under the receiver operating characteristic (ROC) curve. The methodology proposed here was implemented in the open-source R software. Results: This method was applied to a publicly available dataset, as well as to a simulated dataset. We compared our results with the ones obtained using some of the standard methods for detecting differentially expressed genes, namely Welch t-statistic, fold change (FC), rank products (RP), average difference (AD), weighted average difference (WAD), moderated t-statistic (modT), intensity-based moderated t-statistic (ibmT), significance analysis of microarrays (samT) and area under the ROC curve (AUC). On both datasets all differentially expressed genes with bimodal or multimodal distributions were not selected by all standard selection procedures. We also compared our results with (i) area between ROC curve and rising area (ABCR) and (ii) the test for not proper ROC curves (TNRC). We found our methodology more comprehensive, because it detects both bimodal and multimodal distributions and different variances can be considered on both samples. Another advantage of our method is that we can analyze graphically the behavior of different kinds of differentially expressed genes. Conclusion: Our results indicate that the arrow plot represents a new flexible and useful tool for the analysis of gene expression profiles from microarrays.

Py-GC/MS(FID) assessed behavior of polysaccharides during kraft delignification of Eucalyptus globulus heartwood and sapwood

Eucalyptus globulus heartwood, sapwood and their delignified samples by kraft pulping at 130, 150 and 170 degrees C along time were characterized in respect to total carbohydrates by Py-GC/MS(FID). No significant differences between heartwood and sapwood were found in relation to pyrolysis products and composition. The main wood carbohydrate derived pyrolysis compounds were levoglucosan (25.1%), hydroxyacetaldehyde (12.5%), 2-oxo-propanal (10.3%) and acetic acid (8.7%). Levoglucosan decreased during the early stages of delignification and increased during the bulk and residual phases. Acetic acid decreased hydroxyacetaldehyde and 2-oxo-propanal increased, and 2-furaldehyde and hydroxypropanone remained almost constant during delignification. The C/L ratio was 3.2 in wood and remained rather constant in the first pulping periods until a loss of 15-25% in carbohydrate and 60% in lignin. Afterwards it increased sharply until 44 that correspond to the removal of 25-35% of carbohydrates and 95% of lignin. The pulping reactive selectivity to lignin vs. polysaccharides was the same for sapwood and heartwood. (C) 2013 Elsevier B.V. All rights reserved.

The adjusted effect of maternal body mass index, energy and macronutrient intakes during pregnancy, and gestational weight gain on body composition of full-term neonates

Objective - To evaluate the effect of prepregnancy body mass index (BMI), energy and macronutrient intakes during pregnancy, and gestational weight gain (GWG) on the body composition of full-term appropriate-for-gestational age neonates. Study Design - This is a cross-sectional study of a systematically recruited convenience sample of mother-infant pairs. Food intake during pregnancy was assessed by food frequency questionnaire and its nutritional value by the Food Processor Plus (ESHA Research Inc, Salem, OR). Neonatal body composition was assessed both by anthropometry and air displacement plethysmography. Explanatory models for neonatal body composition were tested by multiple linear regression analysis. Results - A total of 100 mother-infant pairs were included. Prepregnancy overweight was positively associated with offspring weight, weight/length, BMI, and fat-free mass in the whole sample; in males, it was also positively associated with midarm circumference, ponderal index, and fat mass. Higher energy intake from carbohydrate was positively associated with midarm circumference and weight/length in the whole sample. Higher GWG was positively associated with weight, length, and midarm circumference in females. Conclusion - Positive adjusted associations were found between both prepregnancy BMI and energy intake from carbohydrate and offspring body size in the whole sample. Positive adjusted associations were also found between prepregnancy overweight and adiposity in males, and between GWG and body size in females.

Arrow plot and CA maps on microarray preprocessing methods

Microarray allow to monitoring simultaneously thousands of genes, where the abundance of the transcripts under a same experimental condition at the same time can be quantified. Among various available array technologies, double channel cDNA microarray experiments have arisen in numerous technical protocols associated to genomic studies, which is the focus of this work. Microarray experiments involve many steps and each one can affect the quality of raw data. Background correction and normalization are preprocessing techniques to clean and correct the raw data when undesirable fluctuations arise from technical factors. Several recent studies showed that there is no preprocessing strategy that outperforms others in all circumstances and thus it seems difficult to provide general recommendations. In this work, it is proposed to use exploratory techniques to visualize the effects of preprocessing methods on statistical analysis of cancer two-channel microarray data sets, where the cancer types (classes) are known. For selecting differential expressed genes the arrow plot was used and the graph of profiles resultant from the correspondence analysis for visualizing the results. It was used 6 background methods and 6 normalization methods, performing 36 pre-processing methods and it was analyzed in a published cDNA microarray database (Liver) available at http://genome-www5.stanford.edu/ which microarrays were already classified by cancer type. All statistical analyses were performed using the R statistical software.

Desacyl-ghrelin does not influence insulin sensitivity

Acyl-ghrelin has been reported to increase food intake and adiposity and it is the best studied of the orexigenic gastrointestinal hormones. On the other hand, desacyl-ghrelin – DAG (the unacylated form of the hormone) has been reported as a potential player on carbohydrate metabolism. However, the potential impact of DAG on glucose homeostasis remains uncertain. In this study we aim to assess the association between DAG and insulin sensitivity.

Aliphatic bio-oils from corks: a Py-Gc/MS study

Cork samples from Betula pendula, Quercus suber and Quercus cerris were submitted to Py-GC-MS/FID at temperatures between 550 degrees C and 900 degrees C and the pyrolysis-derived compounds (py-products) were identified and quantified. Corks were compared with wood samples. Py-products include suberin, lignin and carbohydrates derivatives. Suberin py-products are dominated by unsaturated aliphatics. Corks pyrolysis yield and composition were dramatically influenced by temperature in contrast to wood that showed stable results across temperatures. At 850-900 degrees C the peaks area of cork pyrograms were approximately two times higher than at 550 degrees C, for which yield was about half of the woods, and cork py-products were dominated by suberin-derived short chain aliphatics, namely 1-alkenes, while at 550 degrees C composition was dominated by lignin derivatives. Lignin and carbohydrate derived products decreased dramatically over 750 degrees C while the opposite was observed for unsaturated aliphatics. Cork materials show a high potential as feedstock for production of aliphatic-rich pyrolytic biofuels or as a source of olefins. (C) 2014 Elsevier B.V. All rights reserved.