Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study

Background. Chronic systemic low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy. Methods. To address this limitation, we used an integrative omics approach to characterize modulation of inflammation in overweight men during an intervention with the non-steroidal anti-inflammatory drug diclofenac. Measured parameters included 80 plasma proteins, >300 plasma metabolites (lipids, free fatty acids, oxylipids and polar compounds) and an array of peripheral blood mononuclear cells (PBMC) gene expression products. These measures were submitted to multivariate and correlation analysis and were used for construction of biological response networks. Results. A panel of genes, proteins and metabolites, including PGE2 and TNF-alpha, were identified that describe a diclofenac-response network (68 genes in PBMC, 1 plasma protein and 4 plasma metabolites). Novel candidate markers of inflammatory modulation included PBMC expression of annexin A1 and caspase 8, and the arachidonic acid metabolite 5,6-DHET. Conclusion. In this study the integrated analysis of a wide range of parameters allowed the development of a network of markers responding to inflammatory modulation, thereby providing insight into the complex process of inflammation and ways to assess changes in inflammatory status associated with obesity. Trial registration. The study is registered as NCT00221052 in clinicaltrials.gov database. © 2010 van Erk et al; licensee BioMed Central Ltd

Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500 ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6 h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation

Assessing the performance of statistical validation tools for megavariate metabolomics data

Statistical model validation tools such as cross-validation, jack-knifing model parameters and permutation tests are meant to obtain an objective assessment of the performance and stability of a statistical model. However, little is known about the performance of these tools for megavariate data sets, having, for instance, a number of variables larger than 10 times the number of subjects. The performance is assessed for megavariate metabolomics data, but the conclusions also carry over to proteomics, transcriptomics and many other research areas. Partial least squares discriminant analyses models were built for several LC-MS lipidomic training data sets of various numbers of lean and obese subjects. The training data sets were compared on their modelling performance and their predictability using a 10-fold cross-validation, a permutation test, and test data sets. A wide range of cross-validation error rates was found (from 7.5% to 16.3% for the largest trainings set and from 0% to 60% for the smallest training set) and the error rate increased when the number of subjects decreased. The test error rates varied from 5% to 50%. The smaller the number of subjects compared to the number of variables, the less the outcome of validation tools such as cross-validation, jack-knifing model parameters and permutation tests can be trusted. The result depends crucially on the specific sample of subjects that is used for modelling. The validation tools cannot be used as warning mechanism for problems due to sample size or to representativity of the samplin

Kinetic energy density and agglomerate abrasion rate during blending of agglomerates into powders

Problems related to the blending of a cohesive powder with a free flowing bulk powder are frequently encountered in the pharmaceutical industry. The cohesive powder often forms lumps or agglomerates which are not dispersed during the mixing process and are therefore detrimental to blend uniformity. Achieving sufficient blend uniformity requires that the blending conditions are able to break up agglomerates, which is often an abrasion process. This study was based on the assumption that the abrasion rate of agglomerates determines the required blending time. It is shown that the kinetic energy density of the moving powder bed is a relevant parameter which correlates with the abrasion rate of agglomerates. However, aspects related to the strength of agglomerates should also be considered. For this reason the Stokes abrasion number (St(Abr)) has been defined. This tAbr, parameter describes the ratio between the kinetic energy density of the moving powder bed and the work of fracture of the agglomerate. The St(Abr) number is shown to predict the abrasion potential of agglomerates in the dry-mixing process. It appeared possible to include effects of filler particle size and impeller rotational rate into this concept. A clear relationship between abrasion rate of agglomerates and the value of St(Abr) was demonstrated. (C) 2011 Elsevier B.V. All rights reserved

Metabolomic biomarkers for personalised glucose lowering drugs treatment in type 2 diabetes

We aimed to identify metabolites to predict patients’ response to glucose lowering treatment during the first 5 years after detection of type 2 diabetes. Metabolites were measured by GC–MS in baseline samples from 346 screen-detected type 2 diabetes patients in the ADDITION-NL study. The response to treatment with metformin and/or sulphonylurea (SU) was analysed to identify metabolites predictive of 5 year HbA1c change by multiple regression analysis. Baseline glucose and 1,5 anhydro-glucitol were associated with HbA1c decrease in all medication groups. In patients on SU no other metabolite was associated with HbA1c decrease. A larger set of metabolites was associated with HbA1c change in the metformin and the combination therapy (metformin + SU) groups. These metabolites included metabolites related to liver metabolism, such as 2-hydroxybutanoic acid, 3-hydroxybutanoic acid, 2-hydroxypiperidine and 4-oxoproline). Metabolites involved in oxidative stress and insulin resistance were higher when the HbA1c decrease was larger in the metformin/sulphonylurea group. The associations between baseline metabolites and responsiveness to medication are in line with its mode of action. If these results could be replicated in other populations, the most promising predictive candidates might be tested to assess whether they could enhance personalised treatment

Metabolomic biomarkers for personalised glucose lowering drugs treatment in type 2 diabetes

We aimed to identify metabolites to predict patients’ response to glucose lowering treatment during the first 5 years after detection of type 2 diabetes. Metabolites were measured by GC–MS in baseline samples from 346 screen-detected type 2 diabetes patients in the ADDITION-NL study. The response to treatment with metformin and/or sulphonylurea (SU) was analysed to identify metabolites predictive of 5 year HbA1c change by multiple regression analysis. Baseline glucose and 1,5 anhydro-glucitol were associated with HbA1c decrease in all medication groups. In patients on SU no other metabolite was associated with HbA1c decrease. A larger set of metabolites was associated with HbA1c change in the metformin and the combination therapy (metformin + SU) groups. These metabolites included metabolites related to liver metabolism, such as 2-hydroxybutanoic acid, 3-hydroxybutanoic acid, 2-hydroxypiperidine and 4-oxoproline). Metabolites involved in oxidative stress and insulin resistance were higher when the HbA1c decrease was larger in the metformin/sulphonylurea group. The associations between baseline metabolites and responsiveness to medication are in line with its mode of action. If these results could be replicated in other populations, the most promising predictive candidates might be tested to assess whether they could enhance personalised treatment

The effect of Korean pine nut oil on in vitro CCK release, on appetite sensations and on gut hormones in post-menopausal overweight women

Abstract Appetite suppressants may be one strategy in the fight against obesity. This study evaluated whether Korean pine nut free fatty acids (FFA) and triglycerides (TG) work as an appetite suppressant. Korean pine nut FFA were evaluated in STC-1 cell culture for their ability to increase cholecystokinin (CCK-8) secretion vs. several other dietary fatty acids from Italian stone pine nut fatty acids, oleic acid, linoleic acid, alpha-linolenic acid, and capric acid used as a control. At 50 μM concentration, Korean pine nut FFA produced the greatest amount of CCK-8 release (493 pg/ml) relative to the other fatty acids and control (46 pg/ml). A randomized, placebo-controlled, double-blind cross-over trial including 18 overweight post-menopausal women was performed. Subjects received capsules with 3 g Korean pine (Pinus koraiensis) nut FFA, 3 g pine nut TG or 3 g placebo (olive oil) in combination with a light breakfast. At 0, 30, 60, 90, 120, 180 and 240 minutes the gut hormones cholecystokinin (CCK-8), glucagon like peptide-1 (GLP-1), peptide YY (PYY) and ghrelin, and appetite sensations were measured. A wash-out period of one week separated each intervention day. CCK-8 was higher 30 min after pine nut FFA and 60 min after pine nut TG when compared to placebo (p This study suggests that Korean pine nut may work as an appetite suppressant through an increasing effect on satiety hormones and a reduced prospective food intake.</p