Assessing the metabolic effects of prednisolone in healthy volunteers using urine metabolic profiling.

BACKGROUND: Glucocorticoids, such as prednisolone, are widely used anti-inflammatory drugs, but therapy is hampered by a broad range of metabolic side effects including skeletal muscle wasting and insulin resistance. Therefore, development of improved synthetic glucocorticoids that display similar efficacy as prednisolone but reduced side effects is an active research area. For efficient development of such new drugs, in vivo biomarkers, which can predict glucocorticoid metabolic side effects in an early stage, are needed. In this study, we aim to provide the first description of the metabolic perturbations induced by acute and therapeutic treatments with prednisolone in humans using urine metabolomics, and to derive potential biomarkers for prednisolone-induced metabolic effects. METHODS: A randomized, double blind, placebo-controlled trial consisting of two protocols was conducted in healthy men. In protocol 1, volunteers received placebo (n = 11) or prednisolone (7.5 mg (n = 11), 15 mg (n = 13) or 30 mg (n = 12)) orally once daily for 15 days. In protocol 2, volunteers (n = 6) received placebo at day 0 and 75 mg prednisolone at day 1. We collected 24 h urine and serum samples at baseline (day 0), after a single dose (day 1) and after prolonged treatment (day 15) and obtained mass-spectrometry-based urine and serum metabolic profiles. RESULTS: At day 1, high-dose prednisolone treatment increased levels of 13 and 10 proteinogenic amino acids in urine and serum respectively, as well as levels of 3-methylhistidine, providing evidence for an early manifestation of glucocorticoid-induced muscle wasting. Prednisolone treatment also strongly increased urinary carnitine derivatives at day 1 but not at day 15, which might reflect adaptive mechanisms under prolonged treatment. Finally, urinary levels of proteinogenic amino acids at day 1 and of N-methylnicotinamide at day 15 significantly correlated with the homeostatic model assessment of insulin resistance and might represent biomarkers for prednisolone-induced insulin resistance. CONCLUSION: This study provides evidence that urinary metabolomics represents a noninvasive way of monitoring the effect of glucocorticoids on muscle protein catabolism after a single dose and can derive new biomarkers of glucocorticoid-induced insulin resistance. It might, therefore, help the development of improved synthetic glucocorticoids. TRIAL REGISTRATION: ClinicalTrials.gov NCT00971724.

Lipidomics reveals multiple pathway effects of a multi-components preparation on lipid biochemistry in ApoE*3Leiden.CETP mice.

BACKGROUND: Causes and consequences of the complex changes in lipids occurring in the metabolic syndrome are only partly understood. Several interconnected processes are deteriorating, which implies that multi-target approaches might be more successful than strategies based on a limited number of surrogate markers. Preparations from Chinese Medicine (CM) systems have been handed down with documented clinical features similar as metabolic syndrome, which might help developing new intervention for metabolic syndrome. The progress in systems biology and specific animal models created possibilities to assess the effects of such preparations. Here we report the plasma and liver lipidomics results of the intervention effects of a preparation SUB885C in apolipoprotein E3 Leiden cholesteryl ester transfer protein (ApoE*3Leiden.CETP) mice. SUB885C was developed according to the principles of CM for treatment of metabolic syndrome. The cannabinoid receptor type 1 blocker rimonabant was included as a general control for the evaluation of weight and metabolic responses. METHODOLOGY/PRINCIPAL FINDINGS: ApoE*3Leiden.CETP mice with mild hypercholesterolemia were divided into SUB885C-, rimonabant- and non-treated control groups. SUB885C caused no weight loss, but significantly reduced plasma cholesterol (-49%, p<0.001), CETP levels (-31%, p<0.001), CETP activity (-74%, p<0.001) and increased HDL-C (39%, p<0.05). It influenced lipidomics classes of cholesterol esters and triglycerides the most. Rimonabant induced a weight loss (-9%, p<0.05), but only a moderate improvement of lipid profiles. In vitro, SUB885C extract caused adipolysis stimulation and adipogenesis inhibition in 3T3-L1 cells. CONCLUSIONS: SUB885C, a multi-components preparation, is able to produce anti-atherogenic changes in lipids of the ApoE*3Leiden.CETP mice, which are comparable to those obtained with compounds belonging to known drugs (e.g. rimonabant, atorvastatin, niacin). This study successfully illustrated the power of lipidomics in unraveling intervention effects and to help finding new targets or ingredients for lifestyle-related metabolic abnormality.

Lipidomic approach to evaluate rosuvastatin and atorvastatin at various dosages: investigating differential effects among statins.

