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Ginkgo biloba extract has been advocated for the improvement of blood circulation in circulatory disorders. This study investigated the effect of the Gingko biloba extract EGb 761 on skin blood flow in healthy volunteers and accompanying changes in urinary metabolites. Twenty-seven healthy middle-aged subjects participated in a randomized, double-blind, placebo-controlled, crossover study. Subjects received 240 mg/d EGb 761 or placebo for periods of 3 weeks. Skin blood flow was measured on the forefoot using laser Doppler flowmetry and changes in urinary metabolites were identified by a combination of nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis (MVDA). These measurements were performed on 24-h urine samples collected at the end of the intervention periods. Following EGb 761 treatment, overall mean skin blood flow was significantly reduced as compared with placebo. Remarkably, the change of skin blood flow after EGb 761 intervention was proportionally related to blood flow after placebo treatment: subjects showed either an increased, decreased or unaltered skin blood flow. NMR/MDVA analyses showed that urinary metabolic patterns differed depending on the change in baseline blood flow after treatment with EGb 761. The present findings substantiate that EGb 761 has a multi-directional modulating action on blood flow in healthy subjects and support findings of a vasoregulatory role of this extract. Moreover, the results indicate that metabolic fingerprinting provides a powerful means to identify biochemical markers that are associated with functional changes.

This study investigated whether integrated analysis of transcriptomics and metabolomics data increased the sensitivity of detection and provided new insight in the mechanisms of hepatotoxicity. Metabolite levels in plasma or urine were analyzed in relation to changes in hepatic gene expression in rats that received bromobenzene to induce acute hepatic centrilobular necrosis. Bromobenzene-induced lesions were only observed after treatment with the highest of 3 dose levels. Multivariate statistical analysis showed that metabolite profiles of blood plasma were largely different from controls when the rats were treated with bromobenzene, also at doses that did not elicit histopathological changes. Changes in levels of genes and metabolites were related to the degree of necrosis, providing putative novel markers of hepatotoxicity. Levels of endogenous metabolites like alanine, lactate, tyrosine and dimethylglycine differed in plasma from treated and control rats. The metabolite profiles of urine were found to be reflective of the exposure levels. This integrated analysis of hepatic transcriptomics and plasma metabolomics was able to more sensitively detect changes related to hepatotoxicity and discover novel markers. The relation between gene expression and metabolite levels was explored and additional insight in the role of various biological pathways in bromobenzene-induced hepatic necrosis was obtained, including the involvement of apoptosis and changes in glycolysis and amino acid metabolism. The complete Table 2 is available as a supplemental file online at http://taylorandfrancis.metapress.com/openurlasp?genre=journal&issn=0192-6233. To access the file, click on the issue link for 33(4), then select this article. A download option appears at the bottom of this abstract. In order to access the full article online, you must either have an individual subscription or a member subscription accessed through www.toxpath.org. Copyright © by the Society of Toxicologic Pathology.

Motivation: Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. Results: We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors. © The Author 2005. Published by Oxford University Press. All rights reserved.

Metabolic fingerprints are novel measurement tools to evaluate the biochemical status of a living organism by using 1H NMR and multivariate data analysis (MVDA). In this way, a quick evaluation of changes in health or diseased state can be made, reflected in alterations of metabolic patterns. Normally, metabolic finger-printing is based on in vivo studies. These studies often represent a labor-intensive and expensive manner of investigation. In vitro studies are not hampered by these disadvantages, thus constituting an interesting alternative. In this research, results are presented of a pilot experiment in which metabolic fingerprinting was combined with an in vitro model. For this purpose, differentiated Caco-2 cells were exposed to inulin and its fermentative metabolites, both dissolved in culture medium. Cells were incubated for 0 or 48 h. Cell fractions were analyzed by NMR, then subsequently with MVDA. Differences in treatment provided detectable variations in the time of metabolic patterns of cell contents. Results indicated that glucose metabolism linked to glutamate was of major importance in the effects of inulin and its metabolites on Caco-2 cells under the conditions of our study. Metabolic fingerprinting in combination with an in vitro model appears to be a feasible technique with which to visualize metabolic patterns of cell contents and provides an efficient place for the generation of hypotheses about the metabolic pathways involved. In vitro metabolic fingerprinting may be of great benefit in the future for a better understanding of the relationship between nutrition and health.

The introduction of the concept of systems biology, enabling the study of living systems from a holistic perspective based on the profiling of a multitude of biochemical components, opens up a unique and novel opportunity to reinvestigate natural products. In the study of their bioactivity, the necessary reductionistic approach on single active components has been successful in the discovery of new medicines, but at the same time the synergetic effects of components were lost. Systems biology, and especially metabolomics, is the ultimate phenotyping. It opens up the possibility of studying the effect of complex mixtures, such as those used in Traditional Chinese Medicine, in complex biological systems; abridging it with molecular pharmacology. This approach is considered to have the potential to revolutionize natural product research and to advance the development of scientific based herbal medicine. Copyright © 2005 John Wiley & Sons, Ltd.

Objective: Osteoarthritis (OA) is one of the most common diseases among the elderly. The main characteristic is the progressive destruction of articular cartilage. We lack quantitative and sensitive biomarkers for OA to detect changes in the joints in an early stage of the disease. In this study, we investigated whether a urinary metabolite profile could be found that could serve as a diagnostic biomarker for OA in humans. We also compared the profile we obtained previously in the guinea pig spontaneous OA model. Methods: Urine samples of 92 participants (47 non-OA controls and 45 individuals with radiographic OA of the knees or hips) were selected from the Johnston County Osteoarthritis Project (North Carolina, USA). Participants ranged in age from 60 to 84 years. Samples were measured by 1H nuclear magnetic resonance spectroscopy (NMR) with subsequent principal component discriminant analysis and partial least squares regression analysis. Results: Differences were observed between urine NMR spectra of OA cases and controls (P < 0.001 for both male and female subjects). A metabolite profile could be determined which was strongly associated with OA. This profile largely resembled the profile previously identified for guinea pigs with OA (∼40 out of the ∼125 signals of the human profile were present in the guinea pig profile as well). A correlation was found between the metabolite profile and radiographic OA severity (R2 = 0.82 (male); R2 = 0.93 (female)). Conclusion: This study showed that a urine metabolite profile may serve as a novel discriminating biomarker of OA. © 2005 OsteoArthritis Research Society International. Published by Elsevier Ltd. All rights reserved. Chemicals / CAS: Biological Markers

Osteoarthritis (OA), one of the most common diseases among the elderly, is characterized by the progressive destruction of joint tissues. Its etiology is largely unclear and no effective disease-modifying treatment is currently available. Metabolic fingerprinting provides a novel tool for the identification of biomarkers. A metabolic fingerprint consists of a typical combination of metabolites in a biological fluid and is identified by a combination of 1H NMR spectroscopy and multivariate data analysis (MVDA). The current feasibility study was aimed at identifying a metabolic fingerprint for OA and applying this in a nutritional intervention study. Urine samples were collected from osteoarthritic male Hartley guinea pigs (n = 44) at 10 and 12 mo of age, treated from 4 mo onward with variable vitamin C doses (2.5-3, 30 and 150 mg/d) and from healthy male Strain 13 guinea pigs (n = 8) at 12 mo of age, treated with 30 mg vitamin C/d. NMR measurements were performed on all urine samples. Subsequently, MVDA was carried out on the data obtained using NMR. An NMR fingerprint was identified that reflected the osteoarthritic changes in guinea pigs. The metabolites that comprised the fingerprint indicate that energy and purine metabolism are of major importance in OA. Metabolic fingerprinting also allowed detection of differences in OA-specific metabolites induced by different dietary vitamin C intakes. This study demonstrates the feasibility of metabolic fingerprinting to identify disease-specific profiles of urinary metabolites. NMR fingerprinting is a promising means of identifying new disease markers and of gaining fresh insights into the pathophysiology of disease.