Identification of an urinary metabolite profile associated with osteoarthritis

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 (approximately 40 out of the approximately 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

ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data

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

Profiles of metabolites and gene expression in rats with chemically induced hepatic necrosis

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

Metabolomics in the context of systems biology: Bridging Traditional Chinese Medicine and molecular pharmacology

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