OBJECTIVE: Lipid profiling (lipidomics) may be useful in revealing detailed information with regard to the effects on lipid metabolism, the cardiovascular risk and to differentiate between therapies. The aims of the present study were to: (1) analyze in depth the lipid changes induced by rosuvastatin and atorvastatin at different dosages; (2) compare differences between the two drugs with respect to the lipid profile change; (3) relate the findings with meaningful pathological mechanisms of coronary artery disease. RESEARCH DESIGN AND METHODS: Liquid chromatography-mass spectrometry was applied to obtain the metabolite profiles of plasma samples taken from a prospectively defined subset (n=80) of participants in the RADAR study where a randomly assigned treatment with rosuvastatin or atorvastatin in increasing dosages was administered during an 18-week period. RESULTS: A number of sphingomyelins (SPMs) and phosphatidylcholines (PCs) correlate with the different effects of the two statins on the LDL-C/HDL-C ratio. Rosuvastatin increased the plasma concentration of PCs after 6 and 18 weeks, while atorvastatin reduced the plasma concentrations of PCs at both timepoints and dosages (p<0.01 for between-treatment comparison). Both atorvastatin and rosuvastatin lowered plasma SPMs concentrations, but atorvastatin demonstrated a more pronounced effect with the highest dose (p=0.03). Rosuvastatin resulted in a significantly more effective lowering of the [SPMs/(SPMs + PCs)] ratio than atorvastatin at any dose/timepoint (p<0.05), a ratio reported to be of clinical importance in coronary artery disease. CONCLUSIONS: The lipidomic technique has revealed that statins are different with regards to the effect on detailed lipid profile. The observed difference in lipids may be connected with different clinical outcomes as suggested by the [SPMs/(SPMs + PCs)] ratio.

Quantitative profiling of bile acids in biofluids and tissues based on accurate mass high resolution LC-FT-MS: compound class targeting in a metabolomics workflow.

We report a sensitive, generic method for quantitative profiling of bile acids and other endogenous metabolites in small quantities of various biological fluids and tissues. The method is based on a straightforward sample preparation, separation by reversed-phase high performance liquid-chromatography mass spectrometry (HPLC-MS) and electrospray ionisation in the negative ionisation mode (ESI-). Detection is performed in full scan using the linear ion trap Fourier transform mass spectrometer (LTQ-FTMS) generating data for many (endogenous) metabolites, not only bile acids. A validation of the method in urine, plasma and liver was performed for 17 bile acids including their taurine, sulfate and glycine conjugates. The method is linear in the 0.01-1 microM range. The accuracy in human plasma ranges from 74 to 113%, in human urine 77 to 104% and in mouse liver 79 to 140%. The precision ranges from 2 to 20% for pooled samples even in studies with large number of samples (n>250). The method was successfully applied to a multi-compartmental APOE*3-Leiden mouse study, the main goal of which was to analyze the effect of increasing dietary cholesterol concentrations on hepatic cholesterol homeostasis and bile acid synthesis. Serum and liver samples from different treatment groups were profiled with the new method. Statistically significant differences between the diet groups were observed regarding total as well as individual bile acid concentrations.

Similarities and differences in lipidomics profiles among healthy monozygotic twin pairs.

Differences in genetic background and/or environmental exposure among individuals are expected to give rise to differences in measurable characteristics, or phenotypes. Consequently, genetic resemblance and similarities in environment should manifest as similarities in phenotypes. The metabolome reflects many of the system properties, and is therefore an important part of the phenotype. Nevertheless, it has not yet been examined to what extent individuals sharing part of their genome and/or environment indeed have similar metabolomes. Here we present the results of hierarchical clustering of blood plasma lipid profile data obtained by liquid chromatography-mass spectrometry from 23 healthy, 18-year-old twin pairs, of which 21 pairs were monozygotic, and 8 of their siblings. For 13 monozygotic twin pairs, within-pair similarities in relative concentrations of the detected lipids were indeed larger than the similarities with any other study participant. We demonstrate such high coclustering to be unexpected on basis of chance. The similarities between dizygotic twins and between nontwin siblings, as well as between nonfamilial participants, were less pronounced. In a number of twin pairs, within-pair dissimilarity of lipid profiles positively correlated with increased blood plasma concentrations of C-reactive protein in one twin. In conclusion, this study demonstrates that in healthy individuals, the individual genetic background contributes to the blood plasma lipid profile. Furthermore, lipid profiling may prove useful in monitoring health status, for example, in the context of personalized medicine.

Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation.

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.

Characterization of anti-inflammatory compounds using transcriptomics, proteomics, and metabolomics in combination with multivariate data analysis.

The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (beta2-agonists). In addition to their primary pharmacological target, beta2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized beta2-agonist, gives rise to an almost identical expression pattern as the beta2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study.