The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Urine Metabolomics Analysis for Biomarker Discovery and Detection of Jaundice Syndrome in Patients With Liver Disease
Open accessAbstract
Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms.
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Cites
Metabolite profiles and the risk of developing diabetes
Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., Lewis, G. D., Fox, C. S., Jacques, P. F., Fernandez, C., et al. (2011) · Nature Medicine · doi:10.1038/nm.2307 · Cited by 3,143
Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression
Sreekumar, A., Poisson, L. M., Rajendiran, T. M., Khan, A. P., Cao, Q., Yu, J., Laxman, B., Mehra, R., Lonigro, R. J., Li, Y., et al. (2009) · Nature · doi:10.1038/nature07762 · Cited by 2,234
Nicholson, J. K., & Lindon, J. C. (2008) · Nature · doi:10.1038/4551054a · Cited by 1,886
Case 46-1963: Ascites, Jaundice and Hepatomegaly
Stoeckle, J. D., & Scully, R. E. (1963) · New England Journal of Medicine · doi:10.1056/nejm196307112690210 · Cited by 1,502
Human metabolic phenotype diversity and its association with diet and blood pressure
Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K. S., Chan, Q., Ebbels, T., De Iorio, M., Brown, I. J., Veselkov, K. A., et al. (2008) · Nature · doi:10.1038/nature06882 · Cited by 1,054
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017
Pharmaco-metabonomic phenotyping and personalized drug treatment
Andrew Clayton, T., Lindon, J. C., Cloarec, O., Antti, H., Charuel, C., Hanton, G., Provost, J.-P., Le Net, J.-L., Baker, D., Walley, R. J., et al. (2006) · Nature · doi:10.1038/nature04648 · Cited by 824
Klipp, E., Wierling, C., & Liebermeister, W. (1994) · Cited by 744
Catchpole, G. S., Beckmann, M., Enot, D. P., Mondhe, M., Zywicki, B., Taylor, J., Hardy, N., Smith, A., King, R. D., Kell, D. B., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503955102 · Cited by 392
High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues
Beckonert, O., Coen, M., Keun, H. C., Wang, Y., Ebbels, T. M. D., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2010) · Nature Protocols · doi:10.1038/nprot.2010.45 · Cited by 381
Cited by
Thévenot, E. A., Roux, A., Xu, Y., Ezan, E., & Junot, C. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00354 · Cited by 1,735
Mass spectrometric based approaches in urine metabolomics and biomarker discovery
Khamis, M. M., Adamko, D. J., & El‐Aneed, A. (2017) · Mass Spectrometry Reviews · doi:10.1002/mas.21455 · Cited by 342
Recent advances in natural products from plants for treatment of liver diseases
Zhang, A., Sun, H., & Wang, X. (2013) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2012.12.062 · Cited by 276
Self-citationSun, H., Zhang, A.-H., Song, Q., Fang, H., Liu, X.-Y., Su, J., Yang, L., Yu, M.-D., & Wang, X.-J. (2018) · RSC Advances · doi:10.1039/c8ra06553e · Cited by 89
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Metadata
- Authors
- Wang, X., Zhang, A., Han, Y., Wang, P., Sun, H., Song, G., Dong, T., Yuan, Y., Yuan, X., Zhang, M., Xie, N., Zhang, H., Dong, H., & Dong, W.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2012-08
- Volume
- 11
- Issue
- 8
- Pages
- 370-380
- ISSN
- 1535-9476 (ISSN-L)
- OpenAlex
- W1969082909
- Cited by
- 258
- References
- 32
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Wang, X., Zhang, A., Han, Y., Wang, P., Sun, H., Song, G., Dong, T., Yuan, Y., Yuan, X., Zhang, M., Xie, N., Zhang, H., Dong, H., & Dong, W. (2012). Urine Metabolomics Analysis for Biomarker Discovery and Detection of Jaundice Syndrome in Patients With Liver Disease. Molecular & Cellular Proteomics. https://doi.org/10.1074/mcp.m111.016006
